From d3a68801a1ffd8bafed006f9c380f628535b09ab Mon Sep 17 00:00:00 2001
From: mdecker6 <nobody@makingwithcode.org>
Date: Thu, 23 Oct 2025 09:36:34 -0400
Subject: [PATCH] This planning document will also form the introduction of
 your argument.

DO PEOPLE WHO EXIBIT RISKY BEHAVIORS (SMOKING, GAMBLING, DRINKING) ORDER THEIR STEAK IN THE SAME RISKY MANNER AS THEIR BEAHVIORS (RARE). IS THERE A RELATIONSHIP BETWEEN THE TWO.

THE CENTRAL QUESTION WOULD ASK DO PEOPLE WHO HAVE AN AFFINITY FOR RISKY BEHAVIORS, SPECIFICALLY (SMOKING, DRINKING, GAMBLING) ALSO HAVE A RISKY BEHAVIOR WHEN IT COMES TO EATING THEIR FOOD. SPECIFICALLY IN THIS STUDY, IT IS ABOUT ORDERING STEAK.
I AM INTERESTED IN THIS QUESTION BECASUE IT IS INTERESTING TO SEE IF FOOD RISK BEHAVIORS ARE ASSOCIATED WITH ACTIVITY RISK BEHAVIORS. I ALSO AM INTERESTED TO SEE IN THIS STUDY HOW INCOME LEVELS EFFECT THESE DECISIONS AS WELL AS EDUCATION LEVELS, WHICH IS ALSO IN THE STUDY.

*List 2-4 specific research questions. Each should be answerable
using your data set.*

1. DO MORE MALES THAN FEMALES ORDER RARE STEAK?
2. DO PEOPLE WITH A BACHELORS DEGREE GAMBLE MORE THAN PEOPLE WITH A GRADUATE DEGREE?
3. DO PEOPLE WHO ORDER RARE STEAK ALSO DRINK?
4. DO ALL FEMALES WHO ORDER RARE STEAK ALSO SMOKE?
5. DO ALL MALES WHO ORDER RARE STEAK ALSO DRINK
6. Are males more likely to order their steak rare than females

538 WEBSITE

*Give the title of your data set and provide a link to your data.*

TITLE OF STUDY IS: STEAK-RISK-SURVEY

WEBSITES:
https://github.com/fivethirtyeight/data/tree/master/steak-survey
https://fivethirtyeight.com/features/how-americans-like-their-steak/
https://docs.google.com/spreadsheets/d/125buas5LwUrDXEgQh0DPniGkA6-fWeupBgaXgUqXoOI/edit?gid=1253281910#gid=1253281910

*Describe the source of the data set--not just where you downloaded it, but
the person or organization who gathered the data. Explain why you trust them.*

May 16, 2014, at 5:08 PM

How Americans Like Their Steak
By Walt Hickey
Filed under FiveThirtyAte
DATA LAB
https://github.com/fivethirtyeight/data/tree/master/steak-survey
https://fivethirtyeight.com/features/how-americans-like-their-steak/

*Describe the nature of the data in the dataset, including the number of rows
and some of the columns which will be important to you.*

number of rows= 433
COLUMNS= 8
COLUMNS WHIC HARE IMPORTANT ARE:
1.gender
2.how do you like steak
3.income level
4. degree
5. gamble
6. drink
7. smoke

*For each research question, explain what you will do with the data set
to answer the question, and how you will present your answer (e.g. a chart or a table).*

1. DO MORE MALES THAN FEMALES ORDER RARE STEAK? table
2. DO PEOPLE WITH A BACHELORS DEGREE GAMBLE MORE THAN PEOPLE WITH A GRADUATE DEGREE? chart
3. DO PEOPLE WHO ORDER RARE STEAK ALSO DRINK?
4. DO ALL FEMALES WHO ORDER RARE STEAK ALSO SMOKE?
5. DO ALL MALES WHO ORDER RARE STEAK ALSO DRINK
6. Are males more likely to order their steak rare than females
---
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 .ipynb_checkpoints/Untitled1-checkpoint.ipynb |    6 +
 .ipynb_checkpoints/argument-checkpoint.ipynb  | 1278 +++++++++
 .../lab_pokemon-checkpoint.ipynb              | 2308 +++++++++++++++++
 .ipynb_checkpoints/proposal-checkpoint.md     |   88 +
 .ipynb_checkpoints/steak-checkpoint.csv       |  433 ++++
 .ipynb_checkpoints/untitled-checkpoint.txt    |    0
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 proposal.md                                   |   69 +-
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 rare.jpg                                      |  Bin 0 -> 6766 bytes
 risk.jpg                                      |  Bin 0 -> 7731 bytes
 steak.csv                                     |  433 ++++
 untitled.txt                                  |    0
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 create mode 100644 .ipynb_checkpoints/argument-checkpoint.ipynb
 create mode 100644 .ipynb_checkpoints/lab_pokemon-checkpoint.ipynb
 create mode 100644 .ipynb_checkpoints/proposal-checkpoint.md
 create mode 100644 .ipynb_checkpoints/steak-checkpoint.csv
 create mode 100644 .ipynb_checkpoints/untitled-checkpoint.txt
 create mode 100644 538.jpg
 create mode 100644 Untitled.ipynb
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 create mode 100644 lab_pokemon.ipynb
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 create mode 100644 steak.csv
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diff --git a/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/.ipynb_checkpoints/Untitled-checkpoint.ipynb
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--- /dev/null
+++ b/.ipynb_checkpoints/Untitled-checkpoint.ipynb
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/.ipynb_checkpoints/Untitled1-checkpoint.ipynb b/.ipynb_checkpoints/Untitled1-checkpoint.ipynb
new file mode 100644
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--- /dev/null
+++ b/.ipynb_checkpoints/Untitled1-checkpoint.ipynb
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+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/.ipynb_checkpoints/argument-checkpoint.ipynb b/.ipynb_checkpoints/argument-checkpoint.ipynb
new file mode 100644
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--- /dev/null
+++ b/.ipynb_checkpoints/argument-checkpoint.ipynb
@@ -0,0 +1,1278 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "42901bdc-1637-4bad-b7fd-d901aaa7488b",
+   "metadata": {},
+   "source": [
+    "## **OVERARCHING QUESTION:** *DO PEOPLE WHO EXIBIT RISKY BEHAVIORS (SMOKING, GAMBLING, DRINKING) ORDER THEIR STEAK IN THE SAME RISKY MANNER AS THEIR BEAHVIORS (RARE). IS THERE A RELATIONSHIP BETWEEN THE TWO.*\n",
+    "![alt text](risk.jpg)\n",
+    "\n",
+    "**Why this question**\n",
+    "\n",
+    "THE CENTRAL QUESTION WOULD ASK DO PEOPLE (males and females) WHO HAVE AN AFFINITY FOR RISKY BEHAVIORS, SPECIFICALLY (SMOKING, DRINKING, GAMBLING) ALSO HAVE A RISKY BEHAVIOR WHEN IT COMES TO EATING THEIR FOOD. SPECIFICALLY IN THIS STUDY, IT IS ABOUT ORDERING STEAK.\n",
+    "I AM INTERESTED IN THIS QUESTION BECASUE IT IS INTERESTING TO SEE IF FOOD RISK BEHAVIORS ARE ASSOCIATED WITH ACTIVITY RISK BEHAVIORS. I ALSO AM INTERESTED TO SEE IN THIS STUDY HOW INCOME LEVELS EFFECT THESE DECISIONS AS WELL AS EDUCATION LEVELS, WHICH IS ALSO IN THE STUDY.\n",
+    "\n",
+    "![alt text](r.jpg)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "technical-evans",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Include any import statements you will need\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set_theme()\n",
+    "steak = pd.read_csv(\"steak.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "overhead-sigma",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>smoke</th>\n",
+       "      <th>drink</th>\n",
+       "      <th>gamble</th>\n",
+       "      <th>cooked</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Household Income</th>\n",
+       "      <th>Education</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Medium</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>&gt; 60</td>\n",
+       "      <td>$50,000 - $99,999</td>\n",
+       "      <td>Bachelor degree</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Medium</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>&gt; 60</td>\n",
+       "      <td>$50,000 - $99,999</td>\n",
+       "      <td>Graduate degree</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Medium</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>18-29</td>\n",
+       "      <td>$50,000 - $99,999</td>\n",
+       "      <td>Bachelor degree</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Medium</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>18-29</td>\n",
+       "      <td>$50,000 - $99,999</td>\n",
+       "      <td>Bachelor degree</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Medium</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>18-29</td>\n",
+       "      <td>$50,000 - $99,999</td>\n",
+       "      <td>Bachelor degree</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>427</th>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Well</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>45-60</td>\n",
+       "      <td>$150,000+</td>\n",
+       "      <td>High school degree</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>428</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Well</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>45-60</td>\n",
+       "      <td>$50,000 - $99,999</td>\n",
+       "      <td>Bachelor degree</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>429</th>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Well</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>30-44</td>\n",
+       "      <td>$0 - $24,999</td>\n",
+       "      <td>Some college or Associate degree</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>430</th>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Well</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>30-44</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Some college or Associate degree</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>431</th>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Well</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>&gt; 60</td>\n",
+       "      <td>$100,000 - $149,999</td>\n",
+       "      <td>Bachelor degree</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>432 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    smoke drink gamble  cooked  Gender    Age     Household Income  \\\n",
+       "0     Yes   Yes    Yes  Medium    Male   > 60    $50,000 - $99,999   \n",
+       "1      No   Yes     No  Medium    Male   > 60    $50,000 - $99,999   \n",
+       "2     Yes   Yes    Yes  Medium    Male  18-29    $50,000 - $99,999   \n",
+       "3      No   Yes    Yes  Medium    Male  18-29    $50,000 - $99,999   \n",
+       "4      No   Yes    Yes  Medium    Male  18-29    $50,000 - $99,999   \n",
+       "..    ...   ...    ...     ...     ...    ...                  ...   \n",
+       "427    No   Yes    Yes    Well  Female  45-60            $150,000+   \n",
+       "428   NaN    No     No    Well    Male  45-60    $50,000 - $99,999   \n",
+       "429    No   Yes     No    Well    Male  30-44         $0 - $24,999   \n",
+       "430    No    No    Yes    Well  Female  30-44                  NaN   \n",
+       "431    No   Yes    Yes    Well  Female   > 60  $100,000 - $149,999   \n",
+       "\n",
+       "                            Education  \n",
+       "0                     Bachelor degree  \n",
+       "1                     Graduate degree  \n",
+       "2                     Bachelor degree  \n",
+       "3                     Bachelor degree  \n",
+       "4                     Bachelor degree  \n",
+       "..                                ...  \n",
+       "427                High school degree  \n",
+       "428                   Bachelor degree  \n",
+       "429  Some college or Associate degree  \n",
+       "430  Some college or Associate degree  \n",
+       "431                   Bachelor degree  \n",
+       "\n",
+       "[432 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "steak"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "8d7c325c-ef3f-4072-a51c-1e87325a3c96",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Gender\n",
+       "Male      212\n",
+       "Female    200\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "steak.Gender.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "2899053c-9446-4772-8cc2-113a238b967a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Gender', ylabel='Count'>"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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6dSrbt28nIyOD7OxsJk2aROfOncu9btq0aUyePJkJEyZQUlJCr169GD9+vHezliRJ+1WnEkLr1q157733vrJNbm7uQfXVqFEj7rjjDu64447qKE2SJB3mAr+mSJIkqToYiiRJkjAUSZIkAYYiSZIkwFAkSZIEGIokSZIAQ5EkSRJgKJIkSQIMRZIkSYChSJIkCTAUSZIkAYYiSZIkwFAkSZIEGIokSZIAQ5EkSRJgKJIkSQIMRZIkSYChSJIkCTAUSZIkAYYiSZIkwFAkSZIEGIokSZIAQ5EkSRJgKJIkSQIMRZIkSYChSJIkCTAUSZIkAYYiSZIkANJSXcDnffzxx+Tm5rJ69WrWrl1LZmYmCxYsSO4vKipi1qxZLFu2jI8++oh69erRuXNnxowZQ8eOHZPtNmzYwGmnnVah/y5dujB37txaeS+SJClY6lQoWrt2LcuWLaNLly7E43ESiUS5/Z988glPPfUU3//+9xk9ejR79+7l0Ucf5fzzz2f+/Pl06NChXPsbbriBbt26JZ83bNiwVt6HJEkKnjoVivr378/pp58OwLhx41izZk25/a1bt2bx4sVkZGQkt3Xv3p3+/fvz29/+lltvvbVc+2OOOYasrKwar1uSJAVfnQpF4fBXL3Fq0KBBhW0NGzakbdu2bN68uabKkiRJ/wUCv9C6sLAwuf7oiyZOnMgJJ5xATk4O48ePZ/v27bVfoCRJCoQ6NVNUGXfeeSehUIgLL7wwua1evXpceOGF9OrVi2g0yurVq3nooYdYs2YN8+bNIz09vUrHTEur3iwZiZT2FwqFCIVC1dq3aknoP48hPIdBlBx7IRyHQeQYDLyycVf2mZgKlQ5Fl1xyCT/5yU/IycnZ7/4//elPPPDAAzz++OOVLu5A5s+fz9y5c5kyZQotW7ZMbm/evDkTJ05MPu/atSvHHXccI0eOZPHixQwaNKjSxwyHQzRpUjMLtiORMGlpkRrpW7UjLeL5C6rIvy/fR8KOwyBzDAZXWRiKRjMO0LLmVDoUrVy5kmHDhn3p/q1bt7Jq1arKdn9Ay5YtY8KECVx99dUMHTr0gO379u1LgwYNeOedd6oUiuLxBIWFxZV+/f5EImGi0QxisTglJbFq7Vu1JFT6y7gkFoPEgZur7onF48lHx2EAOQYDLxYrHYOFhbuTP1eXaDTjoGagqnT57KummD/++OMa+wr8W2+9xfXXX893v/tdrr/++ho5xlcpKanek1UmkUhUuA2BgiE5XZ/AcxhQyfPmOQwkx2DwlZ230gmCmvmcPZBDCkXPPPMMzzzzTPL5gw8+uN+bIe7cuZP33nuPPn36VL3CL/jggw8YOXIk3bt3Z9KkSQf9uqVLl1JcXEynTp2qvSZJkhR8hxSKdu/ezbZt25LPd+3atd+v0Tdo0IALLriAa6655pCK2b17N8uWLQNg48aNFBUVkZ+fD5SuC0okElxxxRXUr1+fSy+9tNx9jI488kiOPfZYAKZMmUIoFCIrK4toNMrbb7/Nww8/zMknn5y8D5IkSdLnHVIouuiii7jooouA0hst3nLLLfv95zQq69NPP61wOazsedmC7U2bNgHwox/9qFy7rl278sQTTwDQoUMHfve73zF37lz27NlDixYt+MEPfsCoUaNISwv8F+4kSVINqHRCeOmll6qzDqD0jtXvvffeV7Y50H6AYcOGfeUicEmSpC+q8rRJUVERn3zyCYWFhftd3HbqqadW9RCSJEk1rtKhaOvWrfzyl79k0aJFxGIVv76aSCQIhUL89a9/rVKBkiRJtaHSoWjChAksXbqU4cOH8+1vf5toNFqddUmSJNWqSoeiFStWcOmll/LTn/60OuuRJElKiUr/AyNHHHEErVq1qs5aJEmSUqbSoeicc87hxRdfrM5aJEmSUqbSl8/OPPNMVq1axRVXXMH5559Py5YtieznH+I76aSTqlSgJElSbah0KCq7iSPAq6++WmG/3z6TJElBUulQNHny5OqsQ5IkKaUqHYqGDh1anXVIkiSlVKUXWkuSJB1OKj1T9LOf/eyAbUKhEHfccUdlDyFJklRrKh2KXn/99Qrb4vE4W7ZsIRaL0bRpUzIyMqpUnCRJUm2pdCh66aWX9rt93759PPXUU8yePZtHH3200oVJkiTVpmpfU5Sens7FF19Mz549+cUvflHd3UuSJNWIGltoffzxx7Nq1aqa6l6SJKla1VgoevXVV11TJEmSAqPSa4ruu+++/W7fuXMnq1at4t133+XHP/5xpQuTJEmqTdUeiho3bkybNm2YNGkS5513XqULkyRJqk2VDkV/+9vfqrMOSZKklPKO1pIkSVRhpqjMypUrefnll/nkk08AOProo+nXrx9du3atcnGSJEm1pdKh6LPPPuPGG2/kxRdfJJFIEI1GASgsLGTWrFkMGDCA3/zmN6Snp1dbsZIkSTWl0pfP7r//fhYvXsxll13GK6+8wsqVK1m5ciUrVqzg8ssvZ9GiRdx///3VWaskSVKNqXQoeu655xg6dCg//elP+frXv57c/rWvfY3/+Z//4bvf/S7PPvtstRQpSZJU0yodirZs2ULnzp2/dH/nzp3ZsmVLZbuXJEmqVZUORS1btmTlypVfun/VqlW0bNmyst1LkiTVqkqHou9+97s8//zzTJgwgYKCAmKxGPF4nIKCAm677Tby8/MZOnRoddYqSZJUYyr97bOrrrqK9evXM3fuXObNm0c4XJqv4vE4iUSCoUOHctVVV1VboZIkSTWp0qEoEokwZcoUfvSjH7F8+XI2btwIQKtWrejTpw/HH3/8Iff58ccfk5uby+rVq1m7di2ZmZksWLCgQrt58+Yxc+ZMPvnkE9q3b8+YMWP4zne+U67Nzp07mTx5Mi+++CL79u2jd+/ejB8/nubNm1fuDUuSpMPaIYWivXv3cvvtt3PccccxfPhwAI4//vgKAejxxx/n97//Pbfccssh3ado7dq1LFu2jC5duiRnnL5o4cKF3HrrrVx11VV0796dvLw8rr32Wp588kmysrKS7UaPHs0HH3zAxIkTqV+/PtOmTWPEiBHMnz+ftLQq37NSkiQdZg4pHTz11FM888wz5OXlfWW7fv36ceedd/LNb36Tiy666KD779+/P6effjoA48aNY82aNRXa3HvvvZx99tmMHj0agO7du/P+++9z//33M2PGDADefPNNXnnlFXJzc+nVqxcA7du3Z9CgQSxatIhBgwYddE2SJOm/wyEttH7++ec544wzaNOmzVe2a9u2LWeddRYLFy48tGLCX13O+vXr+eijjxg4cGC57YMGDeK1117js88+A2D58uVEo1F69uyZbJOZmckJJ5zA8uXLD6kmSZL03+GQZoref/99hgwZclBts7OzWbp0aaWK+jIFBQVA6azP53Xo0IF9+/axfv16OnToQEFBAe3btycUCpVrl5mZmeyjKtLSqvff0Y1ESvsLhUIValZAhP7zGMJzGETJsRfCcRhEjsHAKxt3ZZ+JqXBIoWjfvn0HvUYoPT09OXNTXXbs2AGQ/HfWypQ9L9tfWFhIo0aNKry+cePG+70kdyjC4RBNmjSsUh9fJhIJk5YWqZG+VTvSIp6/oIr8e6Y6EnYcBpljMLjKwlA0mpGyGg4pFDVv3py1a9ceVNu1a9celt/0iscTFBYWV2ufkUiYaDSDWCxOSUmsWvtWLQmV/jIuicWg4vcDFACxeDz56DgMIMdg4MVipWOwsHB38ufqEo1mHNQM1CGFoh49evCHP/yBkSNH8rWvfe1L23366af84Q9/4MwzzzyU7g+ocePGQOnX7Zs1a5bcXlhYWG5/NBpl06ZNFV6/Y8eOZJuqKCmp3pNVJpFI7Pcbd6r7ktP1CTyHAZU8b57DQHIMBl/ZeSudIKiZz9kDOaQLdyNGjGDv3r1ceumlrF69er9tVq9ezY9+9CP27t3LlVdeWS1FlsnMzASosC6ooKCA9PT05ALwzMxM1q1bV2FgrFu3LtmHJEnS5x3STFGbNm2YNm0aN9xwAxdccAFt2rThm9/8Jg0bNmTXrl2sXbuWv//97xxxxBHcfffdtG3btlqLbdOmDe3atSM/Pz/51X2AvLw8cnJyqFevHgB9+vThgQce4LXXXqNHjx5AaSB69913qz2oSZKkw8Mh38WwX79+PPvss8yYMYOXX36ZF198MbmvefPmDBs2jBEjRhzwa/v7s3v3bpYtWwbAxo0bKSoqIj8/H4CuXbvStGlTrrvuOm666Sbatm1Lt27dyMvL4+2332bOnDnJfrKzs+nVqxc333wzY8eOpX79+kydOpWOHTtyxhlnHHJdkiTp8FepWzu3bt2aSZMmAVBUVMSuXbto2LAhRx55ZJWK+fTTT7n++uvLbSt7/vjjj9OtWzcGDx7M7t27mTFjBo888gjt27fnvvvuIzs7u9zrpk2bxuTJk5kwYQIlJSX06tWL8ePHezdrSZK0X6GEK9IOSSwWZ+vWXdXaZ1pamCZNGjLtt2+wcUtRtfat2hEKhUhLi1BSEnORZ0BlfbMZPxx4Ivf87v/YsHlnqsvRIXIMBl+rZkcy+qJvsW3brmpfaN20acOD+vZZ6u6QJEmSVIcYiiRJkjAUSZIkAYYiSZIkwFAkSZIEGIokSZIAQ5EkSRJgKJIkSQIMRZIkSYChSJIkCTAUSZIkAYYiSZIkwFAkSZIEGIokSZIAQ5EkSRJgKJIkSQIMRZIkSYChSJIkCTAUSZIkAYYiSZIkwFAkSZIEGIokSZIAQ5EkSRJgKJIkSQIMRZIkSYChSJIkCTAUSZIkAYYiSZIkANJSXcChGj58OCtXrtzvvrvvvpuzzz77S9vk5eXRoUOHmi5RkiQFUOBC0W233UZRUVG5bbNnz2bRokXk5OQkt51yyimMHTu2XLvWrVvXSo2SJCl4AheKjj322ArbbrzxRnr27EnTpk2T26LRKFlZWbVYmSRJCrLAryn6v//7PzZs2MCQIUNSXYokSQqwwIeiBQsW0KBBA0477bRy21euXElWVhadOnXi4osvZtWqVSmqUJIkBUHgLp99XklJCc8//zz9+/enQYMGye2nnnoq5557Lu3atWPz5s3k5uZy2WWX8cQTT5CdnV3l46alVW+WjERK+wuFQoRCoWrtW7Uk9J/HEJ7DIEqOvRCOwyByDAZe2bgr+0xMhUCHohUrVrB161YGDx5cbvuoUaPKPe/Xrx+DBw/mgQceYMaMGVU6ZjgcokmThlXq48tEImHS0iI10rdqR1rE8xdUkXA4+eg4DC7HYHCVhaFoNCNlNQQ6FC1YsICjjjqKXr16fWW7Bg0a0LdvX1544YUqHzMeT1BYWFzlfj4vEgkTjWYQi8UpKYlVa9+qJaHSX8YlsRgkUl2MKiMWjycfHYcB5BgMvFisdAwWFu5O/lxdotGMg5qBCmwo2rNnDy+++CLnnHMO6enptXrskpLqPVllEokEiYSjOYiS0/UJPIcBlTxvnsNAcgwGX9l5K50gqJnP2QMJ7ELrl156ieLi4oP61llxcTEvv/wynTp1qoXKJElSEAV2pui5557j6KOP5lvf+la57X/+85+ZOXMmAwYMoFWrVmzevJlZs2axZcsW7rnnnhRVK0mS6rpAhqIdO3bwxz/+kUsvvbTCt0SaNWvGvn37mDp1Ktu3bycjI4Ps7GwmTZpE586dU1SxJEmq6wIZiho3bsyaNWv2u++YY44hNze3liuSJElBF9g1RZIkSdXJUCRJkoShSJIkCTAUSZIkAYYiSZIkwFAkSZIEGIokSZIAQ5EkSRJgKJIkSQIMRZIkSYChSJIkCTAUSZIkAYYiSZIkwFAkSZIEGIokSZIAQ5EkSRJgKJIkSQIMRZIkSYChSJIkCTAUSZIkAYYiSZIkwFAkSZIEGIokSZIAQ5EkSRJgKJIkSQIMRZIkSYChSJIkCTAUSZIkAYYiSZIkIICh6Omnn6Zjx44V/rvrrrvKtZs3bx5nnnkmnTp14pxzzmHp0qUpqliSJAVBWqoLqKyZM2fSqFGj5PMWLVokf164cCG33norV111Fd27dycvL49rr72WJ598kqysrBRUK0mS6rrAhqKTTjqJpk2b7nffvffey9lnn83o0aMB6N69O++//z73338/M2bMqMUqJUlSUATu8tmBrF+/no8++oiBAweW2z5o0CBee+01PvvssxRVJkmS6rLAzhQNHjyYbdu2cfTRR3Peeedx5ZVXEolEKCgoAKB9+/bl2nfo0IF9+/axfv16OnToUKVjp6VVb5aMREr7C4VChEKhau1btST0n8cQnsMgSo69EI7DIHIMBl7ZuCv7TEyFwIWiZs2acd1119GlSxdCoRAvvfQS06ZN45///CcTJkxgx44dAESj0XKvK3tetr+ywuEQTZo0rFIfXyYSCZOWFqmRvlU70iKev6CKhMPJR8dhcDkGg6ssDEWjGSmrIXChqHfv3vTu3Tv5vFevXtSvX5/Zs2dz1VVX1fjx4/EEhYXF1dpnJBImGs0gFotTUhKr1r5VS0Klv4xLYjFIpLoYVUYsHk8+Og4DyDEYeLFY6RgsLNyd/Lm6RKMZBzUDFbhQtD8DBw7k0Ucf5a9//SuNGzcGYOfOnTRr1izZprCwECC5vypKSqr3ZJVJJBIkEo7mIEpO1yfwHAZU8rx5DgPJMRh8ZeetdIKgZj5nD+SwW2idmZkJkFxbVKagoID09HTatGmTirIkSVIdd1iEory8PCKRCCeeeCJt2rShXbt25OfnV2iTk5NDvXr1UlSlJEmqywJ3+eyKK66gW7dudOzYEYAlS5Ywd+5cLrnkkuTlsuuuu46bbrqJtm3b0q1bN/Ly8nj77beZM2dOKkuXJEl1WOBCUfv27Zk/fz6bNm0iHo/Trl07br75ZoYPH55sM3jwYHbv3s2MGTN45JFHaN++Pffddx/Z2dkprFySJNVlgQtF48ePP6h2w4YNY9iwYTVcjSRJOlwcFmuKJEmSqspQJEmShKFIkiQJMBRJkiQBhiJJkiTAUCRJkgQYiiRJkgBDkSRJEmAokiRJAgxFkiRJgKFIkiQJMBRJkiQBhiJJkiTAUCRJkgQYiiRJkgBDkSRJEmAokiRJAgxFkiRJgKFIkiQJMBRJkiQBhiJJkiTAUCRJkgQYiiRJkgBDkSRJEmAokiRJAgxFkiRJgKFIkiQJMBRJkiQBkJbqAg7V888/z7PPPss777xDYWEhxxxzDMOHD+f73/8+oVAIgOHDh7Ny5coKr83Ly6NDhw61XbIkSQqAwIWixx57jFatWjFu3DiaNGnCq6++yq233sqmTZu49tprk+1OOeUUxo4dW+61rVu3ru1yJUlSQAQuFD344IM0bdo0+TwnJ4ft27cza9Ysrr76asLh0iuC0WiUrKysFFUpSZKCJnBrij4fiMqccMIJFBUVUVxcnIKKJEnS4SBwoWh/3njjDVq0aMGRRx6Z3LZy5UqysrLo1KkTF198MatWrUphhZIkqa4L3OWzL/rzn/9MXl5eufVDp556Kueeey7t2rVj8+bN5Obmctlll/HEE0+QnZ1d5WOmpVVvloxESvsLhULJxeIKmNB/HkN4DoMoOfZCOA6DyDEYeGXjruwzMRUCHYo2bdrEmDFj6NatG5dcckly+6hRo8q169evH4MHD+aBBx5gxowZVTpmOByiSZOGVerjy0QiYdLSIjXSt2pHWsTzF1SRf69HjIQdh0HmGAyusjAUjWakrIbAhqLCwkJGjBjBUUcdxfTp05MLrPenQYMG9O3blxdeeKHKx43HExQWVu/apUgkTDSaQSwWp6QkVq19q5aESn8Zl8RikEh1MaqMWDyefHQcBpBjMPBisdIxWFi4O/lzdYlGMw5qBiqQoWjPnj2MHDmSnTt38tRTT9GoUaNaPX5JSfWerDKJRIJEwtEcRMnp+gSew4BKnjfPYSA5BoOv7LyVThDUzOfsgQQuFJWUlDB69GgKCgp48sknadGixQFfU1xczMsvv0ynTp1qoUJJkhREgQtFkyZNYunSpYwbN46ioiLeeuut5L4TTzyRt99+m5kzZzJgwABatWrF5s2bmTVrFlu2bOGee+5JXeGSJKlOC1woWrFiBQBTpkypsG/JkiU0a9aMffv2MXXqVLZv305GRgbZ2dlMmjSJzp0713a5kiQpIAIXil566aUDtsnNza2FSiRJ0uHksLh5oyRJUlUZiiRJkjAUSZIkAYYiSZIkwFAkSZIEGIokSZIAQ5EkSRJgKJIkSQIMRZIkSYChSJIkCTAUSZIkAYYiSZIkwFAkSZIEGIokSZIAQ5EkSRJgKJIkSQIMRZIkSYChSJIkCTAUSZIkAYYiSZIkwFAkSZIEGIokSZIAQ5EkSRJgKJIkSQIMRZIkSYChSJIkCTAUSZIkAYYiSZIkwFAkSZIEHOah6MMPP+Syyy4jKyuLnj178utf/5rPPvss1WVJkqQ6KC3VBdSUHTt2cOmll9KuXTumT5/OP//5T6ZMmcKePXuYMGFCqsuTJEl1zGEbin7/+9+za9cu7rvvPo466igAYrEYkyZNYuTIkbRo0SK1BUqSpDrlsL18tnz5cnJycpKBCGDgwIHE43FWrFiRusIkSVKddNjOFBUUFPD973+/3LZoNEqzZs0oKCiodL/hcIimTRtWtbxyQqHSxyu/24lYPFGtfUs6OOlppX9HvOLckx2HUgpEwqUfho0bZ5Co5iEY/nffB3LYhqLCwkKi0WiF7Y0bN2bHjh2V7jcUChGJHNwf7qE6skG9GulX0sFzHEqpFQ6n7iLWYXv5TJIk6VActqEoGo2yc+fOCtt37NhB48aNU1CRJEmqyw7bUJSZmVlh7dDOnTvZsmULmZmZKapKkiTVVYdtKOrTpw+vvvoqhYWFyW35+fmEw2F69uyZwsokSVJdFEokqnuNd92wY8cOzj77bNq3b8/IkSOTN28cMmSIN2+UJEkVHLahCEr/mY9f/OIXvPnmmzRs2JBzzz2XMWPGUK+e3y6RJEnlHdahSJIk6WAdtmuKJEmSDoWhSJIkCUORJEkSYCiSJEkCDEWSJEmAoUiSJAkwFEkATJ8+nY4dO9K7d2/i8XiF/RdccAEdO3Zk3LhxB93nhg0b6NixI/n5+dVZqhQ4ZePri/8NHjw41aUldezYkdzc3FSXoRRLS3UBUl2Rnp7Otm3bWLVqFd26dUtu37hxI2+99RYNGjRIYXVSsB1xxBHMnj27wjapLjEUSf+Wnp5OTk4OCxcuLBeKFi5cyHHHHUc47MSqVFnhcJisrKxUlyF9JX/LS58zePBgXnjhBfbt25fctmDBggrT/B9++CFjxoyhb9++dOnShUGDBvHoo4/u99LbFz399NMMGTKETp060bt3b6ZOnUosFqv29yIFxcsvv8ywYcPo3Lkz3bt357bbbqO4uDi5//XXX6djx4788Y9/5Prrryc7O5t+/frx3HPPAfD444/Tr18/unbtyi233MJnn32WfO3mzZv52c9+xmmnnUbnzp0544wzuPvuu8u1qWxdOvw4UyR9zne+8x1uueUWVqxYQb9+/fjggw947733uP/++8nLy0u227x5M+3bt2fIkCE0bNiQv/71r0yfPp3i4mKuvfbaL+1/1qxZ3HnnnVx66aWMGzeODz/8MBmKbrrpptp4i1LKlJSUlHseiUR44YUXGDNmDN/73ve47rrr2LJlC7/5zW8oLCxk6tSp5dpPnDiRoUOHct555zF37lx++tOf8re//Y21a9cyadIk1q9fz5QpU2jTpg1XXXUVANu2beOoo47iZz/7GdFolI8++ojp06ezZcsWJk+e/KW15ufnH3RdOnwYiqTPycjIoH///ixcuJB+/fqxYMECsrOzadOmTbl2OTk55OTkAJBIJPjWt77Fnj17mDNnzpeGoqKiIu69916uvPJKbrjhBgB69uxJeno6U6ZM4YorrqBJkyY1+walFCkuLuakk04qt+1Xv/oV9957L4MGDeL2229Pbm/WrBk//vGPufrqqznuuOOS288666zk+OrcuTOLFy9m4cKFLF68mPT0dABWrlxJfn5+MhR17NiRsWPHJvs45ZRTyMjIYNy4cUyYMIGMjIwKtSYSCX79618fdF06fBiKpC8YPHgwN954I3v27CEvL4/hw4dXaLN3714efvhhnnvuOf7xj3+Uu9y2a9cuGjZsWOE1b775JsXFxZx11lnl/sbco0cP9uzZw9q1a+natWvNvCkpxY444gjmzJlTbls8Hmfjxo3cfPPN5cZE165dCYfDrFmzplz46NmzZ/LnRo0a0bRpU7797W8nAxFAu3bteP3115PPE4kEs2fPZu7cuWzYsIG9e/cm961fv55vfvObFWpdt27dIdWlw4ehSPqCXr16kZ6ezj333MOGDRsYOHBghTZ33nkn8+bN45prruHkk0+mUaNGLFmyhAcffJC9e/fuNxRt27YNgKFDh+73uP/4xz+q941IdUg4HKZTp07ltr3xxhsAXHPNNft9zRfHRKNGjco9r1evHtFotNy29PT0cuuFZs+eza9+9SuuvPJKunXrRjQa5S9/+Qs///nPywWkzysbqwdblw4fhiLpC9LT0znjjDN47LHHyMnJ4etf/3qFNvn5+Zx//vn8+Mc/Tm5btmzZV/bbuHFjAO677z5atmxZYX/r1q2rWLkULEcddRQAEyZMoHPnzhX2N2/evMrHyM/Pp3///tx4443JbR9++GHK61LdZCiS9mPYsGF8+umnnHfeefvdv3fv3nJT9rFYjIULF35ln9nZ2WRkZLBp0yYGDBhQrfVKQZSZmUnLli1Zv349P/zhD2vkGHv27Ck3VoHkt9ZSWZfqJkORtB+dO3fmgQce+NL9PXr0YN68eRx77LE0adKE3/72twf8im80GmXUqFHceeedbNq0ia5duxKJRFi/fj1Llixh+vTp+130KR2uQqEQ48aN46abbqK4uJh+/fqRkZHBJ598wrJlyxgzZgzt27ev0jF69OjB448/zpw5c2jXrh3PPvssH3/8ccrrUt1kKJIq4dZbb+W2227jF7/4BRkZGQwdOpQBAwYwfvz4r3zd5ZdfTosWLZg1axZz5swhLS2Ntm3b0q9fvwp/m5X+GwwcOJBoNMpDDz2UnMFp1aoVvXv33u+l60N1zTXXsG3bNu69914AzjzzTMaPH5/8dlqq6lLdFEokEolUFyFJkpRq3tFakiQJQ5EkSRJgKJIkSQIMRZIkSYChSJIkCTAUSZIkAYYiSZIkwFAkSYesf//+jBs3LtVlSKpm3tFaUp22fv16Zs2axYoVK9i0aRNQemfhbt26cf7553P88cenuEJJhwtDkaQ6a+nSpYwZM4ZIJMKQIUM4/vjjCYfDFBQUsGjRIn73u9+xZMkSWrVqlepSJR0GDEWS6qS///3v3HDDDRx99NE89thjNG/evNz+m266id/+9reEw8FeBVBSUkI8HqdevXqpLkX6rxfs3yaSDlszZ86kuLiYyZMnVwhEAGlpaVxyySV84xvfSG778MMPGTVqFF27dqVTp05873vfY8mSJeVe9/TTT9OxY0feeOMNJk+eTPfu3cnKyuKaa65h69at5domEgkeeOAB+vTpQ5cuXRg+fDhr167db72FhYXcfvvt9O3bl5NPPpkBAwbwyCOPEI/Hk202bNhAx44dyc3N5bHHHuP000+nU6dOfPjhh1X5o5JUTZwpklQnLV26lGOOOYYuXbocVPu1a9dy4YUX0qJFC0aMGEGDBg14/vnnueaaa5g+fToDBgwo1/6Xv/wl0WiUa6+9lo0bNzJ79mx+/vOfM23atGSbe+65hwcffJC+ffvSt29f3nnnHS6//HL27dtXrq/du3dz8cUX889//pMLLriAb3zjG7z55pvcfffdbNmyhVtuuaVc+6effpq9e/dy3nnnUa9ePRo3bly5PyRJ1cpQJKnOKSoqYvPmzZx++ukV9hUWFlJSUpJ83qBBA4444ghuv/12vvGNbzB//vzkpaiLLrqICy+8kLvuuqtCKDrqqKN49NFHCYVCAMTjcZ544gl27txJo0aN2Lp1KzNnzqRfv3489NBDyXZTp07loYceKtfXrFmzWL9+Pc888wzt2rUD4IILLqB58+bk5uZy+eWXl5vR2rRpE4sXL6Zp06ZV/8OSVG28fCapzikqKgJKA88XDR8+nJycnOR/Tz75JNu3b+dPf/oTAwcOpKioiK1bt7J161a2bdtGr169+Oijj/jnP/9Zrp/zzjsvGXQAvv3tbxOLxdi4cSMAr776Kvv27ePiiy8u1+7SSy+tUFN+fj7f+ta3iEajyWNv3bqVHj16EIvFWLVqVbn2Z5xxhoFIqoOcKZJU5zRs2BCA4uLiCvt+/vOfs2vXLv71r3/xP//zP0DpouxEIsE999zDPffcs98+P/30U1q0aJF8fvTRR5fbH41GgdKZKIBPPvkEIDnzU6Zp06YVLnd9/PHHvPfee+Tk5Oz32F9cq9S6dev9tpOUWoYiSXVOo0aNaNas2X4XNZetMdqwYUNyW9li5ssvv5zevXvvt8+2bduWe/5l31pLJBKHXG88Hqdnz55ceeWV+93/xWB1xBFHHPIxJNU8Q5GkOqlfv37MmzePt99+m86dO39l2zZt2gCQnp5Ojx49quX4ZTNJH330UbJ/KJ312bFjR7m2bdu2pbi4uNqOLSk1XFMkqU668sorycjI4Oabb+Zf//pXhf2fn9H52te+RteuXXnqqafYvHlzhbZfvHx1MHr06EF6ejpz5swpd6zZs2dXaDtw4EDefPNN/vjHP1bY98WF4ZLqLmeKJNVJ7dq146677uLGG2/krLPOSt7ROpFIsGHDBhYsWEA4HKZly5YA3HbbbVx00UUMGTKE8847jzZt2vCvf/2Lt956i02bNvHss88e0vGbNm3K5ZdfzsMPP8zIkSPp27cv7777LsuXL6dJkybl2l5xxRW89NJLXHXVVQwdOpSTTjqJ3bt38/777/PCCy+wZMkSF1ZLAWAoklRnnX766Tz33HM8+uijrFixgvnz5xMKhTj66KPp27cvF154YfLfPjv22GOZP38+9913H8888wzbt2+nadOmnHjiiVxzzTWVOv7o0aOpV68ev//973n99dfp3Lkzjz76KCNHjizXLiMjgyeeeIKHH36Y/Px8/t//+38ceeSRtGvXjuuuu45GjRpV+c9CUs0LJSqzqlCSJOkw45oiSZIkDEWSJEmAoUiSJAkwFEmSJAGGIkmSJMBQJEmSBBiKJEmSAEORJEkSYCiSJEkCDEWSJEmAoUiSJAkwFEmSJAGGIkmSJAD+P57COIPl6HJEAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.histplot(data=steak, x=\"Gender\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "continental-franklin",
+   "metadata": {},
+   "source": [
+    "**What is the data about and where is it from?**\n",
+    "\n",
+    "![alt text](538.jpg)\n",
+    "\n",
+    "This is a data set from the website 538. It was a data set collected by Walt Hickey. It is a steak risky survey. The data examines how people value and interpret risk in life, and does this have a correlation to how they order their steak. 550 people were surveyed. It seeks to interpret if people who do risky behaviors (smoke, gamble, drink) order their steak in the same manner (rare) and risk food borne illness."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "infinite-instrument",
+   "metadata": {},
+   "source": [
+    "# Methods and Results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "recognized-positive",
+   "metadata": {},
+   "source": [
+    "## First Research Question: *Do people (males and females) who drink also order their steak rare?*\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "graduate-palmer",
+   "metadata": {},
+   "source": [
+    "### Methods"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "endless-variation",
+   "metadata": {},
+   "source": [
+    "*Explain how you will approach this research question below. Consider the following:* \n",
+    "  - *Which aspects of the dataset will you use?* \n",
+    "  - *How will you reorganize/store the data?* \n",
+    "  - *What data science tools/functions will you use and why?*\n",
+    "\n",
+    "\n",
+    "**I will use the columns Drink/Cooked to answer this question.\n",
+    "I will...\n",
+    "\n",
+    "-get the value of people who drink\n",
+    "-get the value of people who order steak rare\n",
+    "-get the value of people who do both drinking and order their steak rare\n",
+    "\n",
+    "I will use pandas to handle my data (as data frames), seaborne to create visualizations\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "portuguese-japan",
+   "metadata": {},
+   "source": [
+    "# Results "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "68678a65-6dd4-4fff-bb4e-70492249e021",
+   "metadata": {},
+   "source": [
+    "### Number of people who Drink"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "negative-highlight",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "drink\n",
+       "Yes    339\n",
+       "No      93\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "steak.drink.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "victorian-burning",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='drink', ylabel='Count'>"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.histplot(data=steak, x=\"drink\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e0ec8c49-e490-4bac-927a-0f3c50c72c8d",
+   "metadata": {},
+   "source": [
+    "### Number of people who like their steak rare\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "46f2704d-6336-4b76-a7db-8cde388f7d55",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "cooked\n",
+       "Medium rare    166\n",
+       "Medium         132\n",
+       "Medium Well     75\n",
+       "Well            36\n",
+       "Rare            23\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "steak.cooked.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "45451f54-ebc8-4acc-aec7-64ecd9842709",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='cooked', ylabel='Count'>"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.histplot(data=steak, x=\"cooked\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ec996656-8880-4077-bc4f-01e7d600dece",
+   "metadata": {},
+   "source": [
+    "### Number of people who like their steak rare, and who drink"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "5d1555da-3c4f-4105-b7b7-83a1bfb3f833",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "drink  cooked     \n",
+      "No     Medium          23\n",
+      "       Medium Well     17\n",
+      "       Medium rare     38\n",
+      "       Rare             3\n",
+      "       Well            12\n",
+      "Yes    Medium         109\n",
+      "       Medium Well     58\n",
+      "       Medium rare    128\n",
+      "       Rare            20\n",
+      "       Well            24\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "counts = steak.groupby([\"drink\", 'cooked']).size()\n",
+    "print(counts)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "32ca9aa2-e1ad-4563-b2de-55326f187e1f",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='drink', ylabel='count'>"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(x='drink', hue='cooked', data=steak)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "a72f700e-4d9b-46d9-a216-69b754fd409f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of people who drink and order their steak rare: 20\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = steak.dropna(subset=['drink', 'cooked'])\n",
+    "drink_rare = df[(df['drink'].str.lower() == 'yes') & (df['cooked'].str.lower() == 'rare')]\n",
+    "count_drink_rare = len(drink_rare)\n",
+    "print(f\"Number of people who drink and order their steak rare: {count_drink_rare}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "collectible-puppy",
+   "metadata": {},
+   "source": [
+    "## Second Research Question: *Do people (males and females) who gamble also order their steak rare?*\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "demographic-future",
+   "metadata": {},
+   "source": [
+    "### Methods"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "incorporate-roller",
+   "metadata": {},
+   "source": [
+    "*Explain how you will approach this research question below. Consider the following:* \n",
+    "  - *Which aspects of the dataset will you use?* \n",
+    "  - *How will you reorganize/store the data?* \n",
+    "  - *What data science tools/functions will you use and why?* \n",
+    "\n",
+    "✏️ *Write your answer below:*\n",
+    "\n",
+    "**I will use the columns Gamble/Cooked to answer this question.\n",
+    "I will...\n",
+    "\n",
+    "-get the value of people who gamble\n",
+    "-get the value of people who order steak rare\n",
+    "-get the value of people who do both gamble and order their steak rare\n",
+    "\n",
+    "I will use pandas to handle my data (as data frames), seaborne to create visualizations\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "juvenile-creation",
+   "metadata": {},
+   "source": [
+    "# Results "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8d239cc7-d973-452d-a462-7317ab8fe5f2",
+   "metadata": {},
+   "source": [
+    "### Number of people who gamble\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "pursuant-surrey",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "gamble\n",
+       "Yes    216\n",
+       "No     213\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "steak.gamble.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "located-night",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='gamble', ylabel='Count'>"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.histplot(data=steak, x=\"gamble\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e213a7eb-6a17-4db7-bda6-200200421688",
+   "metadata": {},
+   "source": [
+    "### Number of people who like their steak rare\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "c520afe4-e348-4573-a6f3-9968cc8b594b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "cooked\n",
+       "Medium rare    166\n",
+       "Medium         132\n",
+       "Medium Well     75\n",
+       "Well            36\n",
+       "Rare            23\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "steak.cooked.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "b7fd5c7a-a8cb-4253-912b-d062f88caf25",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='cooked', ylabel='Count'>"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.histplot(data=steak, x=\"cooked\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "996d3496-8057-4c0f-b083-ead62e394fde",
+   "metadata": {},
+   "source": [
+    "### Number of people who like their steak rare, and who gamble"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "f75ee0b2-8ed2-40d2-9a6e-dd839663f364",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "gamble  cooked     \n",
+      "No      Medium         59\n",
+      "        Medium Well    40\n",
+      "        Medium rare    85\n",
+      "        Rare           11\n",
+      "        Well           18\n",
+      "Yes     Medium         73\n",
+      "        Medium Well    32\n",
+      "        Medium rare    81\n",
+      "        Rare           12\n",
+      "        Well           18\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "counts = df.groupby([\"gamble\", 'cooked']).size()\n",
+    "print(counts)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "8082a773-1acd-4da5-beef-e78b82e4bca7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='gamble', ylabel='count'>"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(x='gamble', hue='cooked', data=df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "506a2001-d2dc-4e66-b330-699d5194db2d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of people who gamble and order their steak rare: 12\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = df.dropna(subset=['gamble', 'cooked'])\n",
+    "gamble_rare = df[(df['gamble'].str.lower() == 'yes') & (df['cooked'].str.lower() == 'rare')]\n",
+    "count_gamble_rare = len(gamble_rare)\n",
+    "print(f\"Number of people who gamble and order their steak rare: {count_gamble_rare}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "87df3b25-3df9-42a8-884f-2a9c1a362bf4",
+   "metadata": {},
+   "source": [
+    "## Third Research Question: *Do people (males and females) who smoke also order their steak rare?*\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f20e80e3-f582-4422-ba20-5ff133730a79",
+   "metadata": {},
+   "source": [
+    "### Methods\n",
+    "*Explain how you will approach this research question below. Consider the following:* \n",
+    "  - *Which aspects of the dataset will you use?* \n",
+    "  - *How will you reorganize/store the data?* \n",
+    "  - *What data science tools/functions will you use and why?* \n",
+    "\n",
+    "✏️ *Write your answer below:*\n",
+    "\n",
+    "**I will use the columns Smoke/Cooked to answer this question.\n",
+    "I will...\n",
+    "\n",
+    "-get the value of people who smoke\n",
+    "-get the value of people who order steak rare\n",
+    "-get the value of people who do both smoke and order their steak rare\n",
+    "\n",
+    "I will use pandas to handle my data (as data frames), seaborne to create visualizations\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e3a6ce78-a232-440d-be07-d5263b922bd7",
+   "metadata": {},
+   "source": [
+    "# Results "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fdf13331-0905-49a9-9da6-97124c967248",
+   "metadata": {},
+   "source": [
+    "### Number of people who smoke\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "22f25f57-c47a-4b73-9fc6-12a5dcd85d2f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "smoke\n",
+       "No     356\n",
+       "Yes     72\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "steak.smoke.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "2377f2bc-3aa3-4d92-a128-c4cc9eaf7d35",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='smoke', ylabel='Count'>"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.histplot(data=steak, x=\"smoke\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "baefe767-40fb-4afe-ae12-2374ce33ece4",
+   "metadata": {},
+   "source": [
+    "### Number of people who like their steak rare\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "2e62dead-0ed2-422b-abdb-c0d359732689",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "cooked\n",
+       "Medium rare    166\n",
+       "Medium         132\n",
+       "Medium Well     75\n",
+       "Well            36\n",
+       "Rare            23\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "steak.cooked.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "09aec01a-465f-49e7-8b2f-f6e3c74720da",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='cooked', ylabel='Count'>"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.histplot(data=steak, x=\"cooked\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ee1b058c-71de-4141-b9e6-c1e66ebb6033",
+   "metadata": {},
+   "source": [
+    "### Number of people who like their steak rare, and who smoke"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "6abbe66a-565d-47a1-a6b0-b676c0362261",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "smoke  cooked     \n",
+      "No     Medium         111\n",
+      "       Medium Well     61\n",
+      "       Medium rare    136\n",
+      "       Rare            16\n",
+      "       Well            30\n",
+      "Yes    Medium          20\n",
+      "       Medium Well     11\n",
+      "       Medium rare     30\n",
+      "       Rare             6\n",
+      "       Well             5\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "counts = df.groupby([\"smoke\", 'cooked']).size()\n",
+    "print(counts)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "4751cff3-a23a-449d-b8f3-c52072d02768",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='smoke', ylabel='count'>"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(x='smoke', hue='cooked', data=df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "81274425-b884-4604-a2d4-7f2cc6b4a1c2",
+   "metadata": {},
+   "source": [
+    "### Number of people who like their steak rare, and who do all 3 risky behaviors(smoke, drink, gamble)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "8817c157-0528-47c7-8680-c886dd5499f4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of people who smoke, drink, and gamble who order their steak rare: 5\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = df.dropna(subset=['smoke', 'drink', 'gamble', 'cooked'])\n",
+    "smoke_drink_gamble_rare = df[\n",
+    "    (df['smoke'].str.lower() == 'yes') &\n",
+    "    (df['drink'].str.lower() == 'yes') &\n",
+    "    (df['gamble'].str.lower() == 'yes') &\n",
+    "    (df['cooked'].str.lower() == 'rare')\n",
+    "]\n",
+    "count_smoke_drink_gamble_rare = len(smoke_drink_gamble_rare)\n",
+    "print(f\"Number of people who smoke, drink, and gamble who order their steak rare: {count_smoke_drink_gamble_rare}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "beneficial-invasion",
+   "metadata": {},
+   "source": [
+    "## Summary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "about-raise",
+   "metadata": {},
+   "source": [
+    "\n",
+    "\n",
+    "**OVERARCHING QUESTION**\n",
+    "*DO PEOPLE WHO EXIBIT RISKY BEHAVIORS (SMOKING, GAMBLING, DRINKING) ORDER THEIR STEAK IN THE SAME RISKY MANNER AS THEIR BEAHVIORS (RARE). IS THERE A RELATIONSHIP BETWEEN THE TWO?*\n",
+    "\n",
+    "## *Findings \n",
+    "-The number of total people in the survey was **432** \n",
+    "\n",
+    "-The number of people who smoke was **72** or **17%** of people in survey\n",
+    "\n",
+    "-The number of people who drink was **339** or **78%** of people in survey\n",
+    "\n",
+    "-The number of people who gamble was **216** or **50%** of people in survey\n",
+    "\n",
+    "-The number of people that order their steak rare was **23** or **5%** \n",
+    "\n",
+    "-The number of people who smoke and order steak rare was **6** or **1%** of people in survey\n",
+    "\n",
+    "-The number of people who drink and order steak rare was **19** or **4%** of people in survey\n",
+    "\n",
+    "-The number of people who Gamble and order steak rare was **12** or **3%** of people in survey\n",
+    "\n",
+    "# The most surprising stat was that the Number of people who do all 3 risky behaviors (smoke, drink, gamble) and order steak rare was **5** or **1%*\n",
+    "\n",
+    "\n",
+    "\n",
+    "**What did I learn about my data?**\n",
+    "I learned that you can extract a bunch of different data about a survey. This was kind of a silly survey, but fun to see the results. What I learned was that out of small sample size of 432 people, doing risky behaviors such as smoking, drinking, and gambling, have nothing to do with food choices, specifically risky food choices such as ordering steak rare.\n",
+    "\n",
+    "**Did the results make sense?**\n",
+    "Yes. The results made sense to me in the fact that I came in with a preconceived notion that the two wouldnt be related. What would have been interesting would be if you did a study between people who do risky behaviors who also live shorter lives.\n",
+    "\n",
+    "**What was most surprising?**\n",
+    "# The most surprising stat was that the Number of people who do all 3 risky behaviors (smoke, drink, gamble) and order steak rare was **5** or **1%*\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "infectious-symbol",
+   "metadata": {},
+   "source": [
+    "# Discussion"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bearing-stadium",
+   "metadata": {},
+   "source": [
+    "*It's important to recognize the limitations of our research.\n",
+    "Consider the following:*\n",
+    "\n",
+    "**Do the results give an accurate depiction of your research question? Why or why not?**\n",
+    "Yes becasue it answers the question.\n",
+    "  \n",
+    "**What were limitations of your datset?**\n",
+    "I only had a small sample size.\n",
+    "\n",
+    "**Are there any known biases in the data?**\n",
+    "I dont believe so.\n",
+    "\n",
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "cell_metadata_json": true,
+   "text_representation": {
+    "extension": ".Rmd",
+    "format_name": "rmarkdown",
+    "format_version": "1.2",
+    "jupytext_version": "1.9.1"
+   }
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/.ipynb_checkpoints/lab_pokemon-checkpoint.ipynb b/.ipynb_checkpoints/lab_pokemon-checkpoint.ipynb
new file mode 100644
index 0000000..6e82c33
--- /dev/null
+++ b/.ipynb_checkpoints/lab_pokemon-checkpoint.ipynb
@@ -0,0 +1,2308 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "90041b00-672b-4bd4-a8e8-0cab3f0548af",
+   "metadata": {},
+   "source": [
+    "# Lab 04: Data Science Tools\n",
+    "\n",
+    "## 0. Jupyter Notebooks\n",
+    "\n",
+    "Welcome to your first Jupyter notebook! Notebooks are made up of cells. Some cells contain text (like this one) and others contain Python code.\n",
+    "\n",
+    "Each cell can be in two different modes: editing or running. To edit a cell, double-click on it. When you're done editing, press **shift+Enter** to run it.  You can use [Markdown](https://www.markdownguide.org/cheat-sheet/) to add basic formatting to the text. Before you go on, try editing the text in this cell."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "5923b0d7-c0e0-48fa-b765-4aa6002c2d4f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Other cells are code cells, containing Python code. (This is a comment, of course!)\n",
+    "# Try running this cell (again, shift+Enter). You'll see the result of the final statement \n",
+    "# printed below the cell. \n",
+    "# Then try changing the Python code and re-run it.\n",
+    "\n",
+    "1+1+1+2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "257ef44f-8f53-4136-9d0d-23a811ec53e9",
+   "metadata": {},
+   "source": [
+    "### 0.1 Cells share state\n",
+    "\n",
+    "Even though code cells run one at a time, anything that happens in a cell (like declaring a variable or running a function) affects the whole notebook. Try running these two cells a few times, in different orders. What happens when you run *Cell B* over and over?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "0e2a2927-f6d1-4b13-97ae-ff97416723e9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Cell A\n",
+    "x = 9\n",
+    "x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "69dd7908-b213-4d0f-8016-e46a4a491961",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "18"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Cell B\n",
+    "x = x * 2\n",
+    "x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "adc581ac-db13-40a8-bcfc-bf5d6e5472c5",
+   "metadata": {},
+   "source": [
+    "### 0.2 Saving your work\n",
+    "\n",
+    "When you finish working on a notebook, save your work using the icon in the menu bar above. Your notebook is stored in the file `lab_pokemon.ipynb` in the lab directory. You can commit your changes to `ipynb` files just like any other file. Once you finish with Jupyter, you can stop the server by pressing **Control + C** in the Terminal. \n",
+    "\n",
+    "*If you're doing this lab on a cloud-based platform like Binder, then you can't save your work. So don't close the tab!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0269bf0f-b993-4dfe-99cd-a7d38e94546c",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "## 1. Pandas\n",
+    "\n",
+    "Pandas is probably the most important Python library for data science. Pandas provides an object called a **DataFrame**, which is basically a table with rows and columns. Most of the time, you will load data into Pandas using a `.csv` file. CSV files can be exported from Excel or Google Sheets, and are a common format for public data sets. \n",
+    "\n",
+    "In this lab, we'll be working with two data sets: The first contains Pokémon characteristics and the second comes from a wide-scale survey conducted by the US Centers for Disease Control ([details](https://www.cdc.gov/brfss/annual_data/annual_2020.html)). We will demonstrate techniques with Pokémon; your job is to replicate these tasks with the CDC dataset. \n",
+    "\n",
+    "**Note:** Pandas has *extensive* capabilities, and there's no way we could possibly present them all here. If you have a clearly-formed idea of what you want to do with tabular data, there's a way to do it. This lab introduces *some* of what Pandas can do, but expect to spend time reading the documentation and Stack Overflow when you start working on new tasks. \n",
+    "\n",
+    "### 1.0 Getting started\n",
+    "\n",
+    "First, we'll import pandas (using the conventional variable name `pd`) and load the two datasets. *Run these cells and every code cell you encounter in this notebook.*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f60aa4b0-7050-4e43-9619-5f8500770cb0",
+   "metadata": {},
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "pokemon = pd.read_csv(\"pokemon.csv\")\n",
+    "people = pd.read_csv(\"brfss_2020.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d4e0b811-b8bf-4e9a-a934-3aad8f0520bb",
+   "metadata": {},
+   "source": [
+    "### 1.1 A first look\n",
+    "\n",
+    "#### Demo\n",
+    "\n",
+    "Let's start by learning the *shape* of the data. How many columns are there? How many rows? What kinds of data are included?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "579d8dda-ca39-48b1-8819-b17651029729",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "pokemon = pd.read_csv(\"pokemon.csv\")\n",
+    "people = pd.read_csv(\"brfss_2020.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ee8b0718-56f9-4fc8-bd35-fa0ccb445179",
+   "metadata": {},
+   "source": [
+    "OK, 800 Pokémon, with 12 columns for each. And you can see all the columns. Not all the data is shown in this preview, of course. If there were more columns than could be displayed, you could see them all by typing `pokemon.columns`. \n",
+    "\n",
+    "#### Your turn\n",
+    "\n",
+    "Now do the same for your data set, `people`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "c9e5e4ec-b197-450c-ae2d-318006fa0a2f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>income</th>\n",
+       "      <th>education</th>\n",
+       "      <th>sexual_orientation</th>\n",
+       "      <th>height</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>health</th>\n",
+       "      <th>no_doctor</th>\n",
+       "      <th>exercise</th>\n",
+       "      <th>sleep</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>55</td>\n",
+       "      <td>female</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>other</td>\n",
+       "      <td>1.55</td>\n",
+       "      <td>83.01</td>\n",
+       "      <td>2</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>65</td>\n",
+       "      <td>female</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1</td>\n",
+       "      <td>heterosexual</td>\n",
+       "      <td>1.65</td>\n",
+       "      <td>78.02</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>35</td>\n",
+       "      <td>female</td>\n",
+       "      <td>8</td>\n",
+       "      <td>4</td>\n",
+       "      <td>heterosexual</td>\n",
+       "      <td>1.65</td>\n",
+       "      <td>77.11</td>\n",
+       "      <td>4</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>55</td>\n",
+       "      <td>male</td>\n",
+       "      <td>8</td>\n",
+       "      <td>4</td>\n",
+       "      <td>heterosexual</td>\n",
+       "      <td>1.83</td>\n",
+       "      <td>81.65</td>\n",
+       "      <td>5</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>55</td>\n",
+       "      <td>female</td>\n",
+       "      <td>8</td>\n",
+       "      <td>4</td>\n",
+       "      <td>heterosexual</td>\n",
+       "      <td>1.80</td>\n",
+       "      <td>76.66</td>\n",
+       "      <td>4</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>166420</th>\n",
+       "      <td>45</td>\n",
+       "      <td>female</td>\n",
+       "      <td>8</td>\n",
+       "      <td>3</td>\n",
+       "      <td>heterosexual</td>\n",
+       "      <td>1.63</td>\n",
+       "      <td>86.18</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>166421</th>\n",
+       "      <td>25</td>\n",
+       "      <td>male</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "      <td>heterosexual</td>\n",
+       "      <td>1.78</td>\n",
+       "      <td>86.18</td>\n",
+       "      <td>4</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>166422</th>\n",
+       "      <td>25</td>\n",
+       "      <td>female</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>heterosexual</td>\n",
+       "      <td>1.91</td>\n",
+       "      <td>45.36</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>166423</th>\n",
+       "      <td>35</td>\n",
+       "      <td>female</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>heterosexual</td>\n",
+       "      <td>1.60</td>\n",
+       "      <td>68.04</td>\n",
+       "      <td>4</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>166424</th>\n",
+       "      <td>35</td>\n",
+       "      <td>male</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "      <td>heterosexual</td>\n",
+       "      <td>1.75</td>\n",
+       "      <td>86.18</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>166425 rows × 11 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        age     sex  income  education sexual_orientation  height  weight  \\\n",
+       "0        55  female       5          2              other    1.55   83.01   \n",
+       "1        65  female       8          1       heterosexual    1.65   78.02   \n",
+       "2        35  female       8          4       heterosexual    1.65   77.11   \n",
+       "3        55    male       8          4       heterosexual    1.83   81.65   \n",
+       "4        55  female       8          4       heterosexual    1.80   76.66   \n",
+       "...     ...     ...     ...        ...                ...     ...     ...   \n",
+       "166420   45  female       8          3       heterosexual    1.63   86.18   \n",
+       "166421   25    male       7          2       heterosexual    1.78   86.18   \n",
+       "166422   25  female       1          2       heterosexual    1.91   45.36   \n",
+       "166423   35  female       5          4       heterosexual    1.60   68.04   \n",
+       "166424   35    male       7          2       heterosexual    1.75   86.18   \n",
+       "\n",
+       "        health  no_doctor  exercise  sleep  \n",
+       "0            2       True      True      7  \n",
+       "1            3      False     False      8  \n",
+       "2            4       True      True      7  \n",
+       "3            5      False      True      8  \n",
+       "4            4      False      True      8  \n",
+       "...        ...        ...       ...    ...  \n",
+       "166420       1      False     False      6  \n",
+       "166421       4      False      True      6  \n",
+       "166422       1      False     False      8  \n",
+       "166423       4       True      True      6  \n",
+       "166424       3      False     False      8  \n",
+       "\n",
+       "[166425 rows x 11 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "people\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7fab76ef-d453-4568-a916-4d4c29535a42",
+   "metadata": {},
+   "source": [
+    "### 1.2 Descriptive Statistics\n",
+    "\n",
+    "#### Demo\n",
+    "\n",
+    "Let's get a sense of the data contained in some of the columns. For categorical data like `generation`, it makes sense to look at value counts--showing us how many of each category there are. You can use the optional keyword `normalize=True` to see percentage of total instead of frequencies. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "9afca362-9edc-423c-981b-dc42107d5de0",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'people' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mpeople\u001b[49m\u001b[38;5;241m.\u001b[39mgeneration\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'people' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "people.generation\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a9b98eee-bdc2-4c63-bab2-ee82e2466d0f",
+   "metadata": {},
+   "source": [
+    "For numeric data, we could start by looking at the mean value. We can select multiple columns and get all the column means at once."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "5fe580d0-5939-4152-9f8c-4c32d35a4479",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "hp         69.25875\n",
+       "attack     79.00125\n",
+       "defense    73.84250\n",
+       "speed      68.27750\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pokemon[[\"hp\", \"attack\", \"defense\", \"speed\"]].mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0d8e6e78-fcfc-4c38-a418-545fe4216a44",
+   "metadata": {},
+   "source": [
+    "We can also compute the mean of boolean data. In this case, True will map to 1 and False will map to 0. So the mean value equals the percentage of data which is True. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "dc69ef53-70cd-4ae0-80e7-c9c8e28de76f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(0.08125)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pokemon.legendary.mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "69333e87-8df2-4b46-9005-2b8c9df3a7b4",
+   "metadata": {},
+   "source": [
+    "Just over 8% of Pokemon are legendary."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f563d97d-d9d3-4f2d-a46a-5d5dfc6382de",
+   "metadata": {},
+   "source": [
+    "#### Your turn\n",
+    "\n",
+    "**1.2.0.** In this survey, people are grouped into age bands of 18-24, 25-34, 35-44, 45-54, 55-64, and 65+, with the lower bound reported. What percentage of people are in each age band? (When we talk about \"people\" in this lab, we're referring to the people who responded to the survey, not the whole US population.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "8fbcc766-8399-4f93-a6c8-e0607250a72a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(48.76603274748385)"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "people.age.mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38006e7b-4771-4c29-86a8-19d04a50fc25",
+   "metadata": {},
+   "source": [
+    "**1.2.1.** What are the mean height and weight of people in this survey?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "b7f910c8-3d40-49ae-b270-678734c04100",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "height     1.705082\n",
+       "weight    83.053588\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "people[[\"height\", \"weight\"]].mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f74634bb-8664-46e4-b371-6f45cbb7c8ef",
+   "metadata": {},
+   "source": [
+    "**1.2.2.** The `exercise` column indicates whether a person has done any physical activity or exercise in the last 30 days, outside of work. What percentage of people have done exercise?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "f3891188-a85f-4089-8388-d4d81c7438ad",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(0.7858014120474688)"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "people.exercise.mean()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f6082e65-321c-4ee0-9457-74f9bb1b0363",
+   "metadata": {},
+   "source": [
+    "### 1.3 Filtering\n",
+    "\n",
+    "Sometimes we're just interested in a selection of the data set. The way to do this is to create a boolean series, and then use this to select which rows you want to include. Vocabulary note: A dataframe is two-dimensional, with rows and columns. A series (a single row or a single column) is one-dimensional. \n",
+    "\n",
+    "#### Demo\n",
+    "`pokemon.legendary` is already boolean, so we can use this to select just the legendary pokémon. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "12c0c6c9-c07b-4183-82f6-5e346c74aac9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>type</th>\n",
+       "      <th>subtype</th>\n",
+       "      <th>total</th>\n",
+       "      <th>hp</th>\n",
+       "      <th>attack</th>\n",
+       "      <th>defense</th>\n",
+       "      <th>special_attack</th>\n",
+       "      <th>special_defense</th>\n",
+       "      <th>speed</th>\n",
+       "      <th>generation</th>\n",
+       "      <th>legendary</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>156</th>\n",
+       "      <td>Articuno</td>\n",
+       "      <td>Ice</td>\n",
+       "      <td>Flying</td>\n",
+       "      <td>580</td>\n",
+       "      <td>90</td>\n",
+       "      <td>85</td>\n",
+       "      <td>100</td>\n",
+       "      <td>95</td>\n",
+       "      <td>125</td>\n",
+       "      <td>85</td>\n",
+       "      <td>1</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>157</th>\n",
+       "      <td>Zapdos</td>\n",
+       "      <td>Electric</td>\n",
+       "      <td>Flying</td>\n",
+       "      <td>580</td>\n",
+       "      <td>90</td>\n",
+       "      <td>90</td>\n",
+       "      <td>85</td>\n",
+       "      <td>125</td>\n",
+       "      <td>90</td>\n",
+       "      <td>100</td>\n",
+       "      <td>1</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>158</th>\n",
+       "      <td>Moltres</td>\n",
+       "      <td>Fire</td>\n",
+       "      <td>Flying</td>\n",
+       "      <td>580</td>\n",
+       "      <td>90</td>\n",
+       "      <td>100</td>\n",
+       "      <td>90</td>\n",
+       "      <td>125</td>\n",
+       "      <td>85</td>\n",
+       "      <td>90</td>\n",
+       "      <td>1</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>162</th>\n",
+       "      <td>Mewtwo</td>\n",
+       "      <td>Psychic</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>680</td>\n",
+       "      <td>106</td>\n",
+       "      <td>110</td>\n",
+       "      <td>90</td>\n",
+       "      <td>154</td>\n",
+       "      <td>90</td>\n",
+       "      <td>130</td>\n",
+       "      <td>1</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>163</th>\n",
+       "      <td>MewtwoMega Mewtwo X</td>\n",
+       "      <td>Psychic</td>\n",
+       "      <td>Fighting</td>\n",
+       "      <td>780</td>\n",
+       "      <td>106</td>\n",
+       "      <td>190</td>\n",
+       "      <td>100</td>\n",
+       "      <td>154</td>\n",
+       "      <td>100</td>\n",
+       "      <td>130</td>\n",
+       "      <td>1</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>795</th>\n",
+       "      <td>Diancie</td>\n",
+       "      <td>Rock</td>\n",
+       "      <td>Fairy</td>\n",
+       "      <td>600</td>\n",
+       "      <td>50</td>\n",
+       "      <td>100</td>\n",
+       "      <td>150</td>\n",
+       "      <td>100</td>\n",
+       "      <td>150</td>\n",
+       "      <td>50</td>\n",
+       "      <td>6</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>796</th>\n",
+       "      <td>DiancieMega Diancie</td>\n",
+       "      <td>Rock</td>\n",
+       "      <td>Fairy</td>\n",
+       "      <td>700</td>\n",
+       "      <td>50</td>\n",
+       "      <td>160</td>\n",
+       "      <td>110</td>\n",
+       "      <td>160</td>\n",
+       "      <td>110</td>\n",
+       "      <td>110</td>\n",
+       "      <td>6</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>797</th>\n",
+       "      <td>HoopaHoopa Confined</td>\n",
+       "      <td>Psychic</td>\n",
+       "      <td>Ghost</td>\n",
+       "      <td>600</td>\n",
+       "      <td>80</td>\n",
+       "      <td>110</td>\n",
+       "      <td>60</td>\n",
+       "      <td>150</td>\n",
+       "      <td>130</td>\n",
+       "      <td>70</td>\n",
+       "      <td>6</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>798</th>\n",
+       "      <td>HoopaHoopa Unbound</td>\n",
+       "      <td>Psychic</td>\n",
+       "      <td>Dark</td>\n",
+       "      <td>680</td>\n",
+       "      <td>80</td>\n",
+       "      <td>160</td>\n",
+       "      <td>60</td>\n",
+       "      <td>170</td>\n",
+       "      <td>130</td>\n",
+       "      <td>80</td>\n",
+       "      <td>6</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>799</th>\n",
+       "      <td>Volcanion</td>\n",
+       "      <td>Fire</td>\n",
+       "      <td>Water</td>\n",
+       "      <td>600</td>\n",
+       "      <td>80</td>\n",
+       "      <td>110</td>\n",
+       "      <td>120</td>\n",
+       "      <td>130</td>\n",
+       "      <td>90</td>\n",
+       "      <td>70</td>\n",
+       "      <td>6</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>65 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    name      type   subtype  total   hp  attack  defense  \\\n",
+       "156             Articuno       Ice    Flying    580   90      85      100   \n",
+       "157               Zapdos  Electric    Flying    580   90      90       85   \n",
+       "158              Moltres      Fire    Flying    580   90     100       90   \n",
+       "162               Mewtwo   Psychic       NaN    680  106     110       90   \n",
+       "163  MewtwoMega Mewtwo X   Psychic  Fighting    780  106     190      100   \n",
+       "..                   ...       ...       ...    ...  ...     ...      ...   \n",
+       "795              Diancie      Rock     Fairy    600   50     100      150   \n",
+       "796  DiancieMega Diancie      Rock     Fairy    700   50     160      110   \n",
+       "797  HoopaHoopa Confined   Psychic     Ghost    600   80     110       60   \n",
+       "798   HoopaHoopa Unbound   Psychic      Dark    680   80     160       60   \n",
+       "799            Volcanion      Fire     Water    600   80     110      120   \n",
+       "\n",
+       "     special_attack  special_defense  speed  generation  legendary  \n",
+       "156              95              125     85           1       True  \n",
+       "157             125               90    100           1       True  \n",
+       "158             125               85     90           1       True  \n",
+       "162             154               90    130           1       True  \n",
+       "163             154              100    130           1       True  \n",
+       "..              ...              ...    ...         ...        ...  \n",
+       "795             100              150     50           6       True  \n",
+       "796             160              110    110           6       True  \n",
+       "797             150              130     70           6       True  \n",
+       "798             170              130     80           6       True  \n",
+       "799             130               90     70           6       True  \n",
+       "\n",
+       "[65 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "legendary = pokemon[pokemon.legendary]\n",
+    "legendary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b4ad804a-f5f0-441f-bb83-51f360c1c154",
+   "metadata": {},
+   "source": [
+    "Let's get all the ice pokémon. We can create a boolean series from another series..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ea42d67-4dd0-47c8-b4da-1c29fb4507ea",
+   "metadata": {},
+   "source": [
+    "pokemon.type == \"Ice\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a5ea9e89-f466-48de-9133-346c99f4a6c1",
+   "metadata": {},
+   "source": [
+    "And then use this series to select just the ice pokémon. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c65eb1bd-72b6-487e-adac-ae2071762ca2",
+   "metadata": {
+    "scrolled": true
+   },
+   "source": [
+    "ice = pokemon[pokemon.type == \"Ice\"]\n",
+    "ice"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "8161cdbd-76af-4a45-bed9-665ad37cd267",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'DataFrame' object has no attribute 'type'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m/tmp/ipykernel_1131/3928984197.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgood\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpeople\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpeople\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"good\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mice\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.cache/pypoetry/virtualenvs/lab-pokemon-MIddldub-py3.12/lib/python3.12/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   6295\u001b[0m             \u001b[0;32mand\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_accessors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6296\u001b[0m             \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6297\u001b[0m         \u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6298\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6299\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'type'"
+     ]
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "3ce81665-f1c6-4528-9d0b-56b5da9ccbe3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>type</th>\n",
+       "      <th>subtype</th>\n",
+       "      <th>total</th>\n",
+       "      <th>hp</th>\n",
+       "      <th>attack</th>\n",
+       "      <th>defense</th>\n",
+       "      <th>special_attack</th>\n",
+       "      <th>special_defense</th>\n",
+       "      <th>speed</th>\n",
+       "      <th>generation</th>\n",
+       "      <th>legendary</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>156</th>\n",
+       "      <td>Articuno</td>\n",
+       "      <td>Ice</td>\n",
+       "      <td>Flying</td>\n",
+       "      <td>580</td>\n",
+       "      <td>90</td>\n",
+       "      <td>85</td>\n",
+       "      <td>100</td>\n",
+       "      <td>95</td>\n",
+       "      <td>125</td>\n",
+       "      <td>85</td>\n",
+       "      <td>1</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>157</th>\n",
+       "      <td>Zapdos</td>\n",
+       "      <td>Electric</td>\n",
+       "      <td>Flying</td>\n",
+       "      <td>580</td>\n",
+       "      <td>90</td>\n",
+       "      <td>90</td>\n",
+       "      <td>85</td>\n",
+       "      <td>125</td>\n",
+       "      <td>90</td>\n",
+       "      <td>100</td>\n",
+       "      <td>1</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>158</th>\n",
+       "      <td>Moltres</td>\n",
+       "      <td>Fire</td>\n",
+       "      <td>Flying</td>\n",
+       "      <td>580</td>\n",
+       "      <td>90</td>\n",
+       "      <td>100</td>\n",
+       "      <td>90</td>\n",
+       "      <td>125</td>\n",
+       "      <td>85</td>\n",
+       "      <td>90</td>\n",
+       "      <td>1</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>162</th>\n",
+       "      <td>Mewtwo</td>\n",
+       "      <td>Psychic</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>680</td>\n",
+       "      <td>106</td>\n",
+       "      <td>110</td>\n",
+       "      <td>90</td>\n",
+       "      <td>154</td>\n",
+       "      <td>90</td>\n",
+       "      <td>130</td>\n",
+       "      <td>1</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>163</th>\n",
+       "      <td>MewtwoMega Mewtwo X</td>\n",
+       "      <td>Psychic</td>\n",
+       "      <td>Fighting</td>\n",
+       "      <td>780</td>\n",
+       "      <td>106</td>\n",
+       "      <td>190</td>\n",
+       "      <td>100</td>\n",
+       "      <td>154</td>\n",
+       "      <td>100</td>\n",
+       "      <td>130</td>\n",
+       "      <td>1</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>795</th>\n",
+       "      <td>Diancie</td>\n",
+       "      <td>Rock</td>\n",
+       "      <td>Fairy</td>\n",
+       "      <td>600</td>\n",
+       "      <td>50</td>\n",
+       "      <td>100</td>\n",
+       "      <td>150</td>\n",
+       "      <td>100</td>\n",
+       "      <td>150</td>\n",
+       "      <td>50</td>\n",
+       "      <td>6</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>796</th>\n",
+       "      <td>DiancieMega Diancie</td>\n",
+       "      <td>Rock</td>\n",
+       "      <td>Fairy</td>\n",
+       "      <td>700</td>\n",
+       "      <td>50</td>\n",
+       "      <td>160</td>\n",
+       "      <td>110</td>\n",
+       "      <td>160</td>\n",
+       "      <td>110</td>\n",
+       "      <td>110</td>\n",
+       "      <td>6</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>797</th>\n",
+       "      <td>HoopaHoopa Confined</td>\n",
+       "      <td>Psychic</td>\n",
+       "      <td>Ghost</td>\n",
+       "      <td>600</td>\n",
+       "      <td>80</td>\n",
+       "      <td>110</td>\n",
+       "      <td>60</td>\n",
+       "      <td>150</td>\n",
+       "      <td>130</td>\n",
+       "      <td>70</td>\n",
+       "      <td>6</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>798</th>\n",
+       "      <td>HoopaHoopa Unbound</td>\n",
+       "      <td>Psychic</td>\n",
+       "      <td>Dark</td>\n",
+       "      <td>680</td>\n",
+       "      <td>80</td>\n",
+       "      <td>160</td>\n",
+       "      <td>60</td>\n",
+       "      <td>170</td>\n",
+       "      <td>130</td>\n",
+       "      <td>80</td>\n",
+       "      <td>6</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>799</th>\n",
+       "      <td>Volcanion</td>\n",
+       "      <td>Fire</td>\n",
+       "      <td>Water</td>\n",
+       "      <td>600</td>\n",
+       "      <td>80</td>\n",
+       "      <td>110</td>\n",
+       "      <td>120</td>\n",
+       "      <td>130</td>\n",
+       "      <td>90</td>\n",
+       "      <td>70</td>\n",
+       "      <td>6</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>65 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    name      type   subtype  total   hp  attack  defense  \\\n",
+       "156             Articuno       Ice    Flying    580   90      85      100   \n",
+       "157               Zapdos  Electric    Flying    580   90      90       85   \n",
+       "158              Moltres      Fire    Flying    580   90     100       90   \n",
+       "162               Mewtwo   Psychic       NaN    680  106     110       90   \n",
+       "163  MewtwoMega Mewtwo X   Psychic  Fighting    780  106     190      100   \n",
+       "..                   ...       ...       ...    ...  ...     ...      ...   \n",
+       "795              Diancie      Rock     Fairy    600   50     100      150   \n",
+       "796  DiancieMega Diancie      Rock     Fairy    700   50     160      110   \n",
+       "797  HoopaHoopa Confined   Psychic     Ghost    600   80     110       60   \n",
+       "798   HoopaHoopa Unbound   Psychic      Dark    680   80     160       60   \n",
+       "799            Volcanion      Fire     Water    600   80     110      120   \n",
+       "\n",
+       "     special_attack  special_defense  speed  generation  legendary  \n",
+       "156              95              125     85           1       True  \n",
+       "157             125               90    100           1       True  \n",
+       "158             125               85     90           1       True  \n",
+       "162             154               90    130           1       True  \n",
+       "163             154              100    130           1       True  \n",
+       "..              ...              ...    ...         ...        ...  \n",
+       "795             100              150     50           6       True  \n",
+       "796             160              110    110           6       True  \n",
+       "797             150              130     70           6       True  \n",
+       "798             170              130     80           6       True  \n",
+       "799             130               90     70           6       True  \n",
+       "\n",
+       "[65 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "legendary = pokemon[pokemon.legendary]\n",
+    "legendary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0af5f534-0bec-4577-beee-29b350102265",
+   "metadata": {},
+   "source": [
+    "Let's get the high-speed ice pokémon. You can join conditions together using the `&` (and) and `|` (or) operators. `~` means \"not\", so `pokemon[~(pokemon.type == \"Ice\")]` would select all the non-ice pokémon. Due to order of operations, each condition needs to be wrapped in parentheses."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "bbbeeeef-3490-48f1-aadf-c39c31c6c41b",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'high_speed' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[29], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mhigh_speed\u001b[49m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'high_speed' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "high_speed"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c84dc7ce-24f2-4ac7-92d7-99ed331488e0",
+   "metadata": {},
+   "source": [
+    "You could get the pokémon who are fire or ice by selecting `pokemon[(pokemon.type == \"Fire\") | (pokemon.type == \"Ice\")]`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1f0e9625-b194-450d-b003-b88798cc2f45",
+   "metadata": {},
+   "source": [
+    "#### Your turn\n",
+    "\n",
+    "**1.3.0.** `no_doctor` indicates whether there was a time in the last year when the person needed to see a doctor, but could not afford to do so. Create a dataframe containing only these people. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "198cb0c6-3f43-43c2-9eee-3939c12ea537",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0          True\n",
+       "1         False\n",
+       "2          True\n",
+       "3         False\n",
+       "4         False\n",
+       "          ...  \n",
+       "166420    False\n",
+       "166421    False\n",
+       "166422    False\n",
+       "166423     True\n",
+       "166424    False\n",
+       "Name: no_doctor, Length: 166425, dtype: bool"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "people.no_doctor"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9d213707-a15b-4751-8df9-48aa568af209",
+   "metadata": {},
+   "source": [
+    "**1.3.1.** `health` asks people for their general health, with the meanings of numbers shown below. Create a dataframe which contains people whose general health is good or better. \n",
+    "\n",
+    "| number | health status | \n",
+    "| ------ | ----------- |\n",
+    "| 1      | Poor        |\n",
+    "| 2      | Fair        |\n",
+    "| 3      | Good        |\n",
+    "| 4      | Very good   |\n",
+    "| 5      | Excellent   |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "8a8c1ad6-4c1e-4996-ab5e-5212dadb1851",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0         2\n",
+       "1         3\n",
+       "2         4\n",
+       "3         5\n",
+       "4         4\n",
+       "         ..\n",
+       "166420    1\n",
+       "166421    4\n",
+       "166422    1\n",
+       "166423    4\n",
+       "166424    3\n",
+       "Name: health, Length: 166425, dtype: int64"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "people.health\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "a50cfa93-8ff7-46ee-b4c4-5c07255ca870",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'DataFrame' object has no attribute 'health_status'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m/tmp/ipykernel_1131/1799491424.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhealth_status\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpeople\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpeople\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhealth_status\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/.cache/pypoetry/virtualenvs/lab-pokemon-MIddldub-py3.12/lib/python3.12/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   6295\u001b[0m             \u001b[0;32mand\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_accessors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6296\u001b[0m             \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6297\u001b[0m         \u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6298\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6299\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'health_status'"
+     ]
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "ef61ce67-b36f-4e80-9d57-8b80a7d9eb2b",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'DataFrame' object has no attribute 'good'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m/tmp/ipykernel_1131/4081568713.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mpeople\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhealth\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"good\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mpeople\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgood\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/.cache/pypoetry/virtualenvs/lab-pokemon-MIddldub-py3.12/lib/python3.12/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   6295\u001b[0m             \u001b[0;32mand\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_accessors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6296\u001b[0m             \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6297\u001b[0m         \u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6298\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6299\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'good'"
+     ]
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7add542b-bfd2-481a-b5b4-4e1ca744078a",
+   "metadata": {},
+   "source": [
+    "**1.3.2.**. `education` indicates the highest level of education completed, with codes as follows. Create a dataframe which only contains female college graduates who needed a doctor but couldn't afford one. (The survey asked people for their current sex, and only had options for male and female.)\n",
+    "\n",
+    "| number | education level      | \n",
+    "| ------ | ----------- |\n",
+    "| 1      | Did not graduate from high school        |\n",
+    "| 2      | Graduated from high school        |\n",
+    "| 3      | Attended some college        |\n",
+    "| 4      | Graduated from college   |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "315682ae-7d54-4d78-9a63-d23c83ba1576",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(3.0645515998197386)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "people.education.mean()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f4b023b1-b754-4b6a-a7e6-b840ae180501",
+   "metadata": {},
+   "source": [
+    "### 1.4. Grouping\n",
+    "\n",
+    "Now things get crazy. You can group a dataframe using one or more columns, and then compare their statistics. \n",
+    "\n",
+    "#### Demo\n",
+    "\n",
+    "Do different types of pokémon move at different speeds? We'll use `sort_values` to put these in order from slow to fast."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "069ea0ab-eff6-4985-9f46-db956fe1df91",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "type\n",
+       "Fairy        48.588235\n",
+       "Steel        55.259259\n",
+       "Rock         55.909091\n",
+       "Bug          61.681159\n",
+       "Grass        61.928571\n",
+       "Ice          63.458333\n",
+       "Poison       63.571429\n",
+       "Ground       63.906250\n",
+       "Ghost        64.343750\n",
+       "Water        65.964286\n",
+       "Fighting     66.074074\n",
+       "Normal       71.551020\n",
+       "Fire         74.442308\n",
+       "Dark         76.161290\n",
+       "Psychic      81.491228\n",
+       "Dragon       83.031250\n",
+       "Electric     84.500000\n",
+       "Flying      102.500000\n",
+       "Name: speed, dtype: float64"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pokemon.groupby(\"type\").speed.mean().sort_values()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bdc801b7-d3ae-45bb-80f4-ebeb474e20a1",
+   "metadata": {},
+   "source": [
+    "Do types differ in other stats? Let's sort by hit points. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "5c420c0e-b5d2-49ae-ab98-3305ee076169",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>hp</th>\n",
+       "      <th>attack</th>\n",
+       "      <th>defense</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>type</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Bug</th>\n",
+       "      <td>56.884058</td>\n",
+       "      <td>70.971014</td>\n",
+       "      <td>70.724638</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Electric</th>\n",
+       "      <td>59.795455</td>\n",
+       "      <td>69.090909</td>\n",
+       "      <td>66.295455</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Ghost</th>\n",
+       "      <td>64.437500</td>\n",
+       "      <td>73.781250</td>\n",
+       "      <td>81.187500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Steel</th>\n",
+       "      <td>65.222222</td>\n",
+       "      <td>92.703704</td>\n",
+       "      <td>126.370370</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Rock</th>\n",
+       "      <td>65.363636</td>\n",
+       "      <td>92.863636</td>\n",
+       "      <td>100.795455</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Dark</th>\n",
+       "      <td>66.806452</td>\n",
+       "      <td>88.387097</td>\n",
+       "      <td>70.225806</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Poison</th>\n",
+       "      <td>67.250000</td>\n",
+       "      <td>74.678571</td>\n",
+       "      <td>68.821429</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Grass</th>\n",
+       "      <td>67.271429</td>\n",
+       "      <td>73.214286</td>\n",
+       "      <td>70.800000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fighting</th>\n",
+       "      <td>69.851852</td>\n",
+       "      <td>96.777778</td>\n",
+       "      <td>65.925926</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fire</th>\n",
+       "      <td>69.903846</td>\n",
+       "      <td>84.769231</td>\n",
+       "      <td>67.769231</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Psychic</th>\n",
+       "      <td>70.631579</td>\n",
+       "      <td>71.456140</td>\n",
+       "      <td>67.684211</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Flying</th>\n",
+       "      <td>70.750000</td>\n",
+       "      <td>78.750000</td>\n",
+       "      <td>66.250000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Ice</th>\n",
+       "      <td>72.000000</td>\n",
+       "      <td>72.750000</td>\n",
+       "      <td>71.416667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Water</th>\n",
+       "      <td>72.062500</td>\n",
+       "      <td>74.151786</td>\n",
+       "      <td>72.946429</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Ground</th>\n",
+       "      <td>73.781250</td>\n",
+       "      <td>95.750000</td>\n",
+       "      <td>84.843750</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fairy</th>\n",
+       "      <td>74.117647</td>\n",
+       "      <td>61.529412</td>\n",
+       "      <td>65.705882</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Normal</th>\n",
+       "      <td>77.275510</td>\n",
+       "      <td>73.469388</td>\n",
+       "      <td>59.846939</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Dragon</th>\n",
+       "      <td>83.312500</td>\n",
+       "      <td>112.125000</td>\n",
+       "      <td>86.375000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 hp      attack     defense\n",
+       "type                                       \n",
+       "Bug       56.884058   70.971014   70.724638\n",
+       "Electric  59.795455   69.090909   66.295455\n",
+       "Ghost     64.437500   73.781250   81.187500\n",
+       "Steel     65.222222   92.703704  126.370370\n",
+       "Rock      65.363636   92.863636  100.795455\n",
+       "Dark      66.806452   88.387097   70.225806\n",
+       "Poison    67.250000   74.678571   68.821429\n",
+       "Grass     67.271429   73.214286   70.800000\n",
+       "Fighting  69.851852   96.777778   65.925926\n",
+       "Fire      69.903846   84.769231   67.769231\n",
+       "Psychic   70.631579   71.456140   67.684211\n",
+       "Flying    70.750000   78.750000   66.250000\n",
+       "Ice       72.000000   72.750000   71.416667\n",
+       "Water     72.062500   74.151786   72.946429\n",
+       "Ground    73.781250   95.750000   84.843750\n",
+       "Fairy     74.117647   61.529412   65.705882\n",
+       "Normal    77.275510   73.469388   59.846939\n",
+       "Dragon    83.312500  112.125000   86.375000"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ptypes = pokemon.groupby(\"type\")\n",
+    "ptypes[[\"hp\", \"attack\", \"defense\"]].mean().sort_values(\"hp\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cc9a3d19-0ecd-487b-b34f-b748c44fc9c9",
+   "metadata": {},
+   "source": [
+    "Which type/subtype combinations are most likely to have legendary pokémon?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "444a580d-e70c-48a1-bf87-77f98b8c9f85",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "type      subtype \n",
+       "Electric  Flying      0.600000\n",
+       "Rock      Fairy       0.666667\n",
+       "Ghost     Dragon      1.000000\n",
+       "Ground    Fire        1.000000\n",
+       "Fire      Water       1.000000\n",
+       "          Steel       1.000000\n",
+       "Steel     Dragon      1.000000\n",
+       "Dragon    Electric    1.000000\n",
+       "Psychic   Ghost       1.000000\n",
+       "Dragon    Psychic     1.000000\n",
+       "          Ice         1.000000\n",
+       "Rock      Fighting    1.000000\n",
+       "Steel     Fighting    1.000000\n",
+       "Dragon    Fire        1.000000\n",
+       "Psychic   Dark        1.000000\n",
+       "          Fire        1.000000\n",
+       "Name: legendary, dtype: float64"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "legendary_percentages = pokemon.groupby([\"type\", \"subtype\"]).legendary.mean().sort_values() \n",
+    "legendary_percentages[legendary_percentages > 0.5]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "de23775b-8670-4371-913d-d8fa1d1f3a76",
+   "metadata": {},
+   "source": [
+    "#### Your turn\n",
+    "\n",
+    "**1.4.0.** `income` records peoples' annual income, in the following bands. `sleep` records the average hours of sleep someone gets per night. Is there a difference in the average hours of sleep by income level?\n",
+    "\n",
+    "| number | annual income, in $1000   | \n",
+    "| ------ | ----------- |\n",
+    "| 1      | Less than 10       |\n",
+    "| 2      | 10-15        |\n",
+    "| 3      | 15-20         |\n",
+    "| 4      | 20-25        |\n",
+    "| 5      | 25-35        |\n",
+    "| 6      | 35-50        |\n",
+    "| 7      | 50-75        |\n",
+    "| 8      | More than 75   |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "75c1ac4f-3914-4c0a-a156-2e084002df66",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(7.068553402433529)"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "people.sleep.mean()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f6413f2b-26a0-4b70-976f-90e45558c4bb",
+   "metadata": {},
+   "source": [
+    "**1.4.0.** Is there a difference in peoples' income or general health, by sex and education level? "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "d46df8a1-bbc2-45a4-9be1-cee1858cbf21",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# YOUR CODE HERE"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "931d602b-ddf4-4c8b-80e0-f886267cce76",
+   "metadata": {},
+   "source": [
+    "### 1.5. Plotting \n",
+    "\n",
+    "Pandas has excellent built-in plotting capabilities, but \n",
+    "we are going to use the [seaborn](https://seaborn.pydata.org/) library because it's a bit \n",
+    "more intuitive and produces more beautiful plots. `set_theme`, called here without any arguments, assigns the default color palette. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "b1e06e4f-6b9e-42af-a27c-dbb525a259ce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import seaborn as sns\n",
+    "sns.set_theme()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a15ad672-13e8-4bdd-bc31-a489a1730daf",
+   "metadata": {},
+   "source": [
+    "#### Demo\n",
+    "\n",
+    "**When you want to visualize the distribution of a series**, a [histogram](https://seaborn.pydata.org/generated/seaborn.histplot.html) puts data into bins and plots the number of data points in each bin.\n",
+    "\n",
+    "Let's see the distribution of pokémon attack values. Note how assigning `x=\"attack\"` spreads attack values over the x-axis, while `y=\"attack\"` spreads attack values over the y-axis. The number of bins is selected automatically, but you can specify this with the optional `bins` argument. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "5ce066fe-f81d-4b78-a394-c5c2f4dc9f46",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='legendary', ylabel='Count'>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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VbbfdFsTK/o/ValGHDnEBmTvCalVkZERA5m6pph9Smy02yJW0XGtYQ7ig1+agz+agz+YIZp9DKhSdTkFBgZ566il99tlnSkhIkHTy5fZJSUmeMU6nU5I85202mw4cOOAzl8Ph8IxpLrfbkNN5vEVz/LeoqAjFx7eRy+1WY6PLr3P7i8vlliQ5nXWez8NNRIRVNltsWK8hXNBrc9Bnc9BncwSyzzZb7BldgQr5UHSq1NRUSSf3DDV93vQ4KipKycnJnnEVFRUyDMPrdlRlZaXS09NbXEdjo3+/WZ5vlCEZhuHXuf2lqS6Xy+339ZutNawhXNBrc9Bnc9BncwSzzyF/g7S0tFQRERE6//zzlZycrO7du6usrMxnzMCBAxUdHS1Jys/Pl8PhUEVFhWdMZWWlduzYofz8fFPrBwAA4SGkrhT95je/UU5OjjIyMiRJ//jHP/TSSy9p3LhxnttlkyZN0tSpU9WtWzfl5OSotLRU27Zt0/PPP++ZJzs7W3l5eZoxY4amTZummJgYLVy4UBkZGbr88suDsjYAABDaQioUpaSkaM2aNTpw4IDcbre6d++uGTNm6Prrr/eMKSoqUl1dnUpKSrRixQqlpKRo6dKlys7O9ppr0aJFmjdvnoqLi9XY2Ki8vDzNnDmTd7MGAACnZTFCdRNLiHK53Dp6tNavc8bERMpmi9Wjqz7U/kPVfp3bX85NitfkMX117Fht2N5Tj4y0qkOHuLBeQ7ig1+agz+agz+YIZJ8TE+POaKN1yO8pAgAAMAOhCAAAQIQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASS0IRePGjVNFRcV3nn/33Xc1bty45k4PAABgqmaHovfee09ff/31d54/evSo3n///eZODwAAYKoW3T6zWCzfeW7v3r2Ki4tryfQAAACmiTybwa+88opeeeUVz+Ply5frpZde8hlXXV2tzz//XPn5+S2vEAAAwARnFYrq6up07Ngxz+Pa2lpZrb4Xm9q2bavrrrtOt99+e8srBAAAMMFZhaIxY8ZozJgxkqShQ4fq3nvv1aWXXhqQwgAAAMx0VqHoVG+++aY/6wAAAAiqZoeiJjU1Nfrqq6/kdDplGIbP+YsuuqilXwIAACDgmh2Kjh49qrlz52rDhg1yuVw+5w3DkMVi0WeffdaiAgEAAMzQ7FBUXFyst956S9dff7369esnm83mz7oAAABM1exQtHnzZt1www265557/FmPR21trQoKCnTw4EG9/PLLyszM9JxbvXq1nnzySX311VdKSUnRlClTNGTIEK/nV1dXa968edq4caMaGho0aNAgzZw5U506dQpIvQAAILw1+80b27Rpo3PPPdeftXh57LHHTntbbt26dZo1a5YKCgpUUlKirKwsTZw4UR9//LHXuMmTJ2vz5s2aPXu2Hn74YVVWVmrChAlqbGwMWM0AACB8NTsUjRgxQhs3bvRnLR5ffPGF/vznP2vSpEk+5xYvXqzhw4dr8uTJGjBggO6//35lZmZq2bJlnjEfffSRNm3apAcffFCFhYW69NJL9eijj+rzzz/Xhg0bAlIzAAAIb80ORcOGDZPD4dBvfvMbbdiwQdu2bdP27dt9Pppj7ty5uu6665SSkuJ1vKqqSnv27FFBQYHX8cLCQlVUVKi+vl6SVF5eLpvNptzcXM+Y1NRU9ezZU+Xl5c2qCQAAtG7N3lPU9CaOkrRlyxaf88199VlZWZl27typJUuW+IQqu90uST5hKS0tTQ0NDaqqqlJaWprsdrtSUlJ8/jZbamqqZw4AAIBTNTsUzZs3z591SDr5Z0Tmz5+vKVOmKD4+3ue8w+GQJJ9XujU9bjrvdDrVrl07n+cnJCTo008/bXGdkZEt+ju6PqzW/4Q3y/f/kd1gaqorIsK/azdTU+3hvIZwQa/NQZ/NQZ/NEQp9bnYouuqqq/xZh6STf2C2Y8eO+sUvfuH3uf3FarWoQ4e4gMwdYbUqMjIiIHO3VNMPqc0WG+RKWq41rCFc0Gtz0Gdz0GdzBLPPLX5Ha3/58ssv9dRTT2nZsmWqrq6WJB0/ftzz39raWiUkJEg6+XL7pKQkz3OdTqckec7bbDYdOHDA52s4HA7PmOZyuw05ncdbNMd/i4qKUHx8G7ncbjU2+r7iLhS4XG5JktNZ5/k83EREWGWzxYb1GsIFvTYHfTYHfTZHIPtss8We0RWoZoeiP/zhDz84xmKx6I9//OMZzbd//341NDTolltu8Tk3btw49enTRwsWLJB0cm9Ramqq57zdbldUVJSSk5Mlndw7VFFR4dnX1KSyslLp6elnVM/3aWz07zfL840ydNo/lRIKmupyudx+X7/ZWsMawgW9Ngd9Ngd9Nkcw+9zsULR161afY263W4cPH5bL5VJiYqJiY8/8EljPnj313HPPeR377LPPNG/ePM2ZM0eZmZlKTk5W9+7dVVZWpssuu8wzrrS0VAMHDlR0dLQkKT8/X4899pgqKip08cUXSzoZiHbs2KGbb765OcsFAACtXLND0Ztvvnna4w0NDfrLX/6iZ599Vk899dQZz2ez2ZSTk3Pac7169VKvXr0kSZMmTdLUqVPVrVs35eTkqLS0VNu2bdPzzz/vGZ+dna28vDzNmDFD06ZNU0xMjBYuXKiMjAxdfvnlZ7FKAADwY+H3PUVRUVEaO3asdu/erQceeEArVqzw6/xFRUWqq6tTSUmJVqxYoZSUFC1dulTZ2dle4xYtWqR58+apuLhYjY2NysvL08yZMxUZGTLbqAAAQAgJWELo0aOHXnvttRbNkZOTo88//9zn+DXXXKNrrrnme5/brl07/fGPfzzjPU0AAODHLWBvBrBly5az2lMEAAAQTM2+UrR06dLTHq+urtb777+vHTt2nPaVZAAAAKHI76EoISFBycnJmjNnjn75y182uzAAAAAzNTsU/fvf//ZnHQAAAEHFH3IBAACQH1599t577+ntt9/WV199JUnq0qWLBg8erP79+7e4OAAAALM0OxTV19fr7rvv1saNG2UYhucv1TudTj399NP6+c9/rgULFigqKspvxQIAAARKs2+fLVu2TG+88YbGjx+vTZs26b333tN7772nzZs366abbtKGDRu0bNkyf9YKAAAQMM0ORX/729901VVX6Z577tH//M//eI537NhRv//97zVq1CitXbvWL0UCAAAEWrND0eHDh9W7d+/vPN+7d28dPny4udMDAACYqtmh6JxzztF77733nefff/99nXPOOc2dHgAAwFTNDkWjRo3S+vXrVVxcLLvdLpfLJbfbLbvdrvvuu09lZWW66qqr/FkrAABAwDT71We33Xabqqqq9NJLL2n16tWyWk/mK7fbLcMwdNVVV+m2227zW6EAAACB1OxQFBERofnz5+vGG29UeXm5vvzyS0nSueeeq/z8fPXo0cNvRQIAAATaWYWiEydO6MEHH9TPfvYzXX/99ZKkHj16+ASg5557Ti+++KLuvfde3qcIAACEhbPaU/SXv/xFr7zyigYPHvy94wYPHqw1a9Zo9erVLakNAADANGcVitavX6/LL79cycnJ3zuuW7duuuKKK7Ru3boWFQcAAGCWswpFO3fuVN++fc9obHZ2tj7//PNmFQUAAGC2swpFDQ0NZ7xHKCoqSvX19c0qCgAAwGxnFYo6deqkXbt2ndHYXbt2qVOnTs0qCgAAwGxnFYouvvhivfbaazpy5Mj3jjty5Ihee+01XXzxxS0qDgAAwCxnFYomTJigEydO6IYbbtAnn3xy2jGffPKJbrzxRp04cUI333yzX4oEAAAItLN6n6Lk5GQtWrRId911l6677jolJycrPT1dcXFxqq2t1a5du7Rv3z61adNGjzzyiLp16xaougEAAPzqrN/RevDgwVq7dq1KSkr09ttva+PGjZ5znTp10jXXXKMJEyb84Mv2AQAAQkmz/sxH165dNWfOHElSTU2NamtrFRcXp/j4eL8WBwAAYJZm/+2zJvHx8YQhAAAQ9s5qozUAAEBrRSgCAAAQoQgAAEASoQgAAEASoQgAAEASoQgAAEASoQgAAEASoQgAAEASoQgAAEBSiIWid955R2PHjtWAAQN0wQUX6NJLL9W8efNUXV3tNe7NN9/UiBEjlJmZqWHDhmnNmjU+c9XX1+tPf/qTcnNzlZWVpfHjx8tut5u1FAAAEGZCKhR988036t27t+bMmaOVK1dq/PjxevXVV3XnnXd6xnzwwQeaOHGisrKyVFJSooKCAt17770qKyvzmmvu3LlavXq1pkyZoiVLlqi+vl433nijT8ACAACQ/PC3z/xp5MiRXo9zcnIUHR2tWbNm6eDBg+rcubOWL1+u3r176/7775ckDRgwQFVVVVq8eLGuuOIKSdKBAwf08ssv67777tPo0aMlSZmZmRoyZIhefPFFTZgwwdyFAQCAkBdSV4pOp3379pKkhoYG1dfXa+vWrZ7w06SwsFBffPGF9u/fL0natGmT3G6317j27dsrNzdX5eXlptUOAADCR0iGIpfLpRMnTmj79u1atmyZhg4dqq5du2rfvn1qaGhQamqq1/i0tDRJ8uwZstvt6tixoxISEnzGsa8IAACcTkjdPmsyZMgQHTx4UJI0aNAgLViwQJLkcDgkSTabzWt80+Om806nU+3atfOZ12azeca0RGSkf7Ok1Wo5+YlFslgsfp3bX5rqiogIyRx9RppqD+c1hAt6bQ76bA76bI5Q6HNIhqIVK1aorq5Ou3fv1vLly3Xbbbfp6aefDnZZkk4GmA4d4gIyd4TVqsjIiIDM3VJNP6Q2W2yQK2m51rCGcEGvzUGfzUGfzRHMPodkKOrRo4ckKTs7W5mZmRo5cqTeeOMNnXfeeZLk8woyp9MpSZ7bZTabTTU1NT7zOp1On1tqZ8vtNuR0Hm/RHP8tKipC8fFt5HK71djo8uvc/uJyuSVJTmed5/NwExFhlc0WG9ZrCBf02hz02Rz02RyB7LPNFntGV6BCMhSdKiMjQ1FRUdq3b5+GDh2qqKgo2e12DRo0yDOmaZ9Q016j1NRUff3113I4HF4hyG63++xHao7GRv9+szzfKEMyDMOvc/tLU10ul9vv6zdba1hDuKDX5qDP5qDP5ghmn0P+Buknn3yihoYGde3aVdHR0crJydHf//53rzGlpaVKS0tT165dJUl5eXmyWq3asGGDZ4zD4dCmTZuUn59vav0AACA8hNSVookTJ+qCCy5QRkaG2rRpo3//+99auXKlMjIydNlll0mSfvvb32rcuHGaPXu2CgoKtHXrVr3++utauHChZ55zzjlHo0eP1kMPPSSr1arOnTvriSeeULt27XTdddcFa3kAACCEhVQo6t27t0pLS7VixQoZhqFzzz1X11xzjX7zm98oOjpaktSvXz8tWbJEixYt0ssvv6wuXbpo7ty5Kigo8Jpr5syZiouL04IFC1RbW6sLL7xQTz/99GlflQYAAGAxQnUTS4hyudw6erTWr3PGxETKZovVo6s+1P5DoflnSM5NitfkMX117Fht2N5Tj4y0qkOHuLBeQ7ig1+agz+agz+YIZJ8TE+POaKN1yO8pAgAAMAOhCAAAQIQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASSEWitavX6/f/va3ys/PV1ZWlkaOHKmXX35ZhmF4jVu9erWGDRumzMxMjRgxQm+99ZbPXNXV1ZoxY4b69++v7Oxs3XHHHTp06JBZSwEAAGEmpELRM888o9jYWE2fPl3Lly9Xfn6+Zs2apWXLlnnGrFu3TrNmzVJBQYFKSkqUlZWliRMn6uOPP/aaa/Lkydq8ebNmz56thx9+WJWVlZowYYIaGxtNXhUAAAgHkcEu4FTLly9XYmKi5/HAgQP1zTff6Omnn9bvfvc7Wa1WLV68WMOHD9fkyZMlSQMGDNDOnTu1bNkylZSUSJI++ugjbdq0SStXrlReXp4kKSUlRYWFhdqwYYMKCwtNXxsAAAhtIXWl6NRA1KRnz56qqanR8ePHVVVVpT179qigoMBrTGFhoSoqKlRfXy9JKi8vl81mU25urmdMamqqevbsqfLy8sAuAgAAhKWQulJ0Ov/617/UuXNnxcfH61//+pekk1d9TpWWlqaGhgZVVVUpLS1NdrtdKSkpslgsXuNSU1Nlt9tbXFNkpH+zpNX6nzot8qk5VDTVFRERUjn6rDTVHs5rCBf02hz02Rz02Ryh0OeQDkUffPCBSktLNW3aNEmSw+GQJNlsNq9xTY+bzjudTrVr185nvoSEBH366actqslqtahDh7gWzfFdIqxWRUZGBGTulmr6IbXZYoNcScu1hjWEC3ptDvpsDvpsjmD2OWRD0YEDBzRlyhTl5ORo3LhxwS7Hw+025HQe9+ucUVERio9vI5fbrcZGl1/n9heXyy1JcjrrPJ+Hm4gIq2y22LBeQ7ig1+agz+agz+YIZJ9tttgzugIVkqHI6XRqwoQJat++vZYsWSKr9eRCEhISJJ18uX1SUpLX+FPP22w2HThwwGdeh8PhGdMSjY3+/WZ5vlGGfN5+IFQ01eVyuf2+frO1hjWEC3ptDvpsDvpsjmD2OeRukH777be69dZbVV1drSeffNLrNlhqaqok+ewLstvtioqKUnJysmdcZWWlT8CorKz0zAEAAHCqkApFjY2Nmjx5sux2u5588kl17tzZ63xycrK6d++usrIyr+OlpaUaOHCgoqOjJUn5+flyOByqqKjwjKmsrNSOHTuUn58f+IUAAICwE1K3z+bMmaO33npL06dPV01NjdcbMp5//vmKjo7WpEmTNHXqVHXr1k05OTkqLS3Vtm3b9Pzzz3vGZmdnKy8vTzNmzNC0adMUExOjhQsXKiMjQ5dffnkQVgYAAEJdSIWizZs3S5Lmz5/vc+4f//iHunbtqqKiItXV1amkpEQrVqxQSkqKli5dquzsbK/xixYt0rx581RcXKzGxkbl5eVp5syZiowMqSUDAIAQYTFCdWdviHK53Dp6tNavc8bERMpmi9Wjqz7U/kPVfp3bX85NitfkMX117Fht2G40jIy0qkOHuLBeQ7ig1+agz+agz+YIZJ8TE+PO6NVnIbWnCAAAIFgIRQAAACIUAQAASCIUAQAASCIUAQAASCIUAQAASCIUAQAASCIUAQAASCIUAQAASCIUAQAASCIUAQAASCIUAQAASCIUAQAASCIUAQAASCIUAQAASCIUAQAASCIUAQAASCIUAQAASCIUAQAASCIUAQAASCIUAQAASCIUAQAASCIUAQAASCIUAQAASCIUAQAASCIUAQAASCIUAQAASCIUAQAASCIUAQAASCIUAQAASCIUAQAASCIUAQAASCIUAQAASCIUAQAASCIUAQAASAqxULR3714VFxdr5MiROv/881VUVHTacatXr9awYcOUmZmpESNG6K233vIZU11drRkzZqh///7Kzs7WHXfcoUOHDgV6CQAAIEyFVCjatWuX3nnnHf30pz9VWlraacesW7dOs2bNUkFBgUpKSpSVlaWJEyfq448/9ho3efJkbd68WbNnz9bDDz+syspKTZgwQY2NjSasBAAAhJvIYBdwqqFDh+qyyy6TJE2fPl2ffvqpz5jFixdr+PDhmjx5siRpwIAB2rlzp5YtW6aSkhJJ0kcffaRNmzZp5cqVysvLkySlpKSosLBQGzZsUGFhoTkLAgAAYSOkrhRZrd9fTlVVlfbs2aOCggKv44WFhaqoqFB9fb0kqby8XDabTbm5uZ4xqamp6tmzp8rLy/1fOAAACHshdaXoh9jtdkknr/qcKi0tTQ0NDaqqqlJaWprsdrtSUlJksVi8xqWmpnrmaInISP9mSav1P3Va5FNzqGiqKyIipHL0WWmqPZzXEC7otTnosznoszlCoc9hFYocDockyWazeR1vetx03ul0ql27dj7PT0hIOO0tubNhtVrUoUNci+b4LhFWqyIjIwIyd0s1/ZDabLFBrqTlWsMawgW9Ngd9Ngd9Nkcw+xxWoSgUuN2GnM7jfp0zKipC8fFt5HK71djo8uvc/uJyuSVJTmed5/NwExFhlc0WG9ZrCBf02hz02Rz02RyB7LPNFntGV6DCKhQlJCRIOvly+6SkJM9xp9Ppdd5ms+nAgQM+z3c4HJ4xLdHY6N9vlucbZUiGYfh1bn9pqsvlcvt9/WZrDWsIF/TaHPTZHPTZHMHsc1jdIE1NTZUkn31BdrtdUVFRSk5O9oyrrKz0CRiVlZWeOQAAAE4VVqEoOTlZ3bt3V1lZmdfx0tJSDRw4UNHR0ZKk/Px8ORwOVVRUeMZUVlZqx44dys/PN7VmAAAQHkLq9lldXZ3eeecdSdKXX36pmpoaTwDq37+/EhMTNWnSJE2dOlXdunVTTk6OSktLtW3bNj3//POeebKzs5WXl6cZM2Zo2rRpiomJ0cKFC5WRkaHLL788KGsDAAChLaRC0ZEjR3TnnXd6HWt6/NxzzyknJ0dFRUWqq6tTSUmJVqxYoZSUFC1dulTZ2dlez1u0aJHmzZun4uJiNTY2Ki8vTzNnzlRkZEgtGQAAhAiLEao7e0OUy+XW0aO1fp0zJiZSNlusHl31ofYfqvbr3P5yblK8Jo/pq2PHasN2o2FkpFUdOsSF9RrCBb02B302B302RyD7nJgYd0avPgurPUUAAACBQigCAAAQoQgAAEASoQgAAEASoQgAAEASoQgAAEASoQgAAEASoQgAAEBSiL2jNQAACAyr1SKr1RLsMr7Tmby5YqARigAAaOWsVovad2irCGvwg8f3cbsNWSzBC26EIgAAWjmr1aIIq1V/LvtMh44eD3Y5p9W5Y5x+NaxHUK9mEYoAAPiROHT0uL48XBPsMk4rmFeImoT2dTQAAACTEIoAAABEKAIAAJBEKAIAAJBEKAIAAJBEKAIAAJBEKAIAAJBEKAIAAJBEKAIAAJBEKAIAAJBEKAIAAJBEKAIAAJBEKAIAAJBEKAIAAJBEKAIAAJBEKAIAAJBEKAIAAJBEKAIAAJBEKAIAAJBEKAIAAJBEKAIAAJBEKAIAAJBEKAIAAJDUykPRF198ofHjxysrK0u5ubl66KGHVF9fH+yyAABACIoMdgGB4nA4dMMNN6h79+5asmSJDh48qPnz5+vbb79VcXFxsMsDAAAhptWGohdffFG1tbVaunSp2rdvL0lyuVyaM2eObr31VnXu3Dm4BQIAgJDSam+flZeXa+DAgZ5AJEkFBQVyu93avHlz8AoDAAAhqdVeKbLb7frFL37hdcxmsykpKUl2u73Z81qtFiUmxrW0PC8Wy8n//mbkBXK5Db/O7S8R1pNFJiTEBrmSlmsNawgX9Noc9NkcraHPN4/KDPnfM/HxMYqLi/Hr3Nb/zP1DWm0ocjqdstlsPscTEhLkcDiaPa/FYlFExJk192zFt40OyLz+ZLWG/8XF1rCGcEGvzUGfzdEa+szvmR/42kH7ygAAACGk1YYim82m6upqn+MOh0MJCQlBqAgAAISyVhuKUlNTffYOVVdX6/Dhw0pNTQ1SVQAAIFS12lCUn5+vLVu2yOl0eo6VlZXJarUqNzc3iJUBAIBQZDEMIzS3obeQw+HQ8OHDlZKSoltvvdXz5o1XXnklb94IAAB8tNpQJJ38Mx8PPPCAPvroI8XFxWnkyJGaMmWKoqNDf/c9AAAwV6sORQAAAGeq1e4pAgAAOBuEIgAAABGKAAAAJBGKAAAAJBGKAAAAJBGKAAAAJBGKAu6LL77Q+PHjlZWVpdzcXD300EOqr6//wecZhqEVK1Zo8ODB6t27t6699lp9/PHHgS84TDWnz4cOHdJDDz2kkSNHKjs7W/n5+br77rv15ZdfmlR1eGruz/SpnnnmGWVkZOjWW28NUJXhryV9PnjwoKZNm6YBAwaod+/eKigo0Nq1awNccXhqbp+PHTum4uJiDR48WFlZWSoqKtKqVatMqDg87d27V8XFxRo5cqTOP/98FRUVndHzzP5dGBmwmSGHw6EbbrhB3bt315IlSzzvqv3tt9/+4Ltql5SUaPHixZo6daoyMjL0wgsv6KabbtJrr72m5ORkk1YQHprb5+3bt+uNN97QL37xC/Xp00fHjh3T8uXLdc011+j1119XYmKiiasIDy35mW5y+PBhLVu2TB07dgxwteGrJX0+dOiQrr32WqWkpOiBBx5QfHy8du3addbB9cegJX2+8847Zbfbddddd+knP/mJysvLNXv2bEVEROiXv/ylSSsIH7t27dI777yjPn36yO1260zfItH034UGAubxxx83srKyjGPHjnmOvfjii0bPnj2NAwcOfOfzvv32W+PCCy80FixY4Dl24sQJY8iQIcZ9990XwIrDU3P77HA4jIaGBq9j/+///T8jIyPDWLlyZaDKDWvN7fWpfv/73xv33HOPMXbsWOOWW24JUKXhrSV9njp1qnHttdcajY2NAa4y/DW3z4cOHTLS09ONNWvWeB3/9a9/bYwbNy5Q5YY1l8vl+XzatGnG8OHDf/A5wfhdyO2zACovL9fAgQPVvn17z7GCggK53W5t3rz5O5/34YcfqqamRgUFBZ5j0dHR+vnPf67y8vJAlhyWmttnm82myEjvi6XnnHOOEhMTdejQoUCVG9aa2+smH3zwgTZu3Ki77747gFWGv+b2uaamRuvXr9eYMWMUERFhQqXhrbl9bmxslCS1a9fO63h8fPwZXwH5sbFazz5uBON3IaEogOx2u1JTU72O2Ww2JSUlyW63f+/zJPk8Ny0tTV999ZW+/fZb/xcbxprb59OprKzUkSNHlJaW5s8SW42W9NrlcumBBx7Qbbfdpk6dOgWyzLDX3D5v375dDQ0NioyM1NixY9WrVy/l5ubqf//3f9XQ0BDossNOc/v8k5/8RHl5eXr88ce1e/du1dTUqLS0VJs3b9avf/3rQJf9oxGM34XsKQogp9Mpm83mczwhIUEOh+N7nxcdHa2YmBiv4zabTYZhyOFwqE2bNn6vN1w1t8//zTAMzZ07V506ddLw4cP9WWKr0ZJe//nPf1ZdXZ1uvPHGAFXXejS3z19//bUkaebMmfrlL3+piRMnatu2bVq8eLGsVitX6P5LS36elyxZoilTpnj+XxEREaGZM2dq2LBhAan1xygYvwsJRcB/LFmyRO+++66efPJJtW3bNtjltCpHjhzR4sWL9ac//UnR0dHBLqfVcrvdkqSLL75Y06dPlyQNGDBAtbW1euqpp3T77bfzDyo/MAxDf/jDH7Rnzx4tWLBASUlJ2rJli/74xz8qISGBf1SFMUJRANlsNlVXV/scdzgcSkhI+N7n1dfX68SJE14J2el0ymKxfO9zf4ya2+dTvfTSS1q2bJkefPBBDRw40N8lthrN7fWjjz6qjIwM9evXT06nU9LJfRmNjY1yOp1q27atz/6uH7OW/L9DOhmETjVw4EA9/vjj2rt3rzIyMvxbbBhrbp/ffvttlZWVae3atZ5+5uTk6MiRI5o/fz6hyE+C8buQPUUBlJqa6nNfurq6WocPH/a5R/rfz5NO7m85ld1uV5cuXfiX3n9pbp+bvPHGG5o9e7buuOMOjR49OlBltgrN7XVlZaXef/99XXTRRZ6PDz/8UJs2bdJFF12kLVu2BLr0sNLcPp933nnfO++JEyf8Ul9r0dw+7969WxEREUpPT/c63rNnTx06dEh1dXUBqffHJhi/CwlFAZSfn68tW7Z4/mUsSWVlZbJarcrNzf3O51144YWKj4/X+vXrPccaGhq0YcMG5efnB7TmcNTcPkvS1q1bddddd+maa67R7bffHuhSw15zez1jxgw999xzXh89evRQVlaWnnvuOfXu3duM8sNGc/t87rnnKj093SdkbtmyRW3atPnB0PRj05I+u1wuff75517Ht2/fro4dOyo2NjZgNf+YBOV3YUBe6A/DMAzjm2++MXJzc42xY8ca//znP42XX37Z6NevnzFnzhyvcePGjTMuu+wyr2NPPPGEccEFFxjPPPOMsWXLFmPSpElGdna2sW/fPjOXEBaa2+fdu3cbffv2NYqKiox//etfxkcffeT52Lt3r9nLCAst+Zn+b7xP0XdrSZ//8Y9/GBkZGcbcuXONTZs2GcuXLzd69eplPPLII2YuISw0t8/V1dXG4MGDjZ///OfGq6++amzZssV46KGHjB49ehjLli0zexlh4fjx48b69euN9evXG2PHjjUuueQSz+MjR44YhhEavwu5iR9ACQkJevbZZ/XAAw/o9ttvV1xcnEaPHq0pU6Z4jXO73XK5XF7HJkyYIMMw9NRTT+no0aPq2bOnVq5cybtZn0Zz+/zJJ5+ourpa1dXV+tWvfuU19qqrrtL8+fNNqT+ctORnGmeuJX0eOnSoHnnkET322GNatWqVOnXqpEmTJumWW24xcwlhobl9jo+P1zPPPKOFCxfq4YcfVnV1tbp27arp06dr7NixZi8jLBw5ckR33nmn17Gmx88995xycnJC4nehxTB4pykAAAD2FAEAAIhQBAAAIIlQBAAAIIlQBAAAIIlQBAAAIIlQBAAAIIlQBAAAIIlQBMBEf/3rX5WRkaH9+/cHu5QWa01rAXASoQgAAECEIgAAAEmEIgAICXV1dcEuAfjRIxQBCKp33nlHY8aMUVZWlrKzs3XLLbdo165dPuPWr1+vwsJCZWZmqqioSG+88YamT5+uoUOHeo1zu9165plnNHz4cGVmZuriiy9WcXGxHA6H17ihQ4fq1ltv1QcffKDRo0crMzNTl156qV599VWfr71r1y6NGzdOvXv3Vn5+vh577DG53W6fcRs3btQtt9yivLw8XXDBBbrsssu0bNkynz9yef3116uoqEiffvqpfv3rX6tPnz565JFHNG3aNOXk5KihocFn7ptuuknDhg07k5YCaKbIYBcA4Mfr1Vdf1fTp05WXl6epU6eqrq5Oq1at0pgxY/TKK6+oa9eukqS3335bU6ZMUXp6uu6++245HA7de++96ty5s8+cxcXFeuWVV3T11Vfr+uuv1/79+/XCCy9ox44dWrVqlaKiojxj9+7dqzvvvFOjR4/WVVddpTVr1mj69Onq1auXfvazn0mSDh8+rHHjxsnlcumWW25RbGysXnrpJcXExPh87VdeeUVt27bV+PHj1bZtW7377rtavHixampqNG3aNK+x33zzjSZMmKDhw4drxIgR6tixo9q2batXX31VmzZt0pAhQzxjDx8+rHfffVe33367X/oO4DsYAGCSNWvWGOnp6UZVVZVRU1Nj9OvXz5g5c6bXmMOHDxt9+/b1Ol5UVGTk5+cbNTU1nmNbt2410tPTjSFDhniOvf/++0Z6erqxdu1arznLy8t9jg8ZMsRIT0833n//fc+xI0eOGBdccIExf/58z7EHH3zQSE9PNz755BOvcX379vWspUldXZ3PmmfNmmX06dPHOHHihOfY2LFjjfT0dGPVqlVeY10ul5Gfn29MnjzZ6/jTTz9tZGRkGPv27fOZH4D/cPsMQFBs2bJFTqdTw4cP19GjRz0fVqtVffr00datWyVJBw8e1M6dOzVq1CjFxcV5nt+/f3+lp6d7zVlWVqZ27dopNzfXa85evXqpbdu2njmbnHfeeerXr5/ncWJiolJSUlRVVeU59s477ygrK0u9e/f2GnfllVf6rKlNmzaez2tqanT06FH169dPdXV1stvtXmOjo6N19dVXex2zWq268sor9eabb6qmpsZzfO3atcrOzlZycvJ3NxRAi3H7DEBQ7NmzR5J0ww03nPZ8fHy8JOmrr76SJHXr1s1nzE9/+lPt2LHD83jv3r2qrq7WwIEDTzvnkSNHvB7/5Cc/8RmTkJDgtf/oq6++Up8+fXzGpaSk+BzbtWuXFi1apHfffdcr1EhSdXW11+POnTsrOjraZ45Ro0appKREGzdu1KhRo2S327V9+3bNmTPntGsC4D+EIgBBYRiGJOmhhx5SUlKSz/mIiIizntPtdqtjx456+OGHT3s+MTGxxV/juzidTo0dO1bx8fG644471K1bN8XExGj79u16+OGHfTZmn3pV6VTnnXeeevXqpbVr12rUqFFau3atoqKiVFBQ4LdaAZweoQhAUDTdCurYsaMuvvji7xzXpUsXSdK+fft8zu3du9frcbdu3VRRUaELL7zwO0PH2erSpYvP15GkyspKr8fvvfeevvnmGy1dulQXXXSR53hz3vF61KhRmj9/vg4dOqTXX39dgwcPVkJCwtkXD+CssKcIQFAMGjRI8fHxeuKJJ077EvSjR49KOnmbKT09Xa+++qpqa2s959977z3t3LnT6zkFBQVyuVx67LHHfOZrbGyU0+k86zovueQSffzxx9q2bZtXbX/729+8xlmtJ/932nQFTJLq6+v15z//+ay/ZlFRkSwWix588EFVVVVpxIgRZz0HgLPHlSIAQREfH6/Zs2frnnvu0dVXX63CwkIlJibqq6++0jvvvKMLL7xQxcXFkqQpU6bod7/7nX71q1/p6quvltPp1AsvvKD09HSvoNS/f39de+21euKJJ/TZZ58pNzdXUVFR2rNnj8rKynTvvffqiiuuOKs6b775Zr322mu6+eabNW7cOM9L8rt06aLPP//cMy47O1sJCQmaPn26rr/+elksFr322mteIelMJSYmatCgQSorK5PNZtPgwYPPeg4AZ48rRQCC5sorr9QzzzyjTp06aeXKlXrwwQdVWlqqnj17er0ya+jQoXrkkUfU0NCgBQsW6I033tC8efOUkpLi835B999/vx544AEdOXJECxcu1IIFC/Tuu+9qxIgRuvDCC8+6xk6dOum5555TRkaGVqxYoWeffVYjR47UuHHjvMZ16NBBjz/+uJKSkrRo0SKtXLlSF198sX7/+983qzcjR46UdPLq1+k2ZAPwP4vRnH/GAEAIGDlypBITE/X0008HuxS/27hxo26//Xa98MILXm8bACBwuFIEIOQ1NDSosbHR69jWrVv173//W/379w9SVYG1evVqJScnq2/fvsEuBfjRYE8RgJB38OBBjR8/XiNGjFCnTp1kt9v14osvKikpSdddd12wy/OrdevW6fPPP9fbb7+te++9VxaLJdglAT8ahCIAIS8hIUG9evXS6tWrdfToUbVt21aXXHKJpk6dqg4dOgS7PL+666671LZtW40ePVpjxowJdjnAjwp7igAAAMSeIgAAAEmEIgAAAEmEIgAAAEmEIgAAAEmEIgAAAEmEIgAAAEmEIgAAAEmEIgAAAEmEIgAAAEnS/wcZcr5zMTl01QAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.histplot(data=pokemon, x=\"legendary\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "bceb253b-ef4f-4aa2-aef4-cab2b3ca6d59",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Count', ylabel='attack'>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.histplot(data=pokemon, y=\"attack\", bins=5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2aac9186-86c0-41db-a1c4-8719bb78b46b",
+   "metadata": {},
+   "source": [
+    "**When you want to compare the distribution of a numeric variable across categories**, a [barplot](https://seaborn.pydata.org/generated/seaborn.barplot.html) is a good choice. Choose one numeric column and one categorical column. \n",
+    "\n",
+    "Let's see pokémon hit points by legendary/non-legendary. `errorbar=\"sd\"` shows the standard deviation for each category. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "92be1ad0-12bb-49f0-a3f6-85fcfd98e943",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='legendary', ylabel='hp'>"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.barplot(data=pokemon, x=\"legendary\", y=\"hp\", errorbar=\"sd\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4f75e1fa-a5d7-4d2c-a458-8190a7cd700e",
+   "metadata": {},
+   "source": [
+    "Here, we use a barplot to show average hit points by type. `errorbar=None` removes the standard deviation bars, because they clutter up the plot with too much detail. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "17f1c289-5990-4420-bfcb-e50eee0b8af6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='hp', ylabel='type'>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.barplot(data=pokemon, x=\"hp\", y=\"type\", hue=\"type\", errorbar=None, palette=\"muted\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "213d6139-203f-4d81-a4b1-6f98cb184662",
+   "metadata": {},
+   "source": [
+    "**When you want to show how many observations are the intersection of multiple categories,** a [countplot](https://seaborn.pydata.org/generated/seaborn.countplot.html) is a good choice. \n",
+    "\n",
+    "To demonstrate this, let's convert the numeric variable `speed` into a categorical variable, `speed_category`, using the built-in function [cut](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.cut.html). "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "3c8e9f47-9aea-4bf0-a628-7aa1a66a8eee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bins = [0, 50, 100, 200]\n",
+    "labels = [\"slow\", \"medium\", \"fast\"]\n",
+    "pokemon[\"speed_category\"] = pd.cut(pokemon.speed, bins=bins, labels=labels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "22f78bec-3d18-4133-ba9f-6595d7181ded",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='speed_category', ylabel='count'>"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(data=pokemon, x=\"speed_category\", hue=\"legendary\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fd508c13-9900-4be1-958f-4f9e9e9b633a",
+   "metadata": {},
+   "source": [
+    "**When you want to show the relationship between two numeric variables**, a [scatterplot](https://seaborn.pydata.org/generated/seaborn.scatterplot.html) is a good choice. \n",
+    "\n",
+    "Here, we plot pokémon hit points against their speed. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "444d9832-bd57-4238-9ea4-5ee898847170",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='hp', ylabel='speed'>"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.scatterplot(data=pokemon, x=\"hp\", y=\"speed\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03e81709-393d-4c41-bce5-2dffc9cf5553",
+   "metadata": {},
+   "source": [
+    "You can distinguish between categories within a scatter plot by assigning a categorical variable to `hue`. We set the marker size with `s` and their opacity with `alpha`. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "86f9747b-00a3-407f-9b73-0bce40bac50d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='hp', ylabel='speed'>"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.scatterplot(data=pokemon, x=\"hp\", y=\"speed\", hue=\"legendary\", alpha=0.5, s=60)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f3741251-2a2b-437e-b68f-084fb4399e9f",
+   "metadata": {},
+   "source": [
+    "Finally, if you want scatter plots across multiple categories, a [relplot](https://seaborn.pydata.org/generated/seaborn.relplot.html) lets you distribute categories across rows and colums in a grid. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "7385237c-6a5c-4041-af46-559d6d84d1fa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x131a01e80>"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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siSsZxheP19hkObcV4dgSE8eWuJJ5fBxbw7jGUt1jxkUd/SPXaDTqz2TDsSWuZB4fx5aYODaqjmQ+t8k8tmQfH8eWmDi2xJXs46tPyXxuObbExLElrmQeH8dGVDsYuCAiIiIiIiIiIiIiorjBwAUREREREREREREREcUNBi6IiIiIiIiIiIiIiChuMHBBRERERERERERERERxg4ELIiIiIiIiIiIiIiKKGwxcEBERERERERERERFR3GDggoiIiIiIiIiIiIiI4gYDF0REREREREREREREFDcYuCCKExpNyQcRERERERERERFRQ6av7wMgasj0ei0MRj3MZgP0upI4otcXgNfrh9fjQyAQqu9DJCIiIiIiIiIiIqpTDFwQ1ROL1QijUY8/Fu/Aj7M2Y+c+OzQaDdq2SMP44e0wuGdzuFxeuF2++j5UIiIiIiIiIiIiojrDwAVRPbDaTNiT78KDb89Bgd1T5rVVmw+qj+aNbHjs2qGwWo1wOb31dqxEREREREREREREdYk9LojqmJSFKnL6cM/rM48KWpS2+4ADd746AyFoVGYGERERERERERERUUPAwAVRHdMbdPjg+5VwuP1Vbru/wI3Pf1sLnUFXJ8dGREREREREREREVN8YuCCqQ0ajDj5/EPNW5kW8z28LtsNk1EN3qHk3ERERERERERERUTLjTChRHdLrdVi0di8CwVDE+zhcPmzcUQCDgf9ciYiIiIiIiIiIKPlxJpSoDmk0GhWIiJbLI2WlNLVyTERERERERERERETxhIELojoUCoWQmWqOer80m1HtS0RERERERERERJTsGLggqkNerx8DumbDbIy82XaTTAtyW6TB5wvU6rERERERERERERERxQMGLojqkN8fhD8QwOj+rSPe55QhuXC5fAhG0ReDiIiIiIiIiIiIKFExcEFUxwK+AC47tRuaN7JVuW2nnEycMbI9fF5mWxAREREREREREVHDwMAFUR3zePwIBgJ47sYR6NY2q8LtBnVvhieuHQqX2wu/n4ELIiIiIiIiIiIiahj09X0ARA2R2+WD2WzAY9cMxc69dkyauRk799mh0QBtW6TjtBHtkJVmhtvlVYEOIiIiIiIiIiIiooaCgQuieuJ2++Dx+NAo1YTLx3WDTquBdLEIBoLqo7DAWd+HSERERERERERERFTnGLggqkehUEkAg4iIiIiIiIiIiIjisMfFW2+9hYsvvrjC1++77z6MGTOmzOeCwSBeeeUVjBgxAn369MFVV12F7du318HREhERERERERERERFR0gYuJkyYgJdeeqnC13/77Td8+eWXR33+9ddfxyeffIJHH30Un332mQpkXHnllfB6vbV8xERERERERERERERElHSBiz179uCaa67Bc889h9zc3HK32bt3L+6//34MGjSozOclOPH+++/jhhtuwOjRo9GlSxe8+OKLyMvLw+TJk+toBERERERERERERERElDSBi5UrV8JgMOC7775D7969j3o9FArhrrvuwumnn35U4GLNmjVwOBwYMmTI4c+lpaWhW7duWLBgQZ0cPxERERERERERERERJVFzbulZcWTfitI++OAD7Nu3D2+++abqgVGaZFaI5s2bl/l806ZND79WXXp97GI6Op22zJ/JhGNLXMk8Po4tMXFsDUusrrPJfG6TeWzJPj6OLTFxbIkr2ccXLT7LRoZjS0wcW+JK5vFxbERJGriojGRUvPrqq6r/hdFoPOp1l8ul/jzyNZPJhMLCwmq/r1arQWamDbGWlmZBsuLYElcyj49jS0wcW/KrjetsMp/bZB5bso+PY0tMHFviSvbxRYLPstHj2BITx5a4knl8HBtRAwlceDwe3Hbbbbj22mtV74rymM3mw70uwn8P72uxVP8fVDAYQlGRE7EiUUn5B15U5EIgEEQy4dgSVzKPj2NLTBxb/IvVREgsr7PJcm4b2tiSfXwcW2Li2BJXMowvHq+xyXJuK8KxJSaOLXEl8/g4tvhWGwF9auCBi2XLlmH9+vUq4+K1115Tn/P5fPD7/ejbty/eeeedwyWipHl3Tk7O4X3lvzt37lyj9/f7Y/+PUf6B18bXjQccW+JK5vFxbImJY2sYYn0ekvncJvPYkn18HFti4tgSV7KPL1J8lo0Ox5aYOLbElczj49iIGkjgolevXpg8eXKZz3388cfqc/JndnY2tFotUlJSMG/evMOBi6KiIqxatQoXXXRRPR05ERERERERERERERElXeBCSj+1adOmzOfS09Oh1+vLfF4CFM899xyysrLQsmVLPPvss2jWrBlOPPHEejhqIiIiIiIiIiIiIiJKysBFpG644QZVPuq+++6D2+3GwIED8d5778FgMNT3oRERERERERERERERUSIHLp566qlKX//Xv/6lPkrT6XS4/fbb1QcRERERERERERERESU2bX0fABERERERERERERERURgDF0REREREREREREREFDcYuCAiIiIiIiIiIiIiorjBwAUREREREREREREREcUNBi6IiIiIiIiIiIiIiChuMHBBRERERERERERERERxg4ELIiIiIiIiIiIiIiKKGwxcEBERERERERERERFR3GDggoiIiIiIiIiIiIiI4gYDF0REREREREREREREFDcYuCAiIiIiIiIiIiIiorjBwAUREREREREREREREcUNBi6IiIiIiIiIiIiIiChuMHBBRERERERERERERERxg4ELIiIiIiIiIiIiIiKKGwxcEBERERERERERERFR3GDggoiIiIiIiIiIiIiI4gYDF0REREREREREREREFDcYuCAiIiIiIiIiIiIiorjBwAURJR2NRlPfh0BERERERERERETVpK/vAyAiigWjUQezATBaLIcDF2k2PVzeEDweP4LBUH0fIhEREREREREREUWAgQsiSmharUYFKDQBL4oW/Ig9f/4Bv/0gNDoDzG26IX3QacjK6YZih1cFMIiIiIiIiIiIiCi+MXBBRAlLMivSU/RwrZ+PfZNeA4J/BSZCwQBcGxarD3Prbsg+7x5oNHq43QxeEBFRzen1Wuj1OkiSn2T1+XwBZvcRERERERHFCHtcEFHCSrFo4dnyJ/Z9+0qZoMWR3NtXIe+zR2GzGqDTsf8FERFVn8mkh9Vmgi3FjO37HFi7vRD7ijzIyLTCYjWqgAYRERERERHVDDMuiChhS0SZLCZsn/y+5FdUub1nx1o41s6DuU0/OJzMuiAiouhJYMIfDGHCT6vx+6IdcJUqQZidZcUpQ3Jx2sh28Hp89XqcREREREREiY5LwogoIZlNejg3L4e/aF/E+xTN/0HtR0REFC2zxYhCpw//ev4P/Dh7S5mghdhz0IkPfliF+96cDYPRUG/HSURERERElAwYuCCihKTXBFRvi2h4dq4FggHodPzVR0REkZPyT2azHve9OQsFxZ5Kt121+SBe+mwJvL5AnR0fERERERFRsuHsHRElJOlUEfRWPnlUnqDfpxqpEhERRcpg0KvSUPsL3BFtP2vZTthdPhgMulo/NiIiIiIiomTEwAURJaQgAJ01NbqdNFpojWaEqm6JQUREdJjZYsAPszZHvH0wBEyasUkaMtXqcRERERERESUrPk0RUULyBXVI6TUmqn2snQYiGAggEJCwBxERUdW0Wg30Oi227C6Kar/NuwqZ4UdERERERFRNDFwQUULyeHwwZLWAqXn7iPdJP+Y0uH21elhERJSsqpWux8gFERERERFRdTBwQUQJO3/kcvvQ5LQboTFZq9w+te8JKsjhdvvr5PiIiCg5BIMhBIIhtGiSEtV+LZukIMTahERERERERNXCwAURJSyny4+QNQstLn0Shqzm5W+k0yN9yJlodOKVKLL7OIlERERRc7m8GDu0bVT7nDayPTS85hAREREREVWLvnq7ERHFhyKHHzZLFlr982W4d6xF8ZJf4S86AI3eAEtuT6T2PREhjRaFxR74/extQURE0fN5AzhhUA4+nbwGxc6qaw7269wUWWkm2IvddXJ8RERE8UKv18Jg0CNUqlpiKBCEx8PMdyIiig4DF0SU8ByuAJxuF8xZ7ZB5wpXQ6LTQajTwBUIodofg87GxBRERVZ/fH1AfD181BPe+ORuuSiZfcpql4s5LBqiG3kRERA0pYGE0G9T1b8qC7VizNR/BYBDZWTacMjQXqekWeN0+BjCIiChiDFwQUVKQElAulw8uddMMZGba4Mx3MMuCiIhiwuX0okVjG168eRQ+/GEV5q3MU/0vwmxmPcYMzMElY7si4AtAo2FjbiIiahgMBh1sKWZ8NnkNvpuxCW5voMzrX05dhwFds3Hr3/vDrNHA7ebCMiIiqhoDF0REREREEXA6PEi3GHDzBf3g9QWwZO1elX2RlWZGvy5N4fUG4HH51ApTm81U34dLRERU67RajbrmvfrlUkxduL3cbaTl04JVe3Dry9Pxwk0jYQqFmHlBRERVYuCCiIiIiChCkt0Hlw9Gow4DuzSFJFYEg1D9LMJZflIug4iIqCEwmQxYuGZPhUGL0nbus+Otb5bjn2f0ZOCCiIiqxKcqojhgNOphsRhgNhvURAgRERHFN8mucDq9cDi8cLm8LE1IREQNktGkx8RpGyPefsbSnYCmpLwUERFRZRi4IKrHlFqLxYj0DCvc/iDW7SjEhl1FCECjGpfJayyPTURERERERPFIgg8Olw+rNh+MeB+fP4jpS3YycEFERFViqSiieqDTaWFLMWHZ+v346vf1ZW70JFjRt1NTnH9CJ+Q2T1P1tEs3/yQiIiIiIiKKh8V4B+2eqPfbX+ACH3GJiKgqDFwQ1UfzshQT/jd1Az77dW25jcsWr92Lpev24rpz+mBE7xZw2N3q80RERERERETxIBQKwVCNvk5Ggw5aVhcgIqJEKhX11ltv4eKLLy7zualTp+Lss89G3759MWbMGDz99NNwu92HX/d4PHj44YcxZMgQtc2tt96KgwcjT1Mkqo/mZfNW5pUbtChNVqC89uVSbNldrHpfEBEREREREcUL6e/UrJENGSmmqPYb0LUpqwoQEVHiBC4mTJiAl156qcznFi5ciOuvvx4nnHACvvnmGzz44IP48ccfVaAi7KGHHsLMmTPx73//Gx9++CE2bdqEG264oR5GQFQ1KQNlthjw5ZT1EW0v93JfTFkHvYHJUURERERERBQ/JPjgcvtw4jE5Ee/TvmU6crLT4PH4avXYiIgo8dV74GLPnj245ppr8NxzzyE3N7fMa5999hmOOeYY9bq8NmrUKNx8882YNGkSvF6v2nfixIm47777MGDAAPTq1QsvvPACFixYgCVLltTbmIgqy7bYursIW3YXRbzP4jV74Pb6YTSyeRkRERERERHFj4A/gDNHd0BWmrnKbaU81GXjusHt8bEUMhERVanel3GvXLkSBoMB3333HV577TXs3Lnz8GtXXHEFtNqysRX5b5/PB7vdjkWLFqnPDR48+PDrbdu2RXZ2tgpeSOmo6tJXo05jZY2YS/+ZTDi26H+u1m8viGofybrYvseOds1TY5pOy+9dYuLYElMyj62+r7PJfG6TeWzJPj6OLTFxbIkr2ccXLT7LRoZjiw15RpWJpaeuG4Z73piF/QV/lfYuTa/T4OYL+qFzTiY8bm+1f075fUtcyTw+jo0oSQMX0rdCPsrTrVu3Mv8tAYsPPvgAPXr0QFZWlsq4yMzMhMlUtp5i06ZNkZeXV6PmyZmZNsRaWpoFyYpji1x1V5bYbCb1EWv83iUmji0xJfPY6vs6m8znNpnHluzj49gSE8eWuJJ9fJHgs2z0OLbYMJn0ePOu4/HbvK34ftZm7NhrV59PsRhw3MAcnDGqPVJtRpgMOlhi0MOR37fElczj49iIkixwESm/34877rgD69evV/0whMvlgtFoPGpbCWRI0+6arBgoKnIiViQqKf/Ai4pcCASCSCYcW/Q3c7ktUqPer3ljG+x2N3y+AGKF37vExLElpmQZW6wmQmJ5nU2Wc9vQxpbs4+PYEhPHlriSYXzxeI1NlnNbEY6tdt53dL9WOHloLvz+EIKhEExGneqDEQoE4bS7UdOfTn7fElcyj49ji2+1EdCnupEQgQspC3XTTTdh/vz5ePXVV1UvC2E2m1WviyNJ0MJiqVkk0O+P/T9G+QdeG183HnBskX4tHzq1zlSBiN37HRHt06tDY6RajSgqjN0DSNlj4vcuEXFsiSmZxxatWJ+HZD63yTy2ZB8fx5aYOLbElezjixSfZaPDscWOei+PHy6nR5X51mgApz0U05LHYfy+Ja5kHh/HRhRbcV+gbO/evbjwwguxdOlSvPfee6pBd1izZs1QUFBwVPBC9pE+F0TVodNpYLEYoTfq4fL4YTDqYTYb1E1XTYVCIbXa5KzRHSLe529jOsLn9dX8zYmIiIiIiIhqmZRHDk9y1kbQgoiIGoa4zrgoLCzEpZdeqjIupDxU586dy7zev39/BINB1aR7yJAh6nObN29WvS8GDhxYT0dNiVwP1mQxwGIyYO6KPCxYvQcerx8ZqSacMCgHOdlpcLm9cLtqFkTwevw4bkBrbNxRgJ/nbq1020vGdkXXtlmwF5Xf4IyIiIiIiIiIiIgo2cR14OLJJ5/E9u3b8e6776pm3Pv27Tv8mvy3ZFWceuqpuO+++/DEE0+o8lAPPvggBg0ahD59+tTrsVPiBS1SUs2YtWwX/vP9KhTYy/ZI+X7mZrRvmY7bL+qPdJsRLufRJcoiJStPHA4Prj6jJzq0zsA3f2zEzn0ljcvC2rdKx3nHd0Lfzk3htHtUpgYRERERERFRVTSxKBeQhM/8ghkgRESJI24DF4FAAD/++CN8Pp/KujjSlClT0KpVKzz66KMqaHH99derz48cOVIFMoiiYbWZMHneNrw9cXmF22zcWYhbXp6O528ciXSroUaZF9Jku7jYjWE9m+P4gTnqa2/dXQSNVoOOrTLQskkK3G4fHMVu3lgRERERERFRpfR67eEyx3pdSVVwaagrpYp9Xn+DrE1f3jmRhYQN+ZwQESWSuApcPPXUU4f/rtPp8Oeff1a5j9VqxWOPPaY+iKrDZNKj2OnDO99WHLQIc7r9eOrDBXjpltHwuH2qdmd1qRsmZ0npqeaZFrRsZFWfl0BFQYGjRl+biIiIiIiIGgaL1aiea39ftAM/ztqMrXlF6nmyVdMUjB3WFscNyIHP569R5YBEPCdGox5/LN6BH2ZtxrZS5+TUYW0xpgGeEyKiRBNXgQui+qDV6zBx6tqIAwVb84qxcUchWjaywFXDfhdCykBJdgURERERERFRtBP0BQ4v7n3uDxw8ojeiPLu+8dWf+OSXNXjk6qHIzrTA6ShbFrkhnpPXv/oTExrYOSEiSkQluXJEDZSU/kyxGvHHou1R7Td53haEWDeUiIiIiIiI6onZrIc3EMJdr808aoK+tEK7F/e8PhPFLh8sFgOSmZSF8vojPyd2d/KfEyKiRMXABaGhNy2Tkk1SKioaBwrd0DJwQURERERERPVYPUCyKWQSvioOtx8f/bgaOr0OyX5OJkR5TmQfIiKKPwxcUIOn02mh1UYXhDAZdAjWcxMKWUlithpLPixGWCxGNRYiIiIiIiJK/sbT0nBaejhEas7yXQgEQzAYdEl7TnQ6DaYtifyczP5zt+ozmaznhIgokbHHBTVocoPi8QbQs31jLFu/L+L9+nRuAk2o/gIWJrMBew868fPcLSr7w6DXoXfHxhjZtxW8Xj88bi8CAXb3JiIiIiIiSkZ6vQ5rtuSr59lI+QMh/LlhP3q1awSfL/L9EuucHIzynAST+pwQESUyBi6owfN5fTh9ZLuIAxcWkx7HDciBw15xvczabDLm9gXx6JuzsXrLwTKv/b5oO975dgXOP6Ezxg7Nhb3YrcpgERERERERUfKVPXZ5/VHv53T7Va/HpD0nnmqcE0/ynhMiokTGujLU4Hk8fvTr3BRdcjMj2v78EzqplRh+f90GBSTLwuMP4uYXpx0VtAhzuHx477sV+OK3dbClmHjzRURERERElIRCoRAyUkxR75eVZlL7JiMZV2aqOer9slKT95wQESUyBi6owZNyUU6nB49cPbTK4MU5Yzpi3PB28Lija+YdqxJRz09YjAK7p8ptP/9tHbbvtat9iIiIiIiIKLlIieCOORlolB75RH2q1YBeHZrAG0UppUQ8J1lpPCdERMmAgQsiAG63H36vD0/+33DcfelAdGubdfg1o16LMQNa45VbR+O8EzqpElF1XYLJZNLjYKEbyzfuj3ifiX9sgE7PBmNERERERETJuADP5fLhlCG5Ee9z3MAcuL3+pC0pXJ1zcvyg5D4nRESJjD0uiEoFL6QElDTlGtA1G1qNBr5AECaDDm6PHwF/AMVFLtRHBqlGp8Uv8zZHtc+sP3fjpvP7QafT8iaMiIiIiIgoyfh9AZw5ugMWrNqDtdvyK902t3kaLjq5C9wuL5L9nJx1bAcsWL0H6yI4JxeelPznhIgoUTHjgqiUQEBWaHhRWOCEw+FWjbiLi10qy8Lt9tVL0ELI++YXR9cM3B8IsskYERERERFRkpKFd263F49fOxSDezSv8Nmvf5emePr64fB6/ElfEil8Tp7gOSEiSnjMuCCqJM209J/1zViNsk8GvRb+qltiEBERERERUQLyuP1ACLjtwn4ocngxaeYmbN1djBBCaNU0FaeNaKd6PkhWgcfjR0PAc0JElBwYuCBKAFqE0K9LU/w0Z0vE+7RtkaYCF44gy0QRERERERElK5l8lw/pjXjumI7QaLXQaTUqCz/oD6iKAg35nJwzpiO0Wkm90CAUDCIYCDbIc0JElGgYuCBKAHLDNbBrtloVcrAospJRsorE7aq/8lZERERERERUd8LZA3q9FpmZNuTnO+D3N+yFbMyoICJKXOxxQZQApFyVxxvApWO7RrR9m2apGNWvNXw+3qQRERERERERERFRYmHggihByKqZfl2yccX47pU23M5tnoaHrx6CQrsbBkP0fTGIiIiIiIiIiIiI6hNLRRElAKnLebDQjQfemYOHrhyMvp2b4sdZm/HH4h1wHUp97dg6A6cOa4vhfVrii9/WYc8BB/7vb70B+BAPJNhiNOoP1RYFAoEgvN5AfR8WERFRpXj9IiIiathkEaHFYlT3BFKK2est6Z9BRES1i4ELogSg0Wnxy7zN2L3fgRue/wMj+rbEqUPb4p9n9YLL7VOZFYFACL8v2o5bXpyGbXuKoddpcdP5/eDWadUkS32RiR6LWQezyQB/4V74Du4GNFrYsnORkm6F2xtSY2AvDiIiiic6nQZGkwFmswG79tmRd9AJvVaD9q0ykJJmhN/nV72kiIiIKDlZLAZo9TqYDDqs3ZaPIrsXFrMeXXOzYDDqEQqG4HB46vswiYiSFgMXRAlAJvXzi0uacnt8Afw2f5v6aJRuRqrVCJ8/iP0FLvVamD8QhNPjr7SsVF2sTElPMcKxfj52z/0Wnt0bS72qgaV9H2SMOBfpjXJQ5PCrXh5ERET1TRYE2FJMmL5kJyZO24gtu4vKBOQHds3GBSd1RvMsG5wOD0KMvhMRESWVlBSTClr877d1+HneVhQU/xWgsJj0OHZAa/z9xM5ISTXDfuhZnYiIYouBC6IEYdQf3a/iQKFbfVTEoNfC76m/larpqUYcnPoxihb8UM4WIbg2LoFr0zI0Gf8vpHUchEI7My+IiKh+6XRaFbR4/as/1SKBI0mQfd7KPCxaswd3XjwQvTo0gsPO1ZZERETJwmYzqioBd/x7BjbtLDzqdSnXLKWb5y7fjWf+NQLpqWYUM3hBRBRzbM5NlAC0CKF/l6ZR7dOuZboKXASD9VMmymrSwv7nHxUELUoJBbFv0r8RKNilynEQERHVJ6NJj0kzNpUbtCjNHwjh6Y8XosDu5fWLiIgoiRiMBjz54fxygxalHSxy4743Z6lMTYOB02tERLHG36xECUAafw3omo2sNHPE+4wf3lbV3q6PDAYpo2GymFAw55vIdggFUTjra3Deh4iI6lNJXyYDvplWurRhxaQs4/+mrodWH9tbao1Go46lPss9EhERNUSpqWbkHXBgybp9EW2fd8CJOSt2w2w21vqxERE1NAxcECUAKUshDawvPbVrRNvnNk/DqH6t4PX6UR9MJj1c29fAX7An4n0c6+ZDE/Sr1SpERET1QTInFq7eU6aOdVWkD4Zep1V9nWpCghTSBFRqZTdunIKsLBsaN06FNcXEjA4iIqI6EggB383YFNU+38/cDF05pZ2JiKhm2OOCKEF43D4M69UCdqcP7363osJMCglaPH7tULjdPgQC9VMmSlaJendviG6nYAC+/D3QprSorcMiIiKqlFxaN+woiGofjy+A/QUupJjktjpYo2bgm3cV4dvpGzF/ZR7c3gBsZj2G9W6JM0a1R9NMK5wON/z++rm2ExERNQTSq3Hr7qKo9tmyu0iVadZqZdFhrR0aEVGDw8AFUQJlXdiL3TjhmBwM7JaNidM24vdFO1RjMNGxdQZOH9kew3q3gMvlVWWi6lW1SlSxMzcREVXcNFsm+KWMUigUgs8XiHmAXhYFVKfEYk2uXiVBCzNe+myxyt4ozeH2Y/K8repDgheXjO2K4iJ3vS1MICIiOnLBmtGoP3xt9vsDSRFgD0V7Za+P+sxERA0AAxdEiRa8KHLDZtKryYtrzuqlVnpKiQq5UfS4/SgqdNX7hIasMjFmt41uJ40W+oxsuL286SMior/IhIjOoIPJqMfarQdR7PQizWpE5zZZcHv9CPj88HoDMXkvraYkczEassKycboFLmfk5aVKs9pM+PcXS44KWhxJFiyYDDqcfWwHtZCBiIiovkjQXW/Qqb5Q67cXoMDugcWoR5fcTNX/KegPwO2un7LFNRUIhNC6aSrWbMmPeJ9W2akl4078mA0RUVxh4IIoQZt1w+OH2+lVq1tkTYjcYMULj8eHrNzu0KU1RqBof0T7WDv0g0ZvgM9Rz5kiREQUN6Tng0anw6eT12LKgm2wl8omTLMZcfzAHJx3QidoddqYZBrK9XVwj+bqaxc5vBHtM7x3CwTVKtPoZyukd8Weg06VQRmJr35frzIvZMJIMk6IiIjqmsmsV42ov/59A36aswUHi/4KppuNOtVr8e8ndYHFaoTLGdm1NJ7oNMD4Ee3w6/xtEe8zdmhb+HldJiKKOQYuiBI8AyMeyyvJcXncHqQfMx4Hf/1PBHtokD70bLgS776WiIhqiUzq+0LA7S/8oSb3jySBha//2IB5K/PwzL+Gw2zW13h1p2Qsuj1+XDG+u6pxnZ1lU02z84s8mLZ4B+au2I2Auvb+VSLj7GM7IlDNyQqtXouJ0yLvCeUPhPDj7M0YOySXgQsiIqpzJpMeJpMBd78+C+u2HZ2RIP2Zfpm7VV0vn7puODIkeJFgD3kOhxuts1PRrW0WVm0+WOX2WWlmjOzbEg47syGJiGJNG/OvSEQEwOkOIa3fSUjpdWwVW2rQ6KR/wNAkRzUUJyIikoCA1WrEA2/NKTdoUdrOfXY89M5cWKymQ1mI1SeZDBKwGNKzOQ4UujFpxiZM/GMj1m/Px2XjuuG9+07EcQNblxyjBrjxvD5ommmp9vXLZjFiydp9Ue2zaM1e1e+DiIiorpnMRjw3YXG5QYvSCu1e3PfmbJWdodcn1jVLyj35vAHcd8UxaNkkpdJtJUPzsWuGqsUEsSpbSUREf2HGBRHVClm1WmT3ofEp/4SpZScUzfsOvoO7y2xjatUZmcPPhbFVVxTafexpRkREh1d0rth0AFt2F0W0vdTX3rC9AK0aW+GqZsko6aVhtRnx7ncrMXnuVlWrurT/TV2PY7o3xw3n9UHnnEzkNEtFuxbpcDqq19siTPp0RLd9gIELIiKql2tzod2DeSvLPtNVRBYATFu8E4O7ZcPvT6ysC7vdjZRUM168eRQ+/nE1pi7cBkeprE7pbzWsVwtcemo32Mx69p4iIqolDFwQUa2RlScFRSFYO49Aq97HwZO3Cd4DO6HR6mBq3h769CaqJEdBsQQtGLUgIqISOr1OZTtE49sZG/Gvv/WRq0/076fTqqDFkx8uwIJVe8rdRi5TUvoi74ADz94wAqFASJWFqMnlS0orpqeYUOyM/JjTU4xHBVWIiIhqm/STmvTH+qiue9/P2oTR/VslXLkoIcEIyf686JQuuHx8N5UhKU3IJVDRt3NTleWpCYVQzKAFEVGtYeCCiGo986LYGYTdFYAprTV0GTnq8+5AEJ583uQREdHRTEZ9xNkWYdt2F8No0MFRzWyLqQu3Vxi0KE2O68MfVuGik7rUOFPQ6fJidL9W+O/PayLeZ0z/1ggycEFERHVMo9Via5TX5q27i6HXadUkfyIuVHMeai4u2Sa9OzQ6NA5ZoOevdoYnERFFjnnmRFQn5EZVaoDLzZ98eDw1a6BKRERUmpoOqUaLC2mLYbYY8F0UGR4S5JB+GDWt2+33BTB2WFvodZEdeKrVgOG9W8LLaygREdWDUDX3qGELqnonz65FRW4UFrpQVORi0IKIqI4wcEFEREREccXjC6BV08obYh5Jtq9OY0yDQa/KP23LK454H6fbjwWr8lSmRk1LKuo0Glx7du+IGpbfdtEAuD0+lc1IRERUl4LBIFo3TY1qn1ZNU9U1S0ojEhERRYuBCyIiIiKKKwGfH+OHt4tqn9NGtEMwEH3gQgICBcXRN9jem++KyQpSl9ODkX1a4pa/91MZFeXJSjPjkasGo0ubTLgTsE44ERElvlAgiPEjors2jx2WC5eb2QlERFQ97HFBlIBkosRkMgBamTGRj5D6v9/nh9/PVZhERJT4JRn6dG6isih27LVXuX2bZqnompuF/HxntUoZSm+MaJlrmG0RJqtQpcn3wK7ZGNG7JWYu24nZy3fD5fEjxWrAsf1ao3+Xpmrix1nDZuBEREQ1uTY3yrCiX+emWLx2b5Xbp6cYMaZ/DpwO91ELBuRZVi5nf/W+CMHj9idkHwwiIqo9DFwQJRiz2aBqce/e78DPc7biQKFL1dnu1aExju3fSpXJ8LhZRoKIiBKXTOZLP6RHrh6C216ZgYNFZSc9SmuSaVHbSaPr6kx4SMA/t3karGa9KgEVKQkmxGqxgIzX5fDA6/ZhQJemGNA1u2QyR0prhIIoKHCyzAYREdU7yfq785IBuOPfM7C1khKLNosBj1w9VDWxDl8r5bomz7EWswELV+85HKRPtRoxZkArdGmTpa7lbvaPICKiQxi4IEogFqsRLm8AD782C2u35Zd5bdriHXjvuxU47/hOGDe8nVq92RCzLyQbRVbxEBFRYpOJC7nuvXLraHz042pMW7IDnlI9LCwmPUb1a4VLTukKTSgEVzVLKEmgX3pNHNu/NX6YtTmifXp1bIz0FJNaRWoy6REIyMTMX69X9zokx+JyshRUTcnkmNwPSByLq3eJiGKbdSG/YJ+7cSQ++WUNfpu/DcXOvwINep0GQ3u1wCVjuyLFbIDT4Tl8XbSlmFWmxlvfLD9qQcLkeVvRorENt17YHy2b2OC0eyr9HV9X5K3C71cXCwjCQ0v0ZuZERLHCwAVRAmVaSNDi5pemodBe/qSGrBT9z/erUOTw4YITO6G4yNVgSkpIg1SLIQSj1YpQqCRgk55Scs7c7gBXqhIRJSCZxJfAwBXju+Oq03tg2fp9aoIkzWZA745N4Q8EEPAF4JaJlGpeW7V6nVoZev4JnVWZpoqusWF6nRaXn9odWi2QmWlDMBSCVqOBx1tyDJIFGZ5vSEm1wOPxwevxMxOylskkj8pK1YegN1vUvYBGo4XP7YLbp1Hfh4ZyT0REVJskuz8UDOLc4zrhklO6YdmGfcgv8qjMRakCoNNqEPAHDgcthCxEmLNiN178dHGFv4t37Xfgrtdm4olrh6FlY1uZnk6qvJTZoJ75pLyjXHvlGmx3eNV7eQ9dg2PFaNTBbACMFosqYyXXk6DfB7fbD7c3gEAgdhcUlYli1kOn16lsFBlbRoYNDpcXQX8QbvYIIaIGjIELogQhN2pPvD2nygkV8dXv6zG8d3M0y7TAleSptnITm2bTQ+NzoXDO97Av/wMBRwGg1cOS0w1pg8Yhq30fdVMrN5pERJSAqzs9fuj1OnTPzTq8kl6yHaqbWajXa2G1mbE334mJ01ajR/vG6NAqAw9fPQQPvzMX+RU06zYZdbjjogGwWfTYc8CJG174A0a9FvdefgzaNE/D5Llb8PPcrerrykRE+5bpqpGp9K5wuVn+orZIsCgtxQDvvu04OO87ONbNR8jngcZohq3LEKQfMx7WzBYosvvg90ffwJ2IiMqS8sTwBlSJw86t0g/1qgD8Xj9cvsBRv6NlDdkrny+tMoDs8wfx5IcL8P59J8Dr8anFZxKUtlqNWLB6D76dvhGrNx9EIBhCVpoZxw9sra6zks3hcnpqvFhNxpFm00Eb8qNo4Y/IWzoFgeIDgEYLU8tOSB94KjI7D4LT5YPT5Y9JgMRmM2PDjgJMnLZRldDy+AIqCDSiT0ucOao9GmdYVBCoIVZTICJi4IIoAchqU+llsWLTgYj3+WbaRlz3t95y+4dkJTeW6akGOFfPwv4f3lB1wA8L+uHa8qf6MLXoiGZ/f0D9ymPwgogoMcmEcywmnSVokZJqVuWnZJIgxWLA1Wf2wu2vTMdpI9rj9TvGYMqC7fhpzhbs3FfSGDwz1YSTBrfBSYNzsWu/Hfe8Pguv33mcCnZccVp3OJw+/OOxyWoVZpiUKFq/vQAvfLIYn/6yFo9fOxQWi7HaJa2okqBFqlHdB8jihdJCXjfsf/6uPlL7nYRGJ16BwiJO/hARxYoqceiq/Heq3qDDj7M3wx9h5qGUkZIgRY/crJJyfzotbn15OjbuLDxquy+mrMfXf2zATef3w6Du2XAUu6udXScLI9JT9PBuW46937yAkL/U9ToUhGfHGuzdsQaGxq3R/KKHAYuxRsELCVpYbSY8N2ERZv2566hKCr/M3ao+zj62Iy48uTOKi9zM3iSiBkeLOPLWW2/h4osvLvO51atX46KLLkKfPn0wZswYfPTRR2VeDwaDeOWVVzBixAi1zVVXXYXt27fX8ZET1S6NVovJ87ZFtc/sP3fBqNdBp4urf+YxlWLVwr1hEfZ//1rZoMURPLvWI++Th2GzGpL6fBARUdXMFiM+/3WdClqI3h2bYMfeYmzeVYSXP1+CR96bh/RUI/5927H435On4vPHT8UHD5yEzm2y8PpXy3D/m7Oxv9CN+SvzcNm4bmqC5PEP5pcJWhxp94GS8hcanVZNVFDspNr0OPDzO0cFLY5UvPgX5E/9r8rSJCKiumO1GDF14Y6o9vl1/jZodVqVbSGLBY4MWpTmD4TwwieLsHLTAXWNry7JpvTv2Yg9/3umbNDiCL7927H74/thMWlV8Ly6QRIJWrzw6eKjghblVVP4auoGVW6LiKihiZsZvAkTJuCll14q87n8/HxcfvnlyMnJwVdffYXrrrsOzz33nPp72Ouvv45PPvkEjz76KD777DMVyLjyyivh9XI1GyWX/OKyDcyqIjdwTo8/aRt7ldQ5NeHAbx9EtL1n1wY4Vs+B2RQ3v/aIiKiOyQSDlJeQSYCwVKsBBUV/lYZaveUgnp+wGBc+8BNuenGaysS4+KGf8fC7c7Fg1R5V7iK80lNKQX30wypV2qIqe/Nd+OaPDaqGNcUuIzXoKEDx0t8i2r5wwQ8IeV2qRjoREdU+eRaV57Zon2Xzi9xqwdmUhduxZXdRldvLtfmdiStUj4jqNO+WXaQ084Ff3690QVyY78BOFC36RfXBqA6TyYAde+2YubTyoEXYF1PWqTFWN1BCRJSo6v2ufc+ePXjwwQcxb9485Obmlnntiy++gMFgwCOPPAK9Xo/27dtj69atePvtt3H22Wer4MT777+P2267DaNHj1b7vPjiiyr7YvLkyRg3blw9jYootmSORLInomXQa+Evv0x3wjOb9HBuWlZSczRCRQt+QPMug+FwslwUEVFD9Fe5ir/qSHh8QdXo80guj19NKlRE+l1IlkU0ZRx/mbcVF5zYWTU2rWkdbgLMhhAKp38X+Q6hoLoXsB1zJrjGiYgoNk2lQxrNoR4XIWhCIdUoO9y8Oly2Sa6zUv5INGtkxclDctE5JxNmox5Ojw/L1u3H5HlbUWAveXg1GXTq630/c1PExyPNvWXxQZvslKh7Sklmh3fvNnj3bIl4n6JFPyN90KlwuF1RX9NlEUM48zMSskDi5zlbcNIxOfAd0UOEiCiZ1XvgYuXKlSo48d133+G1117Dzp07D7+2cOFCDBo0SAUtwgYPHqxKSu3fvx+7du2Cw+HAkCFDDr+elpaGbt26YcGCBTUKXEj941gJl6ZJxhI1HFvdkB/HAV2zVb3tSMkqUAlcuDXl/zzH0/iqw6ANoHD9wqj2kZJRCAZhMukO30wnmkT/vlWGY2tYYnWdTeZzm8xjq6/xyUTBojV7y3xu865CdGydoRphhidVItGvc1PVwyIaBcUe1TcjK8WYsBMP8fRzaTCZ4Nq0LKp9nJuWImP4OdDrQ3E9tlhL5rE1hPFFi8+ykeHYqk8y1yRDYcP2AkxZsA0Fdi/MRh0GdW+GIT2bw+P1w+fxq+CD0+1TZRmXrtuLG87ti76dm2DeijxVClkWCaTZDBjeuyXOP7ETfl+0A29/sxx9OjVRwYCtecVRHde8lXnIbdYx6n8Dem0IzvXzo9rHX7AHfnsBjEZr1L2TrBYDlqwtez9SFbl/OWVobkz/fdc1/ptLTMk8Nop/9R64kL4V8lGevLw8dOrUqcznmjZtqv7cvXu3el00b978qG3Cr1WHpDJmZtoQa2lpFiQrjq329e/SFFlpZlWaIhLjR7RTf1b1sxwv44tW0OtCyBd9Okko4EVaWioSXaJ+3yLBsSW/2rjOJvO5Teax1fX4vP4APEf0opASFPIxZkBrfD9zc0Rfp1fHxkhPMWLGkoprbldEMjxSUsxIdPHycxn0RVd+RO4dNDodMjONcT+22pDMY2sI44sEn2Wjx7FFx+sLYNc+O555bRa27ykbWPhj8Q51ffzH+B4Y1rvF4YzGv43pgAtP6oJ12/Pxj8d+RX5x2ec4CWI0b2zDDef2wWPXDEWrpinwB6NvRC2ZkHq9Dik2U1T7BX0eOHzVSMXze5Ga1Sj6/Q4da3Tb+6HXaWGthX/fdY3/5hJTMo+N4le9By4q43a7YTSWfagwmUouQB6PBy6XS/29vG0KC6N/kAyTyH5RkROxIlFJ+QdeVORCIBD9xTeecWx1x2gy4LJTu6kGXlXJbZ6GkX1bwunwVHjs8Ta+aNlMGuhs6dHtpNVBYzCjsNCZsCU6Ev37VhmOLf7FaiIkltfZZDm3DW1s9TU+adqZnnL0ZMYPszbjkrFdMWPpThTaK5+4kEmDi07uqoIdqbboG2Wm2Yyw290JnXERLz+X6akmdS8QKD4Y8T46azqCfj+KHO64HlusJfPYkmV88XiNTZZzG83YdDqNqvggGQWysl09M2gAl8uHYCAQ9cr6aHosyHtqtFrVp0HeV4JQkq0gGeMejz8uvm8Gkx778t24/dUZRy0ECJPrqDyzSgkoWRTgcXnRvJENM5ftwsufLzlcPupIu/c78MDbc/DoP4eqa63Qakr6V0QqI8WIQCCA/PzoFrhZzLrony3l+2ZOQXGxK+qfi/QMKzJSTCrjJFKyvbyPwx5dwD6eNLTfJ8kiGcZWGwF9qhtxHbgwm81HNdmWgIWwWq3qdSHbhP8e3sZiqVkksDZuSOQfeG3d6NQ3jq32BYNeDO3VHHZXT7zz7fIKb/jatkjD49cMg9vti+gGN17GFy2P3oCU3sehcO63Ee9j6zRIPXB4o1zdEo8S9fsWCY6tYYj1eUjmc5vMY6vr8QX8QTWJsviI8gzTluzAgG7ZeOTqoaoJd0XZjdLX4s6LB6gJpk9/WYdbL+yHtycuj6g5t2jXMh2N0swoKHBUeB1PFPHwc+n1eJHSfSQO5kWWKSNSeo2G11f5scfD2GpLMo+tIYwvUnyWrd7YLBajKn80Y+kOTJqxGZt2Fqheg00zrThpcBuMHdoWRl0ILqcnpr/DpeGyLcWkAuLS90DKKHl8AVhMelV26cxR7dGiSQocdg/8/kC9fd8kkGIxGfDEh9MrDFqUJiWfBvdoDrNBr66Tr365rMrzJts98/FCvH/fCeq4+3XJxsLVeyI+xhMGtVHX+mjH7PFqkdJzNA5O/W9EzbmFOacbtAYT3I7oqwA4nV6M6tcSn/26LuJ9jhuYg2CS/DtsCL9PklEyj43iV1wXKGvWrBn27i37YBn+7+zs7MMlosrbRl4nSiay6sZe7MbxA1vj7buPx6nD2qqb2TBpbnb7Rf3xwk2j1KqcaBuSJRoJzBgymsLUsmw5ucqkDT4NsmiJiIgaJmkYKqUrJOuhNJlIeenTxdi0sxCv3TEGV57eAy2bpBx+PTPVpJpqv3nncSp48dA7c1XwQ1aVSl3uSI0f3k5dvxI9aBEv5Jqe2vcEaPSRZb5oTFak9BgBt5cP3URUlsVqhNMbwLVPT8GLny7Bhh0FaqW//L7ec9CJj35cjcsfnYwNOwthjbIMUdVBCzP+/cVS3PLSdExfslMFLYSsxp+6cDv+9fwfeO+7FUhJNddrfwOTSY8Vmw4g70BkGT1y/ibN2ASNTosfZ2+GP8KV2rJ4YMHqPao/xhmj2kd8fCVlHE1RZ6eE7w8kM9/asX/E+6QNGg9XNa8nAX8A44a3g05SSiLMthjcoxm8Xj7MElHDEteBi4EDB2LRokUq1S9s7ty5aNu2LRo1aoQuXbogJSUF8+bNO/x6UVERVq1apfYlStbghdWgxcUnd8EXT5yK/z05DhOfGY8nrxuGfh2boKjQpSZFGgKX24cmp90IrbnqtL+0gWNhbJrbYM4NERGVv1JMrgMS6D9yssAfCKkSFg+/O0cFNv5922h8/fQ4fPXUOHz44Ek4c3QHrN2aj/vfnA3HocUBX/2+HpeP74ZmjaxVvvfArtlqdaVMjlBsSLktmQdrPO46CUtUvrFGi6an3Qif18/VgkR0VPBAUunuem2mClJURAIJkpW3a78TFoshJu8tQZA3vlqmmlJX5sfZW/DxT6tgscYuaBI1rRa/ztsa1S5/LNoBs0mPqQsrH9+RJs/bikAI6JqbhRMH5VS5vVy3bzqvL7ye6j/rOT1BND7lGuhSs6rc1tZtOKzt+8Ltrt41XSoAGPVaXHNWryq31es0uOPiAerZNyAnhYioAYnrwMXZZ58Nu92Oe++9Fxs2bMDXX3+NDz74AP/85z8P97a46KKL8Nxzz2HKlClYs2YNbr75ZpWpceKJJ9b34RPVGllFIv0rDhywqxqXBQVOFOQ74XJ5E7bmYHU4XQEEzelocfnTMDQp/4ZWVmFmjDgPWWMuQZGdq1yJiBo6t8uLTq0z8cjVQ9A44+gm2Wu25OPdb1dg7vLdasGA0+FW19uAz4+B3bJx1Rk9YTOXZDxOWbBdTco8dd0I9GhffnNOCZCceEwb3H3ZQDgdDes6XReKHAFYOgxA07Nvq7A+uS4lE9nn3gNjmx4odiZ+uUgiii3pK/HV7xuwr6Ckh2ZVpYzen7RS9R+MRQbDgUIXfp2/LaLtv52+STVoll4Y9UFKRR3ZVLsqHp8fWo3sF11fhoJij5qwl2fea87uhbPHdFAT/eVp3zJdVR2wGHU1WqQmQQifxoSWlz8NU4uO5W+k0yNt4Kloctq/UOzw1ahvooxtdP9WuPn8vki1lv/z1CTDonp+yBjl/oWIqKGJ6x4XklXx7rvv4vHHH8eZZ56JJk2a4I477lB/D7vhhhvg9/tx3333qWbekmnx3nvvwWCIzQoIqn9SR9okN4ZaDTQajUoZ1YRklV18rZiTlTp6g07VQdVAg5D8LxB9I7VolNwoNeyZ+GKHHzZLBlr94zl48jaheMmv8BcfUAELS25PpPYeg0AwhIIiThYREVFJ2Q8JRkhPqHfvPQFL1u5VK12LnV5VgnFozxaqnJTb41dZjuFJCWnMKvcgUmNaap3LPlJ3e8m6vchINeGhq4Zgf4FLlcXIO+BQEzwdW2eq0o4GvVbVJo9lQ275+jLpFZK7Du1f90ceT80mUmJNjlHKhITvjUTAF4jZuZBxFxT7kZrTCzn/ehuOtfPhWDMHQY8TWlUaaiSs7fvB4/agsJgLGIio/N9Tv86PPJNg+cb9yC9yq2tGTZ71dHodJkbR40B+t0+auRlnjmwH1MMctry/UbJToiDXKCH7OaPITjAZdOoZTq4Vci0+97hO6uOXuVuxctMBFUBqnGHB2KG5aNMsTS3gczlrflLsTj8sZguaX/wYfAd3omjRz/AX7oNGq4OpVRek9TsRIY0ORcXeGl/H5Hw6it0Y1D0bI/u2wsxlOzFnxW6V2ZNmNeLYAa3Rp1MTOF0+dd/C6xcRNURxFbh46qmnjvpcr1698Pnnn1e4j06nw+23364+KPmYzQaYLQbs3GvHz3O34kChG0aDFr06NMax/VurmwWPSpmsvwlpvV6nmrjJPZmktK7dlo9gIITsRlacOrQtMjOsanVEbQYwGjqHy69uhM3prZE+5lI1mSMre+RestAhDaS4upKIiP4iD/9upxdetw9dWmegS06WVBIq6ccZCqmyi+XdW6iSEG4/bDYjRvVtiWG9WqgFFvL1ZB1ops2IC0/qXHId0mrV15BMDXsMV/nL+5ktRljMBhU0mblslypdZbMYMbJvS/Tu0BhOtw8el7deJzlMZj1MJiMK7R78MH0D8vaXBHM65WSqwI+sVpaSHrEIYEjwQjIvdG43zDl9kZnbW72XTAr5QjocLHDFVTCHiOLLjr121bMoGvNW5uH4Aa3gib4v82Hq9/jafVHts3jNXpx7XEdUnRtSC0Ih9O3cJKpm2T3aNVILAXp3bIJpiyMvFyUT9tKIWshiRX+xWy0UlH6PJwzKgU5Xco2V3o4FBY6YXu9cbr86ZpMlG+mjLlKLJyUEEwhqIIkjPl/sSg/LtcnlkPsRPwZ0aYoBXbPLji3fyesXETVocRW4ICqvQdpDr83Cum35ZV6TpmXvfbdS3bSdNrK9KpdUH9kXRqNO1SX96IfVquGY94hj+OaPjeqmS2pSmjUa9leoRXKzKqth5SZemtZlZlrhzHfEVVYOERHFFzVhcKhfRTQcjqMnuEpXRS+5DtmQH+PrkEye2FJMWLX5IF7931LsLyhbemPKgm1ommnBjef3RYeWGfW2QlMWnmj0Wjz54XwsWrO3zGsSaPn4p9U4eXAuLh/fXR2j1PqOBan9LYsZyuJ9ABFVzlON3kNub6DGv18lwBpuxB0p2V6nrZ+K3z5vACcMaqOefSM9bmmuLfl2Z45qH3HgQkpEjR3aFn5f2e+LBLrlQ66xKbVwjT1qgYPbh+gKXFWfBCokY6QuxkZElEjiuscFNVzywOvyBnDLS9OOClqESQrlhz+uxoSf16jggVoIUYfkRlPe98VPl2Di9I1HBS3Clq7bh1tfmg6tZGaYGCskIiKi6i/qWLZhPx55b+5RQYuwvfkuPPDWHKzZlq8yM+qa3OvoDDrc9vKMo4IWYVLiY9LMTXhuwkJYbWbodHV8E0dEVEp6SvQNr7PSav78KRPT6SnR/Z6W7f31VG2gJIs9hHOPr6D/wxEky0Kaa9vtHlXOaXCPZhHtd/rIDip4EcvyikRElJgYuKC4JKWXXvhkcUQpu1//sQFb84pVsKOuj3HeijzMWLqzym13H3Dgza//hL6eGqkRERFRYpNVmFI+4vkJi1BV1QipC/7MxwtLggi6ur3d1xv0eOfbFdi5z17ltrP/3I1Zy3bGpMktEVF1Sa+E3OZpEW8vTaKH9W5Z42wxt8enyh9H47gBOfVWBlcW7hn1Opw6rB3OOrZDpdv26tgYd186EE6PX5VWdjg8uP2iARjYNbvS/aRnxYUnd2YjaiIiUhi4oLgjD9kHCl1YselAxPt8M22Dam5Wl4xGvcq0iJSURpCUU6nNSURERNGRCXhZpGCxGNWfdT0hX98MRj1+W7BNlSeJhPS9mLZYggJ1t2hC7nGkUfj0KOqYfzdjE0wmQ51nzhIRhUkvwvEj2kW8/Yi+rRA61Di6Jvy+QEnPH31k1zObWY9R/VrCW0+9E+U5fcXG/bj3jVk4fWR7vHDjSBw3sPXh45ff41Im+d7LB+GBfwzG2xOX46up69VzutfrV6WQ7rl8EB795xDVy0F76Pe+XqfFqH6t8NLNo/CP03rAXuxhmSQiIlK4/JvijkanxeR5m6PaZ+7y3bjlgn5/NbKqgwdzu9OHtVvLL2NVHknpnbZkB0b1bsG0VyIiogiprAG9TvWVWrXpIOwuL1KtRnRt20jVJQ/4AmpCJNmZjHpMXbg9qn2k58Xw3i1Ql4s6Zi7bWWH5zPKs316AgmI3jAaZ2OL9ERHVPemlMKZ/a8xZvrvKxtPNG9lw1ek9juq/UB3yTGgw6XHDeX3x/CeLKu2ZIdkOt13UHx5PoE6edys4CPw6fxs27izENU9Nweh+rXDmqA644dy+cHv9MBl0KHJ6MXnuVrz9zXLsK3AhK82My8Z1h8vpUddqf0EAHVqk4c6LB6jf+1I60GjQqjLQQX8ARYXOeunNRERE8YmBC4o/ISC/OLo2WP5ACC63v85W68mNY77dE/V++UVuGR4RERFFQHo0yAr+Cb+sxdSF2+Bw/zVRlGIxqJWefz+pC7Q6DdzVaLKdSGTRRLT3RwV2DwwRruSNhWAohAOF0bcyldKgTdOjrzFPRBQLwWAITqcH91w2UJX3nbJguyq5d6ReHRrjrksGIhgIqCyNWHA7vTimezM1kf/G13+WWypZJv9vPL8vurbJhMNeV+2iK3gGLi55BpZAw09ztqiPRulm2CwGeLwB7C9wlTl3EpgO7yvnWT5ccr12+dTnNBoN7KGSzxMRER2JgQuKS7JaI1ryYB6oozkLWQUSaUpvabKqhJUQiIiIqma2GOALhnD7S9NUw+kj2V0+fDt9E+av2oNn/zVClY9yu5M3eCErbKW2eLT3HeVNvtUWKftRnXs4WW3LFbZEVJ8kECGT51ee3hOXntoNP8zagg07CtTnJMti3PC2qheGx+2L6bVGvr692I3eHRrjgwdOwtwV0vtnF5xuP1KsBozq2wr9uzSFy+1TQYv6/F0pxyrXlSNJwLqioHW4THKonAMvCVbwlz8REVWMgQuKOxqE0L9LNn6cvSXifdq1TIdBmn7VUdqsNERr1siGzFTT4VUnkRjUrRlXkxAREVVBVmFaLUbc+MIf5QYtStu934EH35mD528YqSaeypscSQZuj1/VDt+13xHxPr07Nq7TMlqBQAgDu2Xj/UkrI94nPcWIFk1SVHkQIqL6JKWbfD6XKk04bliuygbQHMomQzCIwoLa+T0l1y3p/+D1+NCnfSP06dAEGi0QkkfbUAgFBc74eIYMhdCvc9Mqy2mV1qdjE3X9StJLMxER1bKG1dWQEoJMOvTv2lSlnEZq/PB2cHt8dXZDpFJc3T7VTC1SHVtnoEUTGzye5F0NSkREFAvSrHn5xv3Ymlcc0fYbdxRi3bZ8mM3JuyYn4A/gjFHtI95eymdK89RAHTY4lVXITTOt6JKbGfE+Jw5qo/aToAcRUTyQfjtSwsnl8MDp8Ki/u0uVKqwt8ntQyihJPwin3aP+dLm88RG0kMCON4ATBuXAZNTBYtIjO8uKJhmWSksSynUrFv1AiIioYWLgguJOuO7l5eO6RbR92xZpGNWvJbwxqjMa3QRCB3WzVhWdVoMrxnc/tBK0Tg6PiIgoYen0Wnw3Y1NU+8j2Wl30ZYoShdxDNEq3YMyA1hFtf/LgXNUHpK4bl8uK4SvG94BeV3VxTFmkctaxHeo0uEJERNWvOiBeuHEUPntsLN666zi8e+8J+O/Dp+Das3qhTbPUMttLlmCX3Kw6CfoQEVFyYuCC4pLUDj2mR3Ncc1ZPVS+5shJRj18z7NBKvWCdr8QJBYJ48rrharVJRWQFyp2XDFDH6nYd3WyNiIiIyjIZ9dgWYbZFmGxfXu3tZCIrf68/pw9G9GlZ6XYS3Lj6jJ6q9Ehdk3uyNtmpuPvSQZX2A2uSacFT1w1XpUfqOrhCRETRkSw+q80EXyCI2ct34R+PT8YZd0zCGXd8h3vfmAW9XovnbxypFuvJtr06NsZ9lw9S161kLeFIRES1L3nz6Snhsy4cxW6M6d8aA7o2w7fTNmLqwm1wHFqtISUIThvRHkN6NlcptLFexSFNxKS2qTAa9QgGfeWm6ErqrtTgfv2OMfhj8Q5MmrEJW3YXqdcyUkw48ZgcjBvRTj24S7pvefdscmMn76HVysN9SKUI8wGeiIjqklyHdGqFvAbBYFBdhxJtniHBDrfa9dedDjduvqAfxg1rizkrdqv7Eynb4fb6oddqMax3C7VYwm53q+3rmvzcyDF2b5eF9+4/Ed/P2IRf5m1FwaGeYLIiV+6N5B7P7wvA4Yi8VxgRUSxI7wqTSQfdoSw9efbzN/DMr/A50WjCz6RyL/DXNcRqM2P1loN48sMF8JS6tsjvfGli/u8vluJ/U9bjkX8OweCezdAkw6qCFpItSJExmfQwmw3q7zabEU6nr16u40RE8YSBC4pb8iBuL3ar+pkXntQZV5/ZE15fADqdFsFAEB6vH0WFrphmWsiNgk6vk3kbrN58EG5fQDXg7pyTqXpayAP2kTcPkkUhdTuH9mimVjjKtE8gGFKrPu0OryopJUGL8hqPSg1vk1mPvANO7NhbrG4Y27VIR0aGVZW+klWLXKFCRES1QQLnct0zGA0ocniwaXuBun61apqCFo1TVO8oyYCsj9raMinSOjsVew5G3gi1ddMUdZ+Q7GQiSaPxolMLK7rkdIZnxxoEXAXQWdJgbt0Ffp/UR/fW62SHCl7YPWoRyBkj2+GiU7rC5w9AKz90MobCfQjs2QBTkxwYUvVw+zTqnoeIqDbJc6TRpIfFbMDWvCL1DCbZ8R1aZSA1zaKe6aRkcUM/J3qdFh1z5JwYD/en2LanGI/9Zx78lfQj2n3AobIv/n3bsQxaRCElxQSjNgCNVgv3tuXwepzQWdOQ3rorgn4f3H6NCmIQETVEDFxQ3FM3PB6/egiXyX6ZyK+NSRSLzQSnx4//TlqFmct2wldq1U1WmhknD8nF2cd2gNvtheeIDA9ZoeP3ew8fowQg5BgrCjpIKq0txYz5q/Lw1dQNapVKaT3aN8L5J3RGp5xMOO3uuGnIRkREyUGuVVLyYcPOQnz261r8uX5/mddlxf5ZozuozEaH3V3nK1EDPj/GD2+Lhav3RLzP+BHtEAgkf+DCZtHDpPMjf9pnKF4+DSHPX8EdrdmGlJ6jkTnqfOi0ejhd/noPspi1GgT9XjiW/AbHqhnw7tmGkM+tXtfojbB1G4bMURfAYLOi2MFJLiKqHZJVYUsxYcqC7fh2+kbs2Gs//JqUJu7fJRsXnNQZLRunNJjyRuFz8tv8bapPVHnn5MKTuyCnWSre/W5FpUGLsL35LkycthGnDW9by0efHDLSDND4nTj4+2ewr5hx+PootNY0pPYeg8wR50KvM6KomGWniajhYeCCEobcPEoZpdpgsRqxr8CFe16fBXs5q2wOFrnxyS9rsGjNHtVTQ+pRVLSCpCTIEKp0skiCFhN+WYOvf99Q7jYrNh7A/Ztm459n9lRZHFI2qwHcOxMRUR3WqZ6+dCde+9+ycq8vm3YW4rkJizB+eDtcNq4b7MWS4Vh3FyK5xvbu2ERlf5SeSKlIbvM0dM3NQn5+5Bkaichq0cMYcmHne/fAX7jvqNeDbgeKFvwA54bFaHHp4wiZjXDVY1NUKXthM2ux++P74dl19D1PyO+F/c/f4Vy/AM0vehQptiawOxm8IKLYKlk0ZsLLny3BtCU7j3pdHt8WrN6DJev24o6LB6B3hyYqaJ/MwgvpXv5scaXnpFGGGeOHt8farfkRf+1f5m7Fecd3qreszUSRnmoAnIXY+dE9CNjLLmQUQWcRCudMVNf0lpc+jpQUI+x2Bi+IqGFhc25q8OSmTW/Q4f63ZpcbtChNbthe+nwxjKaS2pPVYTIbMHPZrgqDFmEykfTWN8uxcUfh4VqXRERENSXXFCn58HoFQYvSJs3chN8Xba/Rda86ZKLD6fLi0X8OReMMc6XbZmdZ8cjVQ+B0epN6hazKkrEasfvTR8oNWpTmz9+NvM8fh81qVFmg9cVm0WHvty+XG7QoLeiyI+/Th1WvDrkvIyKKJYPJoJ69ypugL00yCp75eBEK7J6kf/6Sc/LV7+urPCe5zdOxYHVeVF9bFv1J2Si9lGCmCrNdDAYDdk94qNygRWm+fduQ979nYD7Ug5OIqCHhkwE1eAajXqXHFka4emH2sl2qpJQ0Mo2WTB5I/dD/TV0f0fYy//LllPXQV+O9iIiIyqM36PHFlPVqNWUkvvp9g7p21fUEuNvlg0mvxcu3jFblGo98YJceWGOHtcVLN4+CXpv8PRLMJj1cW1bAt297RNt7d2+Ee/cGmM36esu2CLqK4Fw7P6LtZeKmePkfMBv5eEJEse3hIL8/pRRSJPyBoJrQ1yZxEDV8TiZFcE5MBh08pZp0R0r2qce4edyTwL5jzRz4C/dGtL17y3L4Du5GamrlizmIiJINZ0OpwbNYDPhh1uaIt5eJnh9mbsKZo9oD3ugf4qX8xvY9xRHvIynLTrcESnSqVjQREVFNVvhJ4+uFqyJfPbl7vwNrtuajTVNbnTctdbu86tp52aldceVpPfDnhn2wO31ItRnQq0MT1YA64A80iAagkvSyf8EPUe1TNP97ZI39P7hQ90z6EIrm/Fhp+cwjFS/6GS17H8dyUUQUM3INmbdiN4ockT+4SRbC1Wf0UhP8gUDd9niqC0aTDnOXR3ZO7C4vMlJMUb9HeoopqbMga0oaoO9b9HNU+xQu+AFZYy6ptWMiIopHybuMgCgCqpE2NNi5r+r62aVtzSsuKRIeJZ1Og407C6PaR+73du61Q6vlP1ciIqoZuQ7t2m+PONsibOOOAnXNrA8SlHDaPXA63Oiak4FBXZuiS+sMVX9cGqg2hKCF0BmM8EaYbRHm3b8dOn39lDuRHxfvvm1R7SPj0+h09fazRkTJR6530T5/SbbAwSKXumYmo1BIg027IjsnC1fvxdBeLdREe6Q6ts5AeopRLS6g8mn0huiv6fu2qWskEVFDwplQokPBgai2r8P3KtmHq1WIiChGqnMdQv3z+4Mq40N6Wcif8t8NilowUYc3LEREDViyP35FOj6V6ejyYVjvFhF/7dNGtFOLCpL9HFbXX+sReU0nIqoKAxfUoEnzT7n+t2ySEtV+Odmp1QomBAIhtGuZFvU8RcumKQgGG9gEDRERxZxch1o0SVGr4aPRvmW6umZS/Ql4PTA0bh3VPsbGLRH010/vD/lxMTZuFdU+hsYtEQoG+LNGRDEj17u2zdOj2kf6OjRKNyft7yJ5vmzbIrJnUnnklZ4f/xjfHdlZ1iq3H9yjOYb1bgmvJ7n7TtWEPNaHAj4YorxGGpu0UtdIIqKGhIELavBcLi/GDs2N6kZv3PC2CFZjpaesPOnQKgOtmkYeKOndsQlSLAb2tyAiohqTsg0yIdO/S3bE+zRrZEXX3KwGU5IpXskcUNrAsVHtkzZwHNz+eirx5dcgbeCpUe2T1v8U1deEiChW5No1pGdzpFojL5s3om9L1aQ7WTP7vB4/hvZsEfE5+W3+NlVa6oWbRqJH+0blbqPXadQz9R0XD1BlHGWhBFVMfrbkmheN9IGnwheqn/KPRET1hYELavB83gBOOKYN0mzGiLYf0qP5oUBC9BM4kqXhcvtw9rEdIt7nnDEd4avGexEREZXH7/PjnOM6Rdyq6cxRHdS1K1lXniYKt8cPa9teMDRqGdH2xuxcmFt2gtvtr7fJQq01HdaOAyLaXmtNQ2qv0XB7k3OikIjqhzTXdnv9GDe8XUTbywS8PKtVZ5Faop2TU4e1jficNGtkg0GrwSNXDcGbdx6H8cPbYWDXbAzu0QyXjO2Kjx86GZed2k31n6rOc3JD43AFkNJ1CHRpjSPa3pzTTV3/HQ53rR8bEVE8YeCCGjy/P4CAP4BHrh4Cm1lfZaOxW/7eDx539VNfJW12ZN9WqvZnVa48vQc65WTW26QDERElH7fbh9zmqfjnmT2rDF6cPCQXxw/KUaszqX5J4Ej6ezS74AHoUstf8RqmT2+KZuffD4fLW699smRipukZN8PYrPLJMa3ZhuZ/fxBeb/KucCai+uPz+HDOcR0xrFflfRp0Wg1u+Xt/ZKWa1bUy2c/Jucd3wtBezas8J7de2B+ZKSbY7R4UFDiRZtHjghM7qefiG8/ri1MGt4Hf64e92M2G3BGS8yTzEHLt09kqL2UmJaWyz7kTHp+UUqyzQyQiiguVz9ISNRAumQjIsuKlW0bjvz+txqw/d6v04LCMFBNOHNwG5x0vKxd9NSqXIWmzshLl0lO7oWNOBr6augFbdheV2UZKcpx3Qid0b9sIToebzbmJiChm5JIiZRzGDMhRPZ4++3UdVm46UGabNs1SceboDhjRpyUcdin5wCfleOB0SRaDDS2vfB750z6BfcV0hLx/rb7UmKxI7TkKmSMvgCeghctVvwEnuV/SaPRocckTKJj5JYqW/oags9Q9j06PlC5DkDn6QgSMKSh2MEBG1JBptRoVUFd9CGP4+CMBUbnu3XphP1Xq6LsZm1Bk9yDNZoI/GESh3YPu7RrhwpO6oHXTVLicHsQLvV6rzolch2M5aR0+J7dd2B+/tN9a5TmRbcNiFdT56/tdUpkgUppDqy4izRyNVwVFXmSmZZVc0//4BPaVMxHy/1UuUWtOQWqvY5E56nz4gxoUF7OUIhE1PJoQZ0SPIjcFBw86YnqzkZlpQ36+I+lWkSXb2CwWA3R6HQLBkJrEcXkDyEozqQCClMnw+wIxW0UiN2oWixFGkx4799qxbU+xah7XrmU6GmdYVIqty+mrtaBFsn3vSuPYEhPHFv+aNEmNu+tsspzb+hibPPibzQZ1HTpY5MbGHQXq+teqaSpymqXC7fLB6/XVWp1qfu+qz2TSw2KQeX8tXFtXIuiyQ2tJhSW3uyoF5vZpaq0nSXXGZjDoYDECRpMRrm2rEbDnQ2OywJLTHSFo4PJp4mJ1M38mE1cyjC8er7F1cW51Og2MxpJrkUGvVUELnU4Lu9OLgC8Q07JD8nUtViM0Wg3MRr1aqCYZBf5ACKFgCH6/X2W21fcMicGghdlsVL/j9TotgqEQdFotnG4fQoGgOsZYfd+qOic+n1/1hIzVOZF7D5NZD71BD4up5P1kjE6XT1VB8Hjk+beinxM9jCZDyc/JoXNid3jVfolcnio11QyjtmSOwbVlOYIeB3TWDFhyeyDo96mFCA5HcgQtkuF3dUU4toZxjaW6x4wLolJcLnlo9qkH7J7tsmC1muB0elBY6Iz5xI3ZYkRIo8EPszarCSO9tuQGbP32ArRobMOofq1htpRkgxAREcWa6rvk8qoPq0mP3u0bH17pWpDvqPeJG6qYBCU8npIHSUOzbtBqNOr7WVjsi8sHSln04fMBOrcHxiYdoW1acrzF7qCaFCOihsls1sNiNWHeijx8N2Mj1mw5CGmnlJ5ixHEDcnDayHawpZrhcnhq3GdJBestBnj8QXw/YxN+nb9NPYPJda9DqwxVxnd475YqgC+B+/qSkmJSE/RzVuzGpBmbypwTyZQ8fWQ7pKZZVFZITX/fqyCCnBNfAJNmblZNuMPnpGPrTIwf3ladk5L7hZqfExmXzWbC2m0HMXHaJixcnacCJGajDsN6t8QZo9qhRaMU1ceh9NhkkYUEV+aWc06OH5iD8SPawZZiVuck0fpxqWCMQQPPzk3wHtgBfUqWKgcp2ZSFiyfD0qYHTJnN4NFr4/L6TkRU25hxkQSrVOoTx1Y9VpsJW/OK8fB7c+GsoH9FVpoZj10zFJkpxloJXvB7l5g4tsSULGOLx9WgyXJuG9rYyhuf0ahTKz9lIkUmHmRyPlFvU5P5e8exJaZkHluyjC8er7G1eW4liACNFve+OQubd5Utm1s6Q/1f5/TB8N4tVP+E6l4TZCJeJraXrNuH5yYsgq+CcUj5xCeuHQqjThOTifpopaSaIMn991VyTiQb4vpzeqtSjnJOKpqor+r7Fj4ni9fuxXMTFpcpk1xaq6YpePzaYTBqw4v8qkeu8fIM/MzHi1QAoiISmJGSyjI2OW75OZHFfve+Mfuo8sqlz8kN5/XB0J41+zmpa3JOUm0G7P3mBTjXza9wu/RB45F57IWqtFSil+5Mht/VFeHY4hszLhIXm3MT1UM5qgNFbjzwzpwKgxZCVrvc/fpM+AIhVRKCiIgoGck1TlaPBrVarNySjyXr9yOvwIXMTKtaYSnBDCIiSq5JMJPJgHveqHiCXsik/CtfLMHS9ftKAh3VZDIbVVb70x8vrDBoIXbus+Ou12apMkaSgV/X10K9Xod7Xp9Z6TmRjJBXvliqgjAWm6na7yelqNZtL8Az/11UYdBC7Ngr52Rmjc6JBEkkaPH8hMWVBi3Et9M34fNf16nqBId/Tl6fVWHQInxOXvpsCZZt2F+jn5O6JOckNcWIfd++VGnQQhTOn4SCWV8hzVa3P5NERPGAT4JEdUxu+j74fhU83qp7ZRTavfjs17Wq7wYREVEykYd2KU+Rl+/GsxMW4bKHf8Fj/5mvJpZueWk6rn1mKn5fvBOpaWZVSoGIiJKDwaDH1IXbK52MDpPF8+99twIWs0FlYFTnWiMLx979bkVEZYR2H3Dg+1mboa/jwIXBqFelmiQrP9JzYjqUqVidcyIT/O9+G+E52e9Q5Y2re04k+LBjjx2z/twV0fZf/b5BBSOkRNSUBVGck29Lfk7CzbvjmZwT777tcKyZG9H2BXO+gSYUqPOAGhFRfWPggqi8BmUWIwwmA1wev/pTbnZjcQMkEy/yNRet2RPxPr8v2qFuUGTFCRERUUXkOiHXL7PVqFZhqj/V9QtxyWg24s/1+3Dry9Mxf2Weqld95ETJ2xOXq7ISslJTVqLW2bEZdSrbQ2qvh88lsx+JiGouPGk+aeamiPfJO+DEik0HqvV7WCa/t+4uqjSL4Ug/zd5c7UBJdWi1gNGgw/czN0d3TjYegNVqrNY52bKrKKLAUdhPc7ZU+5zIIrxvpm2MeHvJAJH30+q0UZ0TCTqt2nxQ9U6Jd2ZDCEXzJkW+Q8CPosWTYY7+201ElNDi/zc6UR2RmzBJSZXgwoylO7Bw9V64vX5kpppw/KA26NQ6A06Xt0bN2mRSae6qPUdNzlRGAh0bdxSgVWNbwtYTJCKi2g24m8wGFeT+fdF2LF23T2UyNEoz46TBuchtkaYacNdns9EjyeST3enDUx8uqLREhZCyEp/+sgZnHdsBfn/V2Yo1IedQzqU0C/1h9mZs3FmoamU3b5yCccPaIj3dAo/bp/pvEBFR9a5Z8nyzLYJV9KUtWJWHds3TqvGOGsxflRfVHnvzXThQ6IZRp0UwWLvXnXAGilPOyZ7oz0mHVunRv6Em+nOy56BTlTuuzjmxWgxYum5vVPssXrMX44a3i/qczFuZh9xmHRHvDGYLXJuXRbWPa+MSpPY7udaOiYgoHjFwQaRuoDWwpVgwdeE2fPjjajiOmNyZPG8bWmen4s6LB6BxuhlOh6da7yNZG3KjHi13BGWliIioYU4ApaSa8f3MTfjs13VHXWN+nrsVbVuk4a5LBiLNaoTL6UU8kNWXX/+2Bt4IA/I/zt6MC07qDJ9HW2tBfAmmSJbFm18vV6UppExFad/8sQEDumbj9ov6q+u52x0/gSAiokQhvz990oG6Gs9D1Wm5HKrms5QsADDV0cp9WUAn7xctKT1cnT7Usk913s8r5yTKrJdw1me07yfbVyfhRfaL99bc4XMS9EU3pxDye6CR9BwiogYkot96u3btiuqDKNFYbCWTPq9/9edRQYuw7XuKcdsr07Gv0F3tpl+yarNxhiXq/STrQ/YlIiIq/eBrSzHhs8lr8Z/vV1UYGJfyGLe8PB3FLp8qDxEPiwVMRr3KDomUw+3H9CU7VQ3w2jkmrSoL9fh/5mPyvK1HBS3CFq7eg9tfmQG9Uc++G0RE1SA9FWwWQ9ST0vI8VJ3Sh/I+sm+00mzGOnv+qu45yUg1Vat0U12eE9k8GAohIyW695Pt5VJcrXMSpyUyS58TOY86W0ZU+8n2oTrIACIiSrjAxZgxY3DcccdF/EGUSGTVSKHdgw9+WFXltrJa5+mPFsBqMVar54WUlujfpSlsUazead7YprI9vMy6ICKiIxo77trnwJdT11e5rQTln/3vIhV4r++eF1qtFgV2D5zu6DIQN++Ssk21c0xGkzRF3YpFa6ouZSFlKz74fmWdN24lIkoGgUBQTUgP6NYsqv1OGNQGoSpKC5ZHSgwe2791VBP8Pdo1gsWkr1ZmSHXIM6IcXf+u2VHtd8IxbSQqUDfnpH0jmI3VOydOpxej+7eOap/jB+VA+o5Hc07k/ubEQTkIVuPnpK55XS6k9BgR1T4pvcbAG4jzqAwRUX0ELp544onDH3feeSd0Oh2GDx+Oxx9/HG+//TaeeeYZnHzyyTCbzXjooYdifYxEtV6uYmIUzcJ27LVj3bb8ajX9kht1SekdMzAn4n1OHdYWLrePGRdERFSjZpfrtxeoQIcEPBJVdRYNVP01SxqVToqiAejvi3aofhjSu4qIiKIT8Plx5qj2EW/fq2NjpKeYqtVfSBZ/SePrY7pHHig5fVR7eL1128soFAzhjCjOSe+OTVRWgqMaJYzD52RQFMGjM0a2h7ea/Z0C/oB6ptXrNBFnTcj3y+cNqPeN5pyk2ar3cxJLJpMOGRlWZGaWfNhsR3fUlmqTaQNOAbSRLYLQpWTA1nEAXG4uZiSihiWip62zzjoLZ555pvpYsGABzjjjDLzzzjvq8yNGjMD48ePx4osv4m9/+xt++umn2j9qoliW2bAa8cfiHVHtN3n+NoSqOXkiN+qXjO2K3Aiay8lN+tihbeGr4xtnIiKKb7JKUjIGZy7bGdV+UgapvlMupCSGTLbIatZotGmeBp2+pKeHpZolG8sjJZ/y9jtUSchISVmueSvyWC6KiKga3G4/OuVkYuyQ3Cq3TU8x4ubz+8HjqX5fIb/Xh+v+1huNM8xVbjuqb0uVIe+p4z5GDocXXXOzcHIE50SuoTed31cFBGpyTq4/J8Jz0q8V+sk5qeb3QAIleq0G//e33lVuK8EN6SspC/dcLh+6yDkZ3Cbic+Ktwc9JTaWkmJCSZobVZsbqrQcx68/dWLx2HzQ6HVLSLCqYUfqchLQGND7ln1V/YZ0eTc+8FR63R91DERE1JFE/bc2aNQuvvfZaua+NHj0aX3zxRSyOi6jOVm5KFkRFfS0qUlDsgbaaEz9yk6LR+vHM9SPw7ISFql72kckUMiE1ul8rdYPtcnpqrREpERElJtXI0+OHL8rrg5RoUvUo6lFJ9qEfxw5ojR9nRZblYDXrMbx3S9z7xiw0ybTg4lO6IjPFpK6RNU1IlHsBdV6itL/QVd8xICKihCSZ5A67B1ee0QNpKSZ8M22Dyko/UsfWGbj70oEwG7RwOb01CpRIqcQXbhqFpz5cgFWbDx61jUGvxalD2+KSU7uqY6vrCeJgMAiX04erz+iJdJtRVQQor6F1+JxI6WF7sbvWz8m4YW1x8dianxO5Xo/o3RJGvQ7vfLschfajv5/ZWVbcckE/5DZPhdNRcn2XP68+s6fKuKnqnJj0Nfs5qYm0NAs0Wg0++3WtWiRSenxGvRbD+7RU9y6paRYUF7nU54scAaR3G44meiMO/Po+gs6io76uPr0pmpx+I3RN2qCwmIsZiajhiTpwkZmZiT///BPDhg076rW5c+ciOzu6uoxEtb0aVTIjZFJCbpA1Iakh6itz0yUNOXVaTYWNOMtjNGhVk7HqUit4QiHcefFAFDk8qjzFzn121UisbYt0lUprMujUDV4se1tIWQur1QhfIKQmWyRgI41d5QavNnpoyPvpDDpoZJZMU/KQIrVp6zt9l4go0cklqDpliqQ0BOpoLkayIrR6XanAQgg6jQbFxW61SvTs0R3w67ytEQVfThmSi627i1S5K/mQXhQPXzUYuc3S1KRGTci1yWSMvl9FeRkjct+h0WnV4gbJypDeGRpNoM7qpBMRxaoXkZS3Ccjzk0ajnhksVhM87tg9M0ifBZl4l5JRZx/bAb/O34YVG/era0KjDAvGDs1Fq6apcLu8MZmMdrt8MJkNeOyaocg74MSPszdjz0En9DoturTJPJzpYC+WRWP18zu7JKMhhLPHdMTfxnRUE+ArNx1Q56RxhgWnDM091PvQX6OgRUXn5IdZm7E3v3bOiTx/O+xu9O/cBMMeOAlzlu9WH3KtTLUaMWZAK/Ts0BhOp08FScLk+iljPWt0B5xzXEfsPuCATlvy/O71B5FiMahsC1eMfk6qIy3NDLmTuevfM7BpZ+FRr8txTl24XS1YfPL/hqFplhXFhS51TgqLfUhp1x9tbhgC+9p5cK6dh6DXBZ0lFSk9R8PSpjs8Lg+DFkRxqnPnzrj++uvxr3/9C4nk4osvVn9+/PHHSLrAxTnnnKMyLtxut8qwkEDG/v378fPPP+PTTz/FPffcUztHShQhmZA3WYywWgxYtm4f/li8E3anV63WHNa7harlKamncqMmNwtur1/Vw1y8tuqGnGH9u2SrIEhNyOS9fMgkxzljOpTU7VZfM6QmdIpdsbvx0ukAi9WsSlosWbsX05f8dU6G9mqhaoh6/QG4HB4EAqGYBCzkJlhiQT/N2aJ6gkhgqFmWVQVlGmdYVfDGXccp2EREyUKyFkSX3Eys2ZIf8X4Du2bXesKF9NCQCXsJ8P88dyvWbDmorgHZmVaMHZaL7Cwb/D4/9EaNWiH5xAcL4K+kkebgHs1w/gmd8eA7cw5/TlbmPvzuPLx77/EwGnU1mkiTrEYp3yirVx0RNgyXS3b/rk0PZ0TKtdxsMeJgoRvfz9p4aGJFg06tM9TEj1wT5brHAAYRxTtbilkFc1dvOYgpC7ejsNijavYP7NoMI/u2hNEXUMGEWGSEy9fw293Q63UY3a+l+ggvOJMHicICR42z6kqT38PykWE14O8ndlZBZgnSSLaDN05+R//1jGhQDaqPGyhNrTWHg/9yToLB2jknF57U+dD5l1dCMT8n8uytFsx5/OjbsTH6dGxcZoFhQb6z0qwOeWXFxgNYfijA1UQFc9qqcmL1SaPV4pkPF5QbtCityOHFfW/Oxrv3naAWd0gpLBmvZF7o3B6Y2/RDVm4fVRYz4A/CG9ThYH5JgIOIqKGKOnBx7bXXori4GO+9955qzC3kQiONuW+88UZceOGFtXGcRBFPJEhNyQ07C/DSZ0uwL78kDTNs2pKdqtnX/53dC306NYFDVo94Azh9ZLuIAxcy2X9s/1YxWeUiajv7QIIW1hQL1m7Nx8tyTgrKPyfXntVL1XN12L0IBKp/gyoTSFabCe9PWomf52w9ajLquxmb0KtDY9x5yQCVniwBJCIiip48+J82vB3WbFkUVbPLwkJnrR2TPIibzUZ88MNK/Dh7y1HZFJNmbkL3do1w1yUDYTJo1MKB524YgQk/r8GiNXtUwDusWaOSYPdJg3Px/CeLjipjIas0v5+5GacNbwvUIHBRUroqgDEDczBpxqbIG4BajSgqdKlrGbRaPPr+PCxdt6/MdrKadMIva3DiMW1w5ek9VXZIXTd8JSKKlNTn37nPgef+u0hlg5c2c+kuvDNxOS4b1w3H9m+tVs/HqpytrOavyyyH8OIpyVzMzLQhP98Rd6V5JfuiJn09olWXC8rkuhtpdoQEtVJSTfj813WYOH3jUSXF5F5D+qXcdelAWKzGOs+6SE+34GCRW2VTRCK/2IM/Fu3AyD4tgVLPwap8tTNY8jNptaDQIT+TfE4mIoq6xoBExO+8807MmTNHBS6effZZFcSYPXs2rr766lo5SL/fj5dffhnHHnss+vbtq4IjS5cuPfz66tWrcdFFF6FPnz4YM2YMPvroo1o5Dop/ZosJ67bn44G35hwVtCjdn+LJDxdgwco9sNiM6oawV8cm6NG+UUTvceHJXdTKzni7ua2I1WZRkz0Pvj3nqKBF6XPy1EcLMHdlHqy26q9Y0ek0Kmjx3IRFajKpohW0f27Yj1temq5Wp8gqVSIiip5MgA/p1ULVdo7Epad2UxmHscisK488bEvQ4qXPFuPb6ZsqLAElZS9ueXmaKlsoTTibZ1lwx8UD8MEDJ6kMjJsv6Icnrh2G1+8Yg0bpFtz12kzMXZFX7tf6ec4WWC3GkqzFGgj6Azj3+E4quFMVqVUtE3c+b0nWpE6vw60vTz8qaBHmD4TUxMozHy9Q10gpUUlEFG+kfKwELe56deZRQYswu8uHV79chp9mb1G/z4hqk1zb5efynYkr8Plv68rtgyIku/+WF6fB4w/CbK7bZ0t/MKQW5kVD+ntJ/xAiSj4FBQV44IEHMHToUPTs2RPnnnuumj8vzW63q22GDBmi5rhvvvlmfPDBB6rsVGm//fYbzjrrLPV1pF3DY489BqfzrwVo//73v3HCCSfgjz/+wPjx49GjRw+cdNJJmDhxYpmvs2vXLlXOqn///urr/Oc//znquKWq0vPPP48TTzxRfZ1+/frh8ssvV/PtYXfddRcuvfRSPPjgg+r1sWPH4oYbbsDIkSNV5mJp9957rzqWWKj2b0ubzYYmTZogLS0NvXv3htdbe5HtN954A19++SUeffRR9Q1o27YtrrzySuzduxf5+fnqZObk5OCrr77Cddddh+eee079nRoWmQgwm/R45uOFVfarkPTXlz5fom6GZD9ZAfnglYPRs33jKoMWJw/OVenRiUBu3GQi6bn/RnZOXvlsiSrtkZJSvQcRo9GAOX/uxuw/d1e5rdSUfeub5dAbGbggIqoOWZ3ndHpVbWpZbVgRmdO/6oweGNG7RUmPpVpisRgxf1WeyuSriiwueP1/y1TdZ8k8lEaV0ttJAt4yYTZ9yQ7847Ff1TW9stILsnJRSj5K4Lwm5Bj0GuCp64ajUbq50r4WD1w5WJU+lBIPeoMeb09cgd37HVW+hwRfZi7docpoERHFE3kekkDs0x8tKLf58ZHem7QCRU4fbAxeUC0/y0rVACk9XJUCu0dVFzCaDKhLcvexLa84qn225hWX9NnkQgaipOLxeNTE/pQpU1Qw4tVXX0WzZs3U/HXp4MX//d//4aefflJ9MV588UU4HA4VNCht0qRJan67Xbt2ql2DBB6+++47ta8qp3jIvn378Mgjj+CSSy5RyQWtWrVSyQYbN25Ur0ugQxb6r1u3Ts2p33///Wp+fcmSJWXe74477lDz6JKQ8P777+Puu+/G+vXrceutt5Z5v4ULF2L37t3qmOS1v/3tb9izZw/mzZtXJggi7STOPPPMmJzXaj05ffvtt+qkygmSiV8ZtER6DAaD+rzRGNsagxJlGjduHIYPH344yiPvKVkXmzdvVu8r3yi9Xo/27dtj69at6ht29tlnx/Q4KL7JRMCMZTtR7IxsUkZWgspKzRMH5aiUUpnYefSfQ7Bs/T5MnLZR/Slz/TJJMapfK9U4LivNrEpEJUqdSYPJoCZ/Ij0nMoEk9chPGdKmWu9nMuvxbRQrTmYu24VrzuqlemLEQ01ZIqJEUxKICOHp64dj0eo9qoyCZDTI/aX0bDh2QGucMbI90mzGWr9+SU3maFYdzl2xGz5fb1XH2++XHktB1XRbGljWB7kXSLcY8fbdx2PKwm0qczA8GSF1tE8e0gZjh7ZVkxTSE0quXfIfM5bsiPg95Bo5ok8rtQAilnXbiYhqwmo1YvmG/apBcyTk99e30zbi7yeVXR1KFEuyOOCbaRsi3n7p+n2qj4QE4Y4sxyyfk34mQvU08fp5HSaimJK58jVr1uCLL75QC/yFZCNII+zwAnsJYMgkv8yhS3ZDeBuZ8w4HGyRQINuPGDFC/RmWm5uLyy67DNOmTVM9p4XL5cLjjz+usjfC20i1ItlG5se/+eYblXHx/fffo0OHDmobOTbJ1AiTRAQJntx3330qi0IMGjRIZYY89dRTqq+1JC6EKyLJ/LsEZMK/T+XvkmQQPoZff/1VBUzOOOOM+glc/Pjjjyp6c9ppp6mTIVEkIYN++OGH8frrr+Omm25CLDVq1Ai///67ihI1b94cn3/+uQqOdOnSRQUw5IRK0CJs8ODBeOutt9TJbdy48hX0lDxkxcKUBdFNdvy+aAdOH9leTVa43X5VAqpTq3Tce/kgGNXKz5BK43S4vKqMhKwITTTSVC8avy/cjtNHtIv6fWQCR24UJVU3UrKydtqSHRjVpwUDF0RE1eRx++HzBtA9Nwt9OzeFXqdVWXZy/bI7S65fserLVFlvC6fbr4ImkZJjnLpoO04eXBIslwUE0iQ7GpmpJpiNejjtHsSCy+WFz+fHiF4tcMKgNoebhmq1GpXd4vf6D1+vpKfTrD93qaB/pDbuKFS1sCW7pCYNxYmIYkkqCP4W5XPUtMU7cOXpPeBySFC81g6NGiipGiCLA2RRRqQkEDF5/jacLr2vpLyIVqMWR8jiur35LmzZdVA1+G6TnYZmja3q/knKRtdkUYfsmdMsVS16jJRsL4s15IOIkocEJWSCv3v37mqCP0zmzp955hkUFhZi7ty5avH98ccff/h1CapKwECCGWLTpk3Iy8vDP//5zzJfZ+DAgUhJScGsWbMOBy6EtE0ICwcUwiWlJENCKhSFgxZC5tVL7yPz69ICQkj2hCQIbNmyRc3Di9IVljIyMg6/R/jYJbPiww8/xEMPPQSLxaKCJVIqq/R2dRq4ePPNN3H++eerAyrdwFeyGw4ePKgiS7EOXEhtLGn8fdxxx0Gn06kTI99QOfnyzezUqVOZ7Zs2bar+lPSV6gYu1IUyRsIpgMmYChhPY5PvWUGUEzPS20GOvfT3WyYk5MOr1yE11YziYtfhfhax/LmoC3qtnJPoJnMkzfbIcxLRe+m1OFgY/cSRlPmQu1L+m4sMx5aYknls1RWrf/PJfG6jHZtMuMuHTLZLEEAm3MOrCWv7+iXHmF8U/TVArlESwJDjCwWDqon1Rz+urrBH0pFOGtJG9e0oKRVVs3JRpUn/CvmQSQ9RelLjr3OpwcHC6ANChQ4PWmRZEyZ780j8N5eYknlsDWF80Yr2d75Wo4n6mUF+l5W8l/6o2taJIpl/bhJ9bDabGcWyeDDKS2XhoZ9jWVxgthhVb0XpjyElp0rr0CoD5xzXEf27ZMPj9lY7iGDQaXDaiHaYFEXG6dihuar6Q3n/ThP9+1aVZB4fx0bS30IqE0ngojzymrQ7kMn/cAZY6QX7pb+OkOSAhx9++KivI20TSpNgQVj464bLO0mwJDPz6JLCEmCRxf5hM2bMwBNPPKGCJtIaQhIFrFZrma8l5LUjSTxAYgWTJ09WiQQSwCmdKVLngQuJvEjGRXkk3SQcIYqlDRs2IDU1VdXQys7OVlkWt912G/773/+q2llHlqYymUyH64tVhzykZmYe/c2oqbS0v36Ykk08jM3rC8AUZb8Ek1Gn/hFW9v1OTa3/sdXonEgpiyjI9tLnojr/BopcZVNyI30/s9kAmzW2Jebi5eeytnBsiSmZx1bf19lkPreJMjZjNa8BkiFiPfTz4PH6MWZAa0yet7XKfaWU4/jh7dX1ozauIVWRa6XcR0RL7lWkLnysa8MH/T4g4D8Uv9FAa6y4V0dD+rmsDo4tcSX7+GrrGut0+6L+fWbUl2yfnp745zyZf24ScWw+yRSNsNTxkaRqgsGgh9Gox3fTN+LDH/9qLFvahh0FePLDBSp4ccGJnWE49PNcHY3SNRjQNRsLI8gOkUxRKeMp/36kP2cyfd+ikczj49gaLpm3llJNFU3aS/8JmdOW4IUE/EsHLw4c+CtrXXpJh/tODBo06Kivk56eHvExSdBC2ikcKRwcEdu2bVP9NCQLRKoXtW7dWi2EmzBhggpoVEW2l+OUvh3ydSUrpHRGSZ0HLiQKJHW3pBP5keTzpaNEsSBZE9LwQzqsDxgwQH1OOqpLMEOCJGaz+ajG4OGARTg6FC1ZAVdUFFl9z0hIVFL+gRcVuZIuHTCexiaNuPp0ahJVqSLZXkpbeN3euB5bdckqk76dmmD99r9+KVVFtpeaoNE2IJeHpOwsq+oDImUwInVM92bqvaTOaKwkw/euIhxbYkqWscUq2BDL62yynNtEH5vceDfOMKteEPsKIi+rOLB7tqrtkJ9f0txar9fhmrN6Iu+AQ62SrIhMsD145TGQxYrhfeuaTIoM6tYM7323MuJ90lOMaNnYhsJCZ0wyLuRnxGIEdFoNiv/8He4tKxAK+qFLzUJa35NgaNJK9UFxe4MN8ucyWhxb4kqG8dXnNTb8zDB/ZV5Uz1ESbHYeyrxIRMnwc5NsY5PjtqWY8eh78/Dk9cORk52KbXsib3496NB9xZ8bD1QYtCjtyynr0bZFOvp1agzvEX0xoukRc8fFA3DXazOxaWdhhdulWg147Jqh6t9oRfcuifp9i1Qyj49ji2+1sTj9SDJ5/8cff6h58RYtWhz+vGQjrF69WgU0ZJtXXnkFU6dOPTy5L4uppbdzmDTklq+xY8cO/OMf/yiTaSHBDKmCJBWIIiEZENLfYvny5WouXUi1JOkZ3aNHD/XfK1asUPPo0pi79NcNBy1KZ1xURJp0S48MCcBI2atwQkG9BC7kAOQkSzmmUaNGqc9JJEYGKv0tpKFILC1btgw+n+/wCS6d3TF9+nT1w3Bkmkz4vyWSVV3h0kCxJP/Aa+PrxoN4GJtW68f44W3xv6nrI54MOGNUe1X7u7Jjj4exVZcsuhw/on3JOYlwfuSMUR2g12mqNWYp13Hi4Bx8NnldRNt3yslEs0ZWFOTHLlCYLN+7qnBsiSmZxxatWJ+HZD63iTG2IDxevSrd9N+f1kS0R7uW6WjTLE31jwqPT/6Um+OHrhqMidM24qfZW8oEQmSCfnCP5rjw5C7ISjWpCbP6aq7p93vROMOKbm2zsGrzwYj2OXFQG7g8JT21akpKTKRY9SiY/Q0K505EyFd28rB48WQYm7dH9tm3w2JMQbEzdgsEEufnsno4tsSV7OOLVLTnQErjSV+fD39YBXeEv5+kT2AoGEqK853MPzeJNjaLxaj6WqzfUYCZS3filKG5eOub5RHtK8+V3ds2UiUov5wS2fOo+GrqenVvIX0vq3NPUVTkVpO6z/5rBD6dvBa/zt+KQvtfiwCNei1G9G2Ji07uihSLAcWFrqT7vkUrmcfHsTVcZ511lqoMdPnll+Oaa65RvSRmz56Nd955R/Vslt4W0qdCEgGkJYKUapI57f/9739Yu3atmlsX0iJB+kk/8MAD6u/SI6OoqEjNuUsPiopKUZXn9NNPx0cffYTrr79efU3JhnjjjTfKlHiUrydlH5999llcccUVKjng66+/VkGY0v0yKnPSSSfh0UcfxZ9//on7778fsRR14EL6V6xbt079GU5rkQ7pMhDJiJBeFLEUbuYh38RevXod/rwcg6TgSADjs88+U/025BsqpNlJ27ZtY579QfFNJgFS04yqxqRMdlRlVL9WyM60oqCgdibN40FxsRtpGVaMH9EO306vuu7m6H6t0DRLAgnVW70a8AdwxsgOqkn6vvzKb8hk8ukfp3VXTdGIiCjxyUKA00a0x2/ztyHvgLPKLL1/jO9e7kIDyfqTB6NTBrfBWcd2wLqt+Sp4IVkWXXMbqXrScr2Jh1W+sjrzivHdcddrs6rsyyEZKTKewKHm3jUhDzZpKUYc/O1DFC36qeLj270RO9+/Ay2veBYWsw0uXnOJqBzye9dg0uP8Ezvjg+9XVbm9ZGdI0LYgigw7okhotFp8P6vkufWHWZvx2DXD8NOcLdiWV3nWhcz3XXlaD5UFVOz0RbygQGzcWYid++xolGKAu5rXSVmJnpJiwrnHd8RFJ3fByk0HcLDYDavJgJ4dSvqu6rWapJ57IGropOqPlFd6/vnnVRCguLgYLVu2VFWEJCAQ9uKLL+Kpp55S20nzbennfMEFF2DixImHtznnnHNUP4l3330Xn3/+ufra/fr1U1kbUpopUtJaQRpnS/+Kxx9/XD1DnHvuueprhMtTtWnTRh3Lq6++imuvvVaVopLm3R9//LGa75cG3507d670fSTDQrI7pEdG6bn7WNCEIsn5KId0MZeGG9LoQ+p4SbqLZGCEI0SxIlEgiUxJDbAHH3xQBTLkm/n222/j008/VTXCTjnlFIwZMwZXXnmliu5I43BpYCKdzatDHpYPHoxd2QFZESdpSZIOmGzRyXgbm1p9mGrG298sx89zK66PPbx3C9zy9/5w2N2qEXcijK26rFYDTGYj3vr6zyrPya1/768au9rtnhqtknH5ArjnjVkVTlzJqpM7Lx2IHm0bwelwx3y1bLJ878rDsSWmZBlbkyapMfk6sbzOJsu5TZaxyTXY6fHjntdnYdf+8r/H0tPitov6qYaYZqO+0vFJgENKMsmfcssq21V03a4vUtJi9daDePKDBfBUcGxSSvHxa4bCZtarFZ01ZbEYoDu4Gbs/vi+i7c1teqDZeffiQIGnQf5cRopjS1zJML76vsYaDDrYUkz4/Nd1qplxZUGL+644Bn5fAI44CCA39J+bZBub/Du47JFfcKCwpPTw+Sd0wslDcvHQO3OxZXdRhQvi/nVuHwzt1VwtDli1tQCPvj8vqve99e/9MLBLUzhjcI02mXSwWEwqmCKkJLLD4U3q71ukknl8HFvDuMbW1M6dO1WZJglWSOuDsBtuuAHbt2/HN998g0TkdrtVTOD//u//cOmll9ZvxkWYpLbIh9TBknSXIzuix4p8XUljeemll3D33XerQEmnTp1UzwvJthASgZLIkQQqpDO61PyqbtCCEpv8ErUXe3DVGT1VWqlkXsxatgtef1CVP5Ja1GeM7oAOrTLUhHm8TX7UBqdqbKbB1Wf2VDd9ck5m/1n2nJw+qr0q2+SvYdBCuFxeNaHy+h3HYfqSHZg0Y5NaxSKk/4WUkho3rJ1679oIWhARUf2xF7tV8OK1O8Zg+pKd+H7mJtUAU37XS0NKKUUiWYCSPeFxeVTgojKSkeF2V69BZ12Ra1mXnEy8f/+J+HHWZvwybwv2F5RMuLRtkabGKxmNsoozFkELYTYA++d/F/H27q0r4Lfnw2RKUyuriYiOJM9Fksl27vGdMLp/K/XMIL/HpbydBI8lYCHloXp1aHxoIjaxgxaJwGjUqaoSMgEu10M577Hoj5RIPvt1HaSiyfM3jsTs5bvw46wtWLP1oLqvSLMZcdzAHIwdmqvuMeQexFRJw+u64vEE4PEws4KIyp/jvuuuu1TgQvpCyO946SUxefJkPPnkk0g0O3fuVMEWKYkliQxnn312zN+jWhkXkvohfS7kwOx2O7788ktVk0saiEgaSaJjxkXij01u7kwmA3QGHaxmA3z+IAx6LVwen1qFIZMHVf3ox+vYqksqqaWlWeELhGAx6VVJC1n16vb6odOUlJUKBEIxbeBkNOlhNklgU4NgKKRWwzicXlXiIxb1vRvK9640ji0xJcvY6ns1aDKf22Qbm0y2GE0GtYJXZUsEQ+q64HT7EAoE1YrGRB5fhRNMeh1sVqOqr63VlFz73C6fymaM1RjlPGakGrDl2QuBUORfM2PY2bAOPAPFzppdf5Pt+1Yax5a4kmF88XSNTU01Q54KLGbD4WcGlVEWDMHp9CTsOU6UnxtZBKY36NVz2uotB+H1BVXmXsfWGaqnoPQkqep443VsVUlLt+LxD+Zhydp9ZT7fskkKThmSi+MG5ahnWfk5Nxp0WLZ+H7buLsLxg3LgOBS4cPmCuPKJvxrdRuLV245Fps1Q78H9RP2+RSqZx8exxbd4ybgItzd47bXXVMNuKRXVvn171Rcj1j2j68Lu3btxxhlnqLJWklAwZMiQmL9H1OFoObEXXnih6h8hJ1XKNQmJEknNLGn0wWwHqm8Sk1ArNN0+uBweFciQzzW0FSqlBQJA/qEm2G69DgaDFikpZnV+aqsZvawslQ+ZtAqvFGKGBRFR8pPgdDhALQ87cg2Q60KpPnBJR43XG4Db5VUBm/R0KwrznTG/xqrFALKSM4qghQg4CqBBEn8DiChmZEGTcDq8MBq1SE21wGl3J+yEVaKQa6XVZsKefBc+/mmpalJd+vFVghenDmuLccPbqu+NZGAkm1AwiHHD2x0VuJAeFO9+twIf/LBKZVfIvUWxwwu7y4fnbhihFuIJCTxkZlhVD5ZI+1y0b5mOFk1SUFgQu8WrREQVkV4Q8pEMmjdvjnnzoivNF62o6zs9/fTT6NGjB3766Sfcc889h1et33fffSrNRbqVE8UTmSyXTIKGHLQ4kt8fqNMyWeHvAYMWREQNT0lfiuQOWpRW2wsl5N5bozdEvZ9Gb0IoFNtedESU3KTfJIMVdcdiM2H1lnzc+vJ0LFhVNmgh9hx04v1JK/H4f+bDajOqIHmykbLD0gNLMizKIxlA+wpc2L3foYIWkoUiZaDt9pJgm/B6/KrkWaT+dlxHeDy+hHxWlZ8Bi9UIi9Wkfn7MViPMZsPh/hpERIku6sCFNBG57LLLoNfrj2rEPXbsWGzZsiWWx0dERERERIdI5orGYIKxaZuo9rN2HIgAkm+Si4goGagSR54AnvxwgSpzXJlFa/biwx9WwRAH/RxiTQJlXq8Pj14zFI0z/mpcW57mjW146KrBh3p//PV5qbzQvW0jXHpqtyrf77zjO2Fg12x44ryf1pEk48SWYobJYsTURTvwypdL8cIni/HxT2uwv9iD9AybCmAQESW6qK90JpNJdQsvT0FBAYxGYyyOi4iIiIiSiKx3Uf0uGnjpxpqS8ycTLGkDT8X+H16PaB99elNYcrvjYL6r1o+PiIiip9Xr8M3ktSW9RCLwy9ytuPiUrmoCO9myYuzFHqSkmvHyLcdiws+r8fuiHapJfJjNYsDxA1vj7yd1gQYhtf2RmYnSaF5KarVploovp6xXvUJKk0yNv43piP5dmsJh9yTUfYlkWdhSTPj813WYOG3jUT8zP83ZosZ392WDVDaGlG4mImowgYthw4apxtz9+vVDkyZN1Ock88LhcOD999/H0KFDa+M4iYiIiCgByQO23qCDVZpWB6Rpdcnn3fXcADORub1BpPcYiaIFP8C7d2sVW2uQdfxl8LgSa2KGiKihkKC+NJyesnB7xPu4vQFMXbgdI3q1gN+ffBPT9mI3UlJMuOzUbvjHaT2wfON+2J0+pKcY0b1dY/ilgWMwBLu9bNCidHaiNOvumpOBx68Zin2FLmzdVaSazuc0S1X9QqQfhrxPIl0b5WdF+qC8/c1y/Dy34uv/+u0FuOXFaXjpllEq80L1/yQiagiBi9tvvx3nnXceTj75ZHTp0kUFLZ566ils3rxZRbZfeOGF2jlSIiIiIkqoDAupuSzTAT/M2oyf527B/oKSrN3W2ak4dVgujh/UBiaTAX5/+RMPVD5ZXet0+dH8okew+5OH4M3bXP6GGi0an3otzG37oKCYkxZERPFIp9OiyOGFwxXd7+ktecUY1gtJKxyUkDJaPdpmqbknmXPyuD1ISbEgP7/yZtoSkHC5fOojxaRHz3aNDn0+iMICZ0L2tJBzIdkjlQUtwgrsHrz82RLce/kgBi6I6smBQheWrN0Hp8cHq8mAvp2boFG6pb4PK7kDF9Ix/Ntvv8UHH3yAuXPnIicnB06nE+PGjcPll1+Opk2b1s6REhEREVHCkCaRG3cU4rH/zC9T4kFs31OMN79eji9+W4/Hrx2KDJsRAX9QTd5IwEMmG0pqVifgrEIdcbn9gFmPlpc9Dcf6hSiaPwnu7WukSAa01jSk9jwWaYPGAUYrCu3SdJTnkoiIEo9kRshHmJTHqs7XSAZ6gx7fTtsY8fZL1+9TQTEJeCTLOSBKBOu356sydfNX5iFQ6nlGp9VgUPdmOOe4jujYOrNejzFRVKubU2ZmJm6++ebYHw0RERERJTyLxYC9+S48/O5ceCupve1w+zBvRR5OHd4W3kAIKzcdUE1JmzWyokOrDLjcPvi8/qSr3x3L4IXXF4C5dS8069APGp1eoj7Q6HTwOp1w+jTwMtOCiCiuSVmjjFQzUiwG2KPIumjbPO1w+UVKfhKwkenPRWv2RLyPrFn4df42nDa8ba0eGxH9Zc7yXXj2v4vUM82RJIgxZ/luLFy9B7dfNABDejavteO4+OKLMX/+/HJfu+KKK3DnnXdWuv+8efNwySWXYMqUKWjVqhUSKnCxf/9+fPTRR+oEFBYWolGjRhgyZIg6KWlpabE/SiIiIiJKGAajHv/5fnGlQYs0mxEPXTUE/kAQT324AEvW7pVy1YdJ/WlprHnqsLZwOrwqA4OOJn1DHE6/+pDa1+GMFSZYEBElBlXSyOPHcQNz8O30yFbTS0+MY/u3htNRUoKRkp/qLevylblXikRBsSfqfYio+pkWFQUtSpPXn/3vQjx9/fBazbw45ZRTcO+99x71eYslccpVRZ1jt2bNGowdOxYffvghzGYzunXrBp1Oh7feegvjx4/Hrl27audIiYiIiCjuSTmCYqcPS9ftq3AbvU6L+684Bvvynbjn9ZlYtKZs0ELsOejEe9+txNMfLVSNKKXJN1U9+SWBDAYtiIgimwgOhkIwmAzqOmO2mmCxGlXZwroW9Adw5uj2MBkju9adNLgN/P4AMxIbECn5aDRE/7MpP1NMzCGqG1IeqqqgRZhsJ9vXJrPZjCZNmhz1kZKSgkQR9W89acQtfS5+++031efi+eefV0GMX3/9VWVbPP3007VzpERERESUEKUMpIxBZZPno/q1hM1iwHMTFsEfqHyWfd7KPEz4ebXK4iAiIooFs9WI1FQz/ly/H29PXI5nJyzC618tw6K1e5GWZlF9miSLra5I/wGzQYd7LxsEQxU9HAZ2y8YlY7upUorUsEqKWc0G5GSnRrXfMd2bqf5XRFT7jbjluSUa0gND9qsPhYWFuO+++zBixAh0795dVVKS/3a5yj+eLVu24B//+Af69++Pvn37qr+vXbv28OvFxcW4//77MXjwYLWNlJlavnx53Qculi1bhhtuuEFFaErLzs7G9ddfj9mzZ9f4oChxhG/mpCxB8o5Nk5Rjq8sbcSIialjcnkClr48d2hbfz9oc8Yqkn+ZsURkX1WnIWdfktoHXWCKi+GVLMWHHPgf++dQU3P/WbPy+aIfK/Ju5bBeem7AYlz7yCxasyoMt1Ryz3+fydSSTo7Kv53J60DknAy/eNAqDezQ/alvp/3Tl6T1w96UD4XR44PNVfq2Nljz3VnWMDW0OQqfTqExSY4SZMNF8vyvar6I5CFkQ4nb7VBnNSDVvbEPX3Cx43AxyEdW2JWv3qeznaEjPi8qy1GvTXXfdhVWrVuHVV1/FL7/8grvvvhsTJ07E559/Xu72t9xyi5r7/+qrr/Dll19Cq9WqOEA4I+yqq67C9u3bVUWmL774An369MEFF1yg3qMm9NVpzC1RlPIEAgGVhkLJTSYNDAY9zBaDSjmUSYeMDBvsDi8C/kBC16CWGwW5MTEYDWqli9cfQEaGFQ6XF0F/UN0oJCq59zGbDdDqdWqlhjTzlF8uFqsJPp9f3czIfxMREdWEXEoaZ1RcN7VV0xS0aZ6G3xduj/hrOt1+TF+yE4O7Z8Pv9yIeyaSG2QCYrFYE/T6EggFkpJnhdnvh9gZZzoOIKA5YU0zYkleMe9+YrXoslafI4cULny7Bdb4gRvZtCUexu0bPlnqDXpXrkedmo0EHp/QpCATUs2Xpxy/5u8PuQaNUI279ez94fAGs3ZqvnkmbZlrRvmU6XG4f7MXumF5TjEY9dAYtUuS50B9Qxx0KyiIEH7wev1rpX98kCCDHaTLr1WSZHKeag3DKc3pAZazEkmTjhDTSS8Sgvm/qnIRCCPiD8Hp9cJcTCJDnbZPJAJ08b1tKnrelNKZ8//xevzrG8iY1j/w5Cc9BON0+NbbSPyeSZSO9UH6YtRlb88qflyt9PFee1kP9zPA5n6j2OT3Vmy+U55zaMmnSJBWUKE2yId59910MGzYMAwcOROfOndXnpQH3f//7X6xbt67cr7Vt2zYMHToULVu2hMFgwBNPPIFNmzYhGAyqRt5Lly7F3LlzkZGRcTjQsXjxYtUjW6o31Vng4rrrrsNzzz2HnJwc9OvX7/Dn5WBffvnlw9EWSk4WiwEms0GtSvl+5iZs3lWkPp+RasKJg3IwfkQ7tYJFmmgm2sVRJvWtViMWr92Lb6dvwp8b9qkbBLNRh5F9W6mao43TLWp1SzzcvEVDVqlK3dateUWYOG0jZv+5W92o67QalWp8+sj26NwmSzV383pju3KHiIgaFlnAINcWaRwqzUaP1Cjdgv0FrnJfq8yW3YU4pls24o1MDKTZ9NAGfSha8CPylv6GgD1fvWZs2gap/U9Bes9RcHsDqoE2ERHVPVnBLs+pEjiQ3kkVBS1Ke+PrPzG4Z3MVmI72GUkm2eX9Vm06gInTN5b0cgqG1OK4Yb1a4IxR7dE6OxUO+9FBCJfLB7h86n27tZEJoJJJ84ICZ9Sreas6J/KM6PYF8M3UDfhtwTYU2ksWB7RvlY7xw9up52CZOHe7vPX6nC69R2Yt24VJMzZh7baSa2yq1YAxA3Jw+qh2SEk1q+f0mp4fvV4Hi82oJhK/m77xqHMi2Q6j+7aG0WRAUanyLrK4U85l3kGnet6esXQnPN4AJIGiT6emOH1kO/Tq2EQdY+kgS/jnZOWmA6oxe/jnxCg/J71Lfk5aNf3r50Q+PG4fnvi/YbjvzdmH52OOJM/5N57fFz07NIKj2FOjc0JEkbGaDNXbz1x75XDHjBmD2267rcznwgkHf//73zF16lR88803qgzUhg0bsGPHDrRr167cr3XzzTerYMUnn3yCQYMGqRJT48aNU8HklStXquvUscceW2Yfr9cLj6dmv4OiPjuSNiJveuGFF6pojKSJ5Ofnq0FKlOXtt99WH+ELofTCoORgsRjh9AVw08tTVcPM0gqKPfhiynp8M20j7rhoAHp3bKwurokSu5CbIYNJry7+KzYdKPOaTDRMnrdVfVxwYmf8bUxH2ItdqvllIpCbKFuKGW9+vQz/z95ZgMdVZn38Py7xNlZL2qbu7kZbSila3GXx3WVxX3aBZQ1YZFmWZVkcdpEPKZRiLdTd3VJJU0nSNjpu33NOOiGee6fJZGZyfjzzpCT3ztz73jv3Pe+R//l+VV6dsrSVW4/xa9zADrj/muGksCrBC0EQBCFkaFFN8hXTRnbB3KX7m/GdI1O6IineAHf+NhR++hwCtapB3IUHceKbf6F02afIvOZJxFsTUSHBC0EQhLAHmMk5TEFzylI/WaasgoIcyHOX7MOFk7oDKtZH5Iy2xhnx53fWsH55dSh7f+H6fH6dM74bbjq/f4MVFJVrMl+LjsnaHYV4/n91+03l5pfixQ834OP5u9lJTgmMHFBpJoLyj+QzImcXnWt9yYGk8uCHBnc9vxB5tSoMyu0edvZ/tXQffn3pYEwc3InHMtQESp0OfN1Io/6F/62vd0xe+nAjPpm/h8ckMcnCwQuqBqHAyQff7sKnP9VstEtxFEqMpNfgnml4/KbR/HsKXvB9YjXiT2+txpodBTX2c3v9nCxKLwqW/OK8n+8TCiSR/+L5uyfz/UVjsPPASf6s5HgTpo/KwvkTu8NE1T0VrqhLKBWEaGVo7zQOGpKfTSm0/ZBeNVsxNCdxcXHIzs6u83vy3992223Ys2cPBx9mzZrFfS6oR0VDUBxg5syZWLRoEVasWIG///3vePXVVzlOQO9HDb8/++yzOvsZjcbwBi4oWEGv6nTp0gWDBg06rQMRIhvK9tDotHj4xUUoKm64cQwZYn95dw2evn0cumYkwNGKmRlKodJMMhjqC1rU5n/f7+JmotNHdOFsiWiApKDe+mpbnaBFbZZvOYqXPtyAuy4fCre7ZmBKEARBENTg9fhw/Tn9sGXv8TpSBuQ4Iikpyi5SUxrdvWNixIUu4ix6eI/noeCTZwB/w+fiLSvC0fd+i863vMCOimiW1RQEQYg2zGYjZ+m3SzRzwEANtP3VM/vAYXcrDwjEmfDM+2vrBC1qQ3I/VJ142TRKjAtNjipUzBYjduYV47kP1rLDuyGOHLfh4VeW4u/3nQHDqcSE0/pcc6WUkkarwZbc47A5PEiKM2Fgj1S43F74PD9LT1Ngg6SX7n5hEQ4VlDcaYHr5442ItxgwsHt7xdeqNtY4C7btO4m/fbCuyTF55JVl+Pv9UxAfb0IAGny+cG+doEVtNu0pwh/fXIXf3TyGx5EqNJ55b22doEVtKAmE7pNLp/58n1DwguSeB3VvjxF9M7iSiAI/FESpIIUIr58TSQVBCB9UVT6qfyZWbDmqeB/anvYLNzt27MDixYu5F8XgwYP5dx6Ph+WgyMdfmxMnTuCVV17BrbfeiosuuohfBQUFmDRpElavXo1evXqhoqKC36NHjx5V+1Gz7z59+uCaa64JX+Diz3/+Mx+MzWbjagsq+3jvvfdw9OhRzJgxg8tFhNiDjAuK5DcWtAhC0cU3vtyGZ+6cwLrOkR7gJz1JMiKaCloE+e93O3H22K5sSEW6XjU5RxxuL75etk/R9os2HMYVZ/ZGktUQ1f08BEEQhNaFnA7klHjmzon423/Xc5PToD1wuKgC+4+U4ozhXdhpo4Q4sx4Th3ZCucIs2XBhNutxdMHbjQYtgvjKT6JkxReIH3MR3JGf1yEIghATcJ8/i4HXp0/cMhYlFeqSz2h7qgqg91GyriVHe15BmWLH1ReL9uKiKT24AqG5m2031fvwjTlbG3XQBzl2wo6vl+7DzDHZp3WMJPfk9gX4c5eSlFK196KgA1VqXj2zL7Q6DZwODwxGPeavzms0aBGErs2bX23Dvx6axlJKaiWjqNqCZLze+ErZmBw9YeNqHJLKpu0/nt940CLIht1FXB3Ro1MS8o6VYeVWZfcJBUZq3yekAEGJovQKNvWm6opI978IQixz6bSeWLujgJO6m4Ik4Wj71iA1NRV6vR7ffPMN2rVrh5KSEvzrX/9CUVER+/lrk5SUhIULF3Jg47777quqrqBeFwMGDOC+F3379mU5qcceewwdOnRgSSna5o033jitY9Wq3WHTpk2sWUUNO4g//vGP3PPiyy+/xA033IAFCxac1gEJkQdNghazAd+tPKh4n735JThSZGPDLdIhg4h0R5VC2aGUeUMNyiMdnV6LrxbvU2R8BaGx0OpUPxqEVoaMWLPVCEucibN36N+0IKGFiSAIQmtAjgNqRvngNcPx5m9nsNTimAEdMG5QB5RWuHDRGT3YYFfCrHHdWCs6knpMUdDCW1oEV/4uxfuUb1oAo8kInU4ezoIgCOGA7OEDR0px4GgZXB4vNz9WA8ntEEqdwZTwRz0OlEJyRPOW74fu1OeEa0wogaCp5s7VmbfiAMtFBR3kaqF9y+we3PW3hViwJq9G0IKocJDs0z7c++IiaLRaDjaRD2KuwgSHYICFkhEpMVEtiYlW5B4urSNH1RjfrDjAUaAfVh2scz6NQedJzS9IZlvdfXIA+gbuEwrU0EuCFoLQuvTskoIHrhnBgdDGoL/ff80I3r41yMjI4IbZ1OOCZKLuuusu/h359bdu3VpnewpyvP7669zPgrY555xzsHz5cm4VQT2wdTod3nzzTQ5i3H333Tj//POxZs0a/OMf/8DYsWNP61hVP9FffPFF5OTk4LLLLoPD4cCcOXNw5ZVX4ne/+x2/KEIzbdq00zooIbKgygLqaaFUCzQIaUOeM7aullokQYaXyUgVF8dV7bduZwHGDshENDizN+4pUrXPxt1FsFxsiBoprLYOl1BbKjUDqQ8LNXaj6H5asoUdfZ0zEriZnlTQCILQGpCGM73IiXDRlBzOWkWgMiOQnl+P3DAKf3xrdaNNUscO7IArz+oTdhmNpiA5Bvuetar28dvL4D5xFDpTKnw+kYsSBEFocTQarN5eKcWz73AphvZKx2YVa7+hvdNhV2FHWy0GXk+pgbLwqSdBa4yJUkh54USpE0adFn6/T/V8SdJUv3lxcZMVL/mFFXj6rdX4w23jYHN6FVVbVGfN9gJ075AItVBggKpD1VBY7GAFBupfoYaNuwthNuqx91CJqv027C7kfheCIEQ2tHb5668n4JMFe1gysHrPC+ppQfJQVGnR0kGL9957r9G/n3feefyqzSOPPMI/R48ejV27fk7QolhAsKd1fVDlBqk0NTf6UCouXnjhBda8osbb1Kj7ggsu4L9RlIYqL4TYgpwM7hBKQmmfaAj4U/am2lJSt8cPbRSkslNgRk32B0Hbh5pJI4Q/MEWN10m+7MvFudzErTrfrjyInl2S8cj1I7k0O1S9V0EQhNOFghe1MRi0GNQjFc/fPYmfY+REqT4fd0yNYwmGs8Z0hd1GDSnDI6GhFA3JMdRqxq0E2kdjbpFDEgRBEGpBGejB9dA3yw/goetG8JyjRMaDmD05B36F809weah27ewO8/qLxiSU9T1JFIVSzUDyxSs2H1EkO01QItbhonKkhqD7Hsp5Ef5AAC6P+qpOuubkGwjlGAMIwQfRBtfpdM7BJu5q/TaC0FpQUOLRG0bhRKmDg9mk3EL9/agRd2v0tIhmVM86VBZiMpn430uWLEFiYmJVY27qfWE2y0os1qDJITG+8pqroV2SOeKaaNaGJj/KAKGG29QYTCnJCSY2biIdygBJSTCrKnlNSTA1mvkqRAZkvJEk1D8/3cTarw2x51AJ7n1xMV68dzLLmjhVNMIVBEFoSWgapWaSGclm3HfVcDjdXuw+VMKOkYx2cejWMREOpwflZY6IkogKEtBooEtop3o/XVwi2x+CIAhCy0N+3nYJlT6KzXuPo6TcxdKF//t+l6Ks2ezMRJSU2BR9Fj3aae2cnGCGzVmh+BiT403cryCcY0JrPrUkxBnhDyEwYDLrVUk+EV8u3oebLxjATms1zmpap4eSX0hZ0KGMCV3zlER1+9H9QSiVy/x5v/DeJ62ujGHSQ2/Qs7wbBRrJZrQ7PPD7fKwmIKaUEA1QkGLayKzWPoyoRrWQPelVffLJJ9i4cSO+/fZbTJkyhR1o1GGc9K7o70JsQQ4EapbVp6vyMia9TovJQzuHrcFYqNBkZ7O7MWVYZ1X7zRidzZmWkQ45eqjJmRqmjugiskJRAGUubd9/otGgRRAqyX7pow0wRkHPGUEQ2mY1BgUnNH4/+mUlY3BOKgcziovtXCkWiUELwu32Ia7PWGj0lXJ9SjB1yIEuLiXi7SNBEIRYgar1zhjemR3TxLPvr8MFk3Iwe0qPRvcbMyATD1wzHDabS5WD1O5w8+ep4czRWfCHca6jMZk6vEvVmCiBKiRNBn1I85dBr1OVSEfkFZTz8Y3om6F4HwpY0Do9lLEM+AO8DlZT0TCoZyo7088cpc4pSfcHyY+NH9RJ1X70OQF/ZNpEzb3OTUqyIvdIGf763lpc+MCXuPjhubjooa/w6mebUVjqRGKSlSVHBUGIfVR/0x944AFuwHHFFVdw84077riDf3/uuefiwIED3IRDiD3cbi8umJhTo5HMhMEdWZftihm9MXNsV84UCTJ+cEc2HKJhYe7z+nDhZNLdVrZ9l4wE9OqSHBWZ626XFxMHd0KCVZnDmrI+yNjzRsF1a+tQ43U1jf+oPLHM7g6pvFsQBCFcgQCHwwPHqb48kV6VQDYOJYHG95+geJ/EkefC6ZIsQUEQhHDOLbR2HX2qPyE16X7sX8t4/ffiPZMxY3QWLKfsY0q+GzOgA/78y/F46LqRsNvcvA5WA62jzhnfDVkZ8Sx3eOWM3rj4jB4Y2jut3vUmZfmP6pcJl8sT3jEx6DC6v/KejRTs8bhDP0a1VRDB7c9T0dOBJFgSrMqTCapTXu7kzH5VYzIxhwMJAynhop1V0T4UF6H7IuDz832i1ykbmHaJZozsmwFXjCcYUtCCJI7/+PZq/Pa1FVi7o4BtLYKqLhauz8ddzy/CW3O3IT7BLMELQWgDqPZg9e/fHz/88ANyc3PRs2dPWK2VD+gnnngCw4YNQ1paWkscp9DK0ARJxt70kVnolB7P0f5yuwd7DhVz6ebgHqm49cIBWL7lKH5aewi3zx4Ir0ojrzUzPZMTLbjhnH54a+72Rrclo/aha0dwJk2kO1QIylIlBwkd8+9fX1mjKVB9xuE9Vw7j84qGgFNbh753ahrB0e26YPUhnDu+a4selyAIQlvC4QbanXkjnId2wnPySKPbWnuPRlzfsSgpk35DgiAI4cTj9uJXlwxmCVXqs5CbX4rb/jwfk4d1xrkTuuPOy4ayU5QCHMXlTk76KisNXaaQKgVeuu8MbNhVyJXPJoOOHf9Otw9fL9uPuUv38bqMAiUPU4DE6Qm7dr/H5cGvLh2MPfmVY9IYZwzvgqG90lBWpqxHRX09HbIyE7l3hVKyMhJ5jLp3SsKscV0xb/mBRrenANCvLh6MrfuOc5JhKGgRwK8vHYy9CsaEqjOocbutwskJZXQdH35laZP9JW+dPRBWkx4V5U7uVfjrS4fgxQ83NLpP1X1CMkkx3OOB/BFxcSb85d01HLBojLlL97Nv5tKpPXksBUGIXUJKvY2Pj8fgwYNr/O6ss85qrmMSIhCaIEmu4faLBmJ3XgmeeW8ta4RWJ9hE87EbR8Pj8cJRTyPOSIWaflLGg9VswLvztnNQpjZZmQl48JoRSE0yw25zIVpwOtzcGOgPt4/DSx9uQMFJe70ZHL+8eBCXANsqoufc2jIVDvUZu8UV6krdBUEQhKaTH3RaPTre+BcUfv48HPs21tlGozMgYeh0tJt2A8ptkSt9JQiCEMvParPFiBfunoy/vrsWW3KPcxDhu5UH+ZUYZ2TH98QhnXDRGZWO0FCe1dTLwWw24qP5u/H9yoMctAhCmfVUzUEVGFR98daX2/Cry4ZwZYa9FdZf9Y1JbSjgcu7Ebrj6rL7soA/VaU7STeeO76YqcMGKCAD+8OYqPHnLWO65+cXCvXzdakOS1tQra3PucfZV9OwcWuCitNSJhEQznr9rMv763hpszT1R/5hM6IZrzu7LATFK+KNXh/ZW/PXOCXju/XXIL6zb34TusV+c15+VKWynHO0OuwvjB3Xk93ztiy3cf6U2HdrH4d6rhyErPT7m1+lms4ErolZtO6Zo+88X7sVFU3pw9ZAkXgpC7CKaIYJiDEY9lm8+ihc/XF9VrledI8dteO3zLdh54CTuumIY9HoPN4dujsg7TWLUBFOr0YA/mqoC3D7W52wOqMkVGagkf0VVJUs3HcaaHQVwuX3cBOvMUdnI6ZzE8hXRFLQgyFFNgZmuGQl47eFpbJRSiWWF3QOrWY8JgztheJ90boBKxlA0VJIIlQ1t1UJGsSAIkQXJt2l0Wu4XRpmeXl+A5z16FKuQWY55dDoNjEYDGwUaLSVUgHtNkbRGa2cf2h1e+M16pF/yEPy2UpStnQdPyTFoNDqYOvZAwtAZCECLsgp3vQtruvZms77SztFqKgPMfqp+9DaLHRWNyJgIgqBUVkarr5xDCVrHkAQPOeXrS+aiwMITt4zB8RIH5i3fj4KTDg4o9O3aDmeOzuY+BxXljnqfM8FmwVXPJd4kwNK8FOSgv5lMBs66p8qO2tD8vnTTEazbWYg/3DYOf7tnMu/b3EGL4JjQMVIiocFkgE7nUzQmVA1SWFw5Jn26tmMJ4cbGRCkBDTB6QAeWU6ovia42lEyXnmKFz+/HzgPFPKa/uWwoLpyUgx/XHsL2Ayf4eFKTLZg2IgsdUuPYif3xgt04f2JlwCNUyssqgxdP3TqWx2JerTE5i8aEFSncsFdLdiQfQXqSBf+4/wzszivG/DV5KK1ww3xKfmrsoI4sNU1Bi6DdQj/JBzGkZyrefnwGO+xXbDnK140aoVMfkn7d2nGlRawHLQitXocvFu1VvD19p6gS5+yx2TXsKwpk6GndSzYjmw8BtiHIZhRXR8tDj2N6FtJCJui/o2ez71SQTxDUIoELQREWiwEFxXa8+NGGeoMW1Vm04TCXgs4amw2v9/TkEMwWAywWI/YdLsV3Kw/gRKmTm3uRMUNyVTTZk8HVHNmLXFVic8Ht9GBEn3R+6fQ6+MhIC/hRUmyL2omOjpvGye3yoEfHJOR0SuJJRKvTIuDzoaTE3uqOH0Ed8RYDOqfH15vR0xBj+mfiVOhPEIRWhh0cZiPKbC4cLSxDn+x22HuohBe6NNdRcHJQj/bsSDFbTXA7SWfb1zYd2GQLmA28oF+y6TAHCuKtBs6MJb1nCrzTHNeaczQ5I+hlMsUjfuyl0GoD0Gm1oPVZuZMc7vVrUtO5WS1G7DpYjO9X56G4rFJjm7JxyRnj8frgYjun7Ty7g2Oy8+BJ/MBj4uIxGdY7naU52uKYCIJQ1zlPzwqb04svF+Vi/5Ey/n1n6isxoTs37iXHcm1nvcvp5Ve8SY/Lp/fiOUan03JFgNPubjApjioTrBYDtu47gQVrDqG0wsUO6ZH9MjF5WCeen0ku6IX/bag3aFEdcko/8foKvPHbM7nXYnPaFUazgefIOQtzOXO9ckwSuE+EkjG54szKMQkmsjU2JmqwmAy8lqcAyWOvLsfJsoalfbJJ5eDaEfhmxX7MHNOVVQ/o+t7z4iL0ykrBzLHZHJww6LQot7vx7coDWLQ+v6oSg5qqU0AJpxm8INKSLLh6Zp9T9kWAg0F0zez2+n0cZIuQxHaXtDiWoQ6OpSaABqXH6O+kbEHr9EHd27Ofg+5J3tbvR3Gxvc0kFpL6xcY9Rar2oT6OVAFDUL8Lk6Wyx8n3qw5yxYzX50dasgVnj+vK8mPBPmpCy0AJx/RsPlxYwUGlwmI7S51RcJj64prMBv6eSBKKoAYJXAiKo9//t2CPYuf2V0v3cRMymoBDXVhSU6ZyhweP/msFG15U+ZBoNcLt9WHdzgK88/V2XHRGD24QTpkKzfXwIyOBjAea+OLjTCi222LmwcrBGRLkPjWxp6TEobjY1WxBCzLmggaaBEJaFqfLy0bavz7bomh7CnL0zk5h41cQhNaFshsNRgOe/WBtVabgvS8uwsFj5TW2W7n1KN6Zt4Pn00umkjPBVW/GZHNlR9EzHNDA7/dHRKCe5pO4eBM27TmOVz7dVEdCYfGGwyx1eOdlg9G/W/uIqBqk6+OqmmMtsBc3bENY40w4XurEg68sqxOEpozLt77azpIiJGUZqmxJtNHUmLz51bY2NyaCINQf+H/xo/VYvulIjaQ6qpj/YlEuBxTuv3o4TWnslK9NcC7l9R6vhxp/VpOywDPvr8WxEzXtaOrv+J85W/DgdSPQtUMSVmypv9cRfU7Pzkksd0QBapIzIsfqdArGNkMGctNjslfxmDQ3wSbb1Ez55vMH4rm7JuHj+bs52FBdWjop3sgqBxdP7cnVE9Qf5Oyx3Xi988//28zbUCUDvRqC7ClykDbXeqe0VH1PDw5EONQ7xslnQut0JfdkLBK8T9wev+r+KZSQSVUWZDN+8N1OfLV4H9y1xu7blQfRo3MyHrlhJKxWI/t7hOaFEo5dPj+eeGVZne8pJR+9/+1Oloy77hySnnO1meoLb9kJOPZvgt/tgNZogaXbYOgT27f2YUUVErgQmoQmT2LZ5qOK9yHnwprtBRjYvV2DGQlNRWornF489I+lGNIrjRuHUTaq3emF0aBlCacFa/LwzYoDrBd943n9OYuhtR0WbbkUkKpTKBOJjAeKqlNGJOl+khEsQYzmh7J9yLj/ZvmBOs7O2pAv8qbzB3BWsnxHBKF1MRp1/Mx84OUlLNVH/aEeemUpbA0scmm+++93u1je7/pz+rFUDskkNeccT1KQVFlJjwd6RtAz3GZ383OmNas8KIGBJDXIWdTQo4uyNv/wxio8esMo9O/enisnowGyc8hBf//fF9er102QQ4cc9dS0lRw55SE4UKIJytBTOib0k5JXYn1MBEGoiV6v47mBsvZ3HDhZ7zY0X6zedgwP/WMJnr1zEksdhTqXUaXF4eM2PPLK0jqO0CBU9UHOVmq4XXvJ071jEn5xfn9OHqJKSnJEkyyk3eXF7oPFXF12uhWD5LAlh+Fj/2p6TEhy6ZlfT4TfR5WA4ZnfWaXHH0ByvBkvfbSBK+eoKubGc/uxhDHLIlmNGJiTiu37T+K599fy3D+yXwYHpynBY96yA1UVJI2tSW+W9U7UUqnmFOBkVTW9Uml7khSjAOM/P92M+avzGtyWmq5TotCL90xhO0wqL5oPGk+PP4B7X1zcYEUVVb98sTiX+27edfkQ9t/Fsp/IdWQvipd/BvuetdTo5+c/aHWw9hyBlHEXsaSs0DQSuBCaRKvVoqjEwQ8aNew7UooB3duF9JlUQvbKZ1vwp1+OZwcNaZD+/t8rOHBBkPE3a1w3vHjvFHz4/S6Wkuqcag0pu0E4PYcXGQlHj9vw+aJcLN14mI16MhyH9EzDBZNyMLhXGmt+tlQWT1uFDHkqRabvCC3eGjLmSY/17iuGoX+3dtxUTxCE1kVn0HMggrI2KYvw8ddWNBi0qM6XS/Zh8rDOyM6IR1kjEgtqIOcPaQAvWJ2Hucv2V2W4UxXDWWOy+fisBj03jwy3D4CcUyRn+OKHG5r8bFrz/O2/6/H+kzPhoT4hEZ6hSHMkjf0zry5v0EFfnQ9/2I1JQzsj2Rq7i2yqriEH3sP/VDYm//t+F0uFxfKYCIJQF4ORNPBzG3TQV4fkhf73/U5cMrUnEELggqoQrVYDnnlpbYNBiyCpSRYsKKjpML3xvH4sabQ19zg3v16/s4DnK0oOGD+oAy6YnMMOV3L4nc4alubxzxWOCa2ZeUzO6BnWbGe7w40zhnfmjGvqUUEvkn3qnZXCUoAUyPnPnK01Ku0qZaGpX4kPf7pjPH772nI+/vrQaTW464qhGJhTWX0pRCeU8HrG8C7473c7Fe9D94lOo8H2/ScaDVoEob4jf/9oAx67cZTYD80IJUG98P7aRmXgglC11aQhndAvOzlm/Xe2natQ+MULCPjqOT+/D/Zdq+DYux7ps+9BXO/RLXIMDz/8MD7//PNGt9m1axeiAQlcCIrQnEa5X+3fUaZppRzFKedrLYd2ZRaiAzddMAArNh/BG19urZO9QlrQ9KKy1yduGYvNe4tYDxOIzQdfJELam/HxZjZAP1tYs4kWOZk27C7i1+CeaXj8F5UPYwleNC9kbNH35YV7JnMW1ZzFudh5sJgzF1K4qXwWzp3YHSa9joNHknwkCK3/3DQb9fhh9UEOQhwusjUqeVAbctb85rIhzXIs5Dgvsbnr1Zqm/yfHMD3bH7thFHpnJYfdEUDOqR9WHYRLoWOFsvPIETJhUIfT7q/V0pAdlJtfgryCxqvlqkP2zi/O649YhRpxky68ujHJxS/O69eixyUIQuRA60eqDqRqY6VQn5xrZvblHoZqpeXoWb15z3FFzaQr+Xnxe/2sflxV8Ke3V7MKQXUoGZB6QtJr1riuuOWCgWy7h7JOqhqTFfvVjwlLOocn0O/1+DBrfDd8+MMubqjclOwTrWNG9cus6sNI652/3TWJ1zuUyEFBmuB6Z/rILJxH6x1DcL0jC55ohSp9SQry4/k/3yeNQYk2I/pmcMIK2QRKoT4aZTY3S6yJf6J5eg6RHb5uR81nXWOQ32LwTWNiMnBBlRYNBi2qQX8v/PwFdLzu6RapvHjsscdw3333Vf3/hAkT8Oijj2LWrFmINio1gAShESjTIT3FymWtasjplFxlvlUaVUYkp8ShqMyFtbsKsXZXEcqdXiSnWPlvwWAGRTeonHfzniK8Pqdu0KJ2Jg1VYozp34Gz/8kpJIQHKp0mp1btoEVtNu0pwh/fWsWVGZRRKTR/8KKs1M7N3P5w2zjMefZ8fPHs+Xj3iZmYPbkHtP4AV1rEchmmIESTTNSmvUUot3swvE86ZxypgbSzWdrJcHpzHS3UKHn0kVeWNZoZRTJVf3hzFY4ct7NjJJyYjHr8tE7d+Py07hBnnkY8Wo2irMDqkIMrrrqtFGMEoOHG9GpYvCE/psdEEIS6zrHc/FJWAlAKOSdp3qX5VzUa5c8lSrqjJtJEWrIZF07JwTPvrasTtKgNNa9979sdrDYQ6pjsPVSC4yVOVWOyOdQxCRGq7qCseEq+aGo5SBUpj1w/imUSg+uX6uudp24dW2O9c9GUHGgDst6JBcgHpNdq8OtLm07SId/UI9eP5L6PFH5bv6tQ8edQbIsCeFTZK5w+Or0WC9YeatRvV5vNe49z71qqsI41SB6qqaBFENqOtm8JEhISkJaWVvVq6HfRgHxThSahCDYZAaTFrRSKfpNThowMcrIkJFqwemch7n5+Ie56fiGe+2A9nvtgHW7/64/sONmRV4y4eDMHHsiY6ZyRgPe+2aHos0giZ9GGfHhOSRQJLQ9pqWq0GnyyYI+i7anyYtfBk5xRKTQ/wWZupSV2nDhRgZJiG44fL2d5F7dbskgEIVKg4O3J0krnQrzFgFKVVQyUfUYLNJJwPB20eh0+XrAbJQo+n+bWt77exs3Ewz3PqB0f6q9liIJFKN0HSsa+dhCJekjFagIA2RS1m683hTPGx0QQhJrQV50aW6uF5t1QnhMUFFX6rKbei2eNzj7VV24gr09XblXWH/LLxbksRUWyVGqh8ypW+ewkTpa5wv7spHXJ2IEd8dC1I7hSoqHm2n/+1XhkZ8bDWatPZvX1DlVikDxUSYmNmyzLeid2oPtk/KDK+4T6VzR4n/xyPLIy4rmnJkmuqi20IRtT4lzNA429WhuOqLC7cZpLmohsxG3fvUbVPtQDg/YLN5999hnOPPNMPP300xg+fDh++ctfYtWqVejduzfy839OHqv9O6pqe/311zFt2jQMHjwYF1xwAb788ssWPVbxIgqKy/YundYTSzbmKyrbu2BSd26MRfYQBSTenrsdXy3dV++2u/KK8dQbq1gC4eyxXRFAAGt3FODEKeeOEihbZdrILDiVVvIKp4XOoMP3KiQ8iC8W7cO9Vw0DYrAcMJKQTCNBiGwspkrTi56fpOusFoNeB6cn9AU6JQjQ5/6oIrudpDJogWcOY0k9yVeoHR/a3hcFz0Ay+KmiRA1kT1EWaqxKYNB5mVVe71gfE0EQakJfdZqH1GIJMXGKGgWT/JASVm8vwK2zB3HvnSG90vCvzzYr/hxaW3+9bD/On9hdtexx5bMzhDEx6cIuIUtrlIpyJwb3SMVbj8/A6u3HsGLL0arm3NS0u2/XdrA7PE3KUwaf+/L4jz2C98mQnql4W8F9Qhn7xhAqkclmlLSH5oHl4ENY0xgN9ByKrS+xY/8mIKBSgs/vg+PAZiQMOgPhJi8vD4WFhfjiiy/gdDpx8mTTvZJeeOEFzJ07F7/73e/QvXt3rFmzBk888QTKy8tx9dVXt8hxxlh8S2gpqHKifaIZ9109nLMbGmPaKZ1Jt8sLo8nA1RANBS2q8+ZX2ziIQd/zDSpK/Qhq1CUVF+FDq9GovkYkGUUOO7lGgiC0VbxeHwb1TON5lOQuhvZKV7U/NbGkDFAqpQ8VqoKkTFCbU10AYt3OAt43XNACVe34DOudDvdpBHXCRgAY2TdD1S6DeqTB4/XFbHBaEwDrVKuBemjF8pgIglB3Du2dXdnMWSk03w7ukcb7qiYQUPxcoufQf+ZsYYmbOIuB1z1q2Li7iNdXaqHz6hPCmAwKdUxOE3JSUoUEVUwM7N4et1wwAHddNhQ3ntsPXTPiUVxsh9MR2X2qhDDfJ90av098Ph+sFgOyMiql2pQyun8mNGSQCadPIMDjqQaqmkmON7G6SyzhdztC288V2n7NAVVadOnSBT179mxyW7vdjrfffpt7ZUyZMgVZWVm4+OKLccMNN+CNN95ASyGBC0Ex1OxqWK90PHPnRDbiascvqDk26Vb+8uJBHP0mA85iNuCznxrvgVAdkh4KaCozUdVCgQshfJIOaq9RcHuRdBAEoa1CAQeDToPRAzLx3coDGNU/g6UVlUJJAVQBWR8U0KBqiqb0/ukZTLJDaiFZnnDi9/pw4eQcxdvTafP4eCLfFiBZA5JBSFAhC0KVrLRfrEIyHyRJKmMiCEJDkIOLqvEmD1UuXzxmQAfodaEF/MmpOW1EluJqsOWbj+JkeaXziWTs1EDbh9Kvh8fE78ekIerHhPpOtBbkJyDHNMlBkTQQ/2S5H3EkC7XuE0fj9wn9k5Jsz5nQTfH7dkqLR5/sdnCqTOIR6ofGv3unJPYHKmXWuG6s0BJr33mt0RLafqbQ9msOunbtqnjbvXv3wuVycdPvoUOHVr1IOurw4cNctdESiFSUoBh6qNhtTnRsZ2HNQbvLi9z8EpZlyGxvRef0BDgdHlSUO1iD0mw2IPdwKfILKxR/BjUKC/gDSElQ7sghyPiiktdyl8gQhcuIUONsIyiiHtxXEAShreL1+HD9rH64+4VF3EjwhnP64fn/rW9yv15ZKRg/uCOXz1cPQlDvIJ1ex9JDlH1OUlLkAPF6vLwgq70g8IcwxxKpSZawSjKQJFVSkhVnjcnGdysPNrn9OeO7c5ZreWnrZSwphRxvTrcXN5zTHy9/srHJ7Qd0b4+hvdNZ1ztWCY7J9ef0wz8+2dTk9gNy2mNIr9geE0EQ6uL3+nHVWX2wcusxbjLdGFTpfd2svjzvhvRZoP4RTlw5ozfemru9ye2pv2P7JCtLTKUkmmFzKl8Dc+axzx/ymFw9sw9WbWt6TKxmPa4/J/QxEYRIhJIYpo/Mwrxl+3HwWHmj21J88OYL+sek07y1oGGkgNItFw7E7/69okl/T+f0eJw9Nhv2Jp5X0Yil22BAq2P5J8VodbB0HYTWwmxufF1IVU1Bgt+ZF198kWWiamM0GlvgCKXiQgjxoVRe5oAu4Ef/rimctZGWaOaGwBQRp6AFodNpWMJJ7fufLHNixuhsVftRo69gFo4QBvwBnDkqS9UupEdZYVPftEkQBCHWspKS4oz44+3j8M7c7ejfvT3uuGhQo5mWJBH1h9vGclZ6MEOSmlcnJVuQf9yOlz7aiIse+gqXPPI1Zj/0FZ77YB0OFFQgKdkKY61MUY/Hi4z2VlVZUeT8oSqRcDe/pOy622YP4vmjMWaOycaN5/WHI4rmGJfDjcnDOuGm8/s3KqFIDvrf3zKGM1NjPfBPYzJlWOcmx2RgTip+fzONSWV1ryAIbWsONem13Ji3oQbPBFVv/fGOcTzf0j6hQPPyRz/swvRR2Ry8aCpo8eC1I/H3Dzdw8gA9y9QwfVRWHTWDlhiTp28fh0Rr6GMiCJEI+YFcTg/+/MsJnPnfWLLrvVcNR/9u7UWSrJlxOd3o1SWZE5yp/1hDZGUm8HWiNUVryNW1NPrE9rD2HKFqH9qe9osEDIbKyueKip8D7wcOHKj6NwUr9Ho9jhw5guzs7KrXokWLWCpK20Ld1qXiQggZKrmlBaPVauIHT30B61BUgagBaO/sdpxhuHXfCUX7zJ6S06B8RihYLAYYTzU6i4szwW53KyqnNZn0LNVB0NhQxmhTkXxuZmQyVDmtKPiipPkpbU+fF5Reogf/6eieq8Hl8rDjIKOdFQUn7YqOlRrOkaYqHa9kNwiC0NalFzu0t+Jvd0/G0o2Hue/Fvx6ahm9W7MePaw+htMKNvt3ascOenrW0LT3fy081q6SgRXy8Gf/4v41YsOZQjfemuYeyLulFckT3XT0MgYCrag7jknqHh2WVlGS2E2eM6ML719ahpec5zUNq5y+l0GfaKpz49aWDcd6E7vhi0V7Osg02Nh83sCNmT85Bp/R43i6adHLpOtExU0UJnccXi3OxcN0hlNs9PFdSc9cLJuVgUI9U2EkeoQ3IGagZE5vNFbZG8YIghAdaewV7KdHzgNaX9QUnKZDbLsGE1x89Ez+uzcPcZftxqKAyy7pjajxmjeuKM0dn89qQ5tvT4USZE4/8cyl+f9MYnlPnLd+PRRsOw+bwsBN0WJ8MnDO+G/p3a4cXP9qAFZuP4oFrR/DvPvxht6IqCgo2kD58WVnoFYNNjsn4rpgxKpvXi6c7Js0F2TLUWJmWsrQ2JDunuZMQKXmDPoOgz6B5I5ID3rV9EJQw2lJJIzTu9Fk1fRdUhYCohIJxpPjx/F2TsGbHMew5VJlAS99Try8Aq0mHqSOzYNLr+DsQrecZqdB4klz8YGqq/rsZmLt0H75flcdJybROoCSs8yd156Rnlolz1B88pfuRvreVPqNKH1drytqFQsq4i+DYux4BX9MBYo3eyNtHCr169YLVasW///1v3H333Th48CDeeuutqr8nJCTgiiuuwEsvvYT4+HgMGzYMq1atwrPPPovbbrutxY5LAhdCi0GVFzmdklVPoJ3SE1ji4t6rh+OeFxayA6cxLprSg5sxURXI6ZJA8hlaDffLWL2jAE6XF+2TLBjYI5WzZ0hzmwyI2sdsNhthNOm5nHhX7gk+945pcejROZnLEKlReW1DjAMPZgNPsAePlrFxSe+V0zmZAwIup5eNh9rGFRlfBqOO+4ds338ShcV2duBQ5kBCopHHrvYxNjd0THaHB49cPxIPvbK0Sb3022YP5PPNK6xodEwEQRDaCmS00/N87IBMWCxGkOuf5C9+cd4AlszRQMPNPXfsP4nDRRXcXNQab4KPnPZmA16fs6VO0KI2yzYfgUGvxZ2XDakhqUOLYNLtpuDGmu0Fjb4HVWZQM0RXtXmFFxUmPc9DJAl5uLCCn/EkZ9U+2crPd1pANkeQmhYrdOyZKWb88pLBuP+aESxRSY5smofIMVVWao/KBSjZChVlTphNelw1ozduvXBg1bmR/UFSHtSYMpKdLGEdE8rOc7e9MRGEWIfmQL1Rx8GAzXuO85qL1kJ9u7bjNQPJwNQOTNMc6tF7MXFwR67UpzmI5gF6MgQ18U83m5eeM9mZidiwqwh3/HUBJg7txLrsd1w8mAMSlFVcVGzHtysO4oX/rUdJuYuzvYN/o7n3xQ/XNzo/0Rz98PUjOSB/usH3esekSjGBegSc/pg0B7T2JYlLMnzoetuoEjXeiME90zh4QXPf6TrrKQCgN+jhcHuxYe9xXqump1jRr1vwnvJFxFhU90EEOIIDrN1ZCDuPiYkD9gaTHgGfnxMpm4PK5EcDTGY9jh63Yf+RUr5HsjskogtJfzs99fogogE6dgrGDOudgV5dUthX4vb6WdacHOducoJHePAq6qXlK1wceLhgYnde19C9Rfcc2fN0X5HNHlRoqQ7tozPoWfp2y94iFJe7uOKbngtGs4HXP9FSKWbq2APps+9B4ecvNBq80OgMSL/wbt4+UoiPj+cgxHPPPYdZs2ahT58+eOihh/CrX/2qaptHHnkEKSkpHLwoLCxEhw4d8Jvf/AY333xzix2XJiCpz3UgZ+rJk7Zmez/KHElJiUNxsS2qsgFP99xo7k1OicNdzy9EXhNag0GG9U7HIzeMZI1qi9UIh9uHv767FrvyiuvV6LxsWi/OGm2OTMv4RAtKyp14d94OrNhylBfKQRLjjGwAUpmwz+tHRUWlxjg9hK1xJnbcfPDdTmzNrVkh0qF9HEeWKXuQItDBaDGNW1y8GUs2HsZnC/fWGR9y/lxxZi/OtKWMgKCDnzJbaVyo4fm85Qc4gh2EEl6H983AdWf3RXqKpclMgua4L+ncC4odePb9tfX2MiEjlDS8B/dKw8P/WILCYgc6pMZx9UXtMWlO2up3LtqRc4t80tKUywuFa56NlbENBrNp6nln3g4s2pBfIyhMxjs1JL3unH78vL/y8W8UO+tfe3gaEsy6Gln7tLCzxhnxnzlbOSOqdlYozeGj+mXivquGn+qX4anKkIyLN2H+6jzMWbyPgyrVIacALVR6ZSXz4qWxxWEo144ysCqzM3/WWY1EQju3yvMjInlRHc7vXLjHJFaeJ23t3GLl/CJxjm3JsaWvNq0ljp6w471vdnDfp+qP9dRkMwcKLpzcg6XhGquyClb+qX1ONHZutO5y+QK46Y8/1DguklyinkpOtw9lNM9V+xsFK8aRtKLLy2u9tTuP4d+fb62xZgvSMTUO91w5DF07JDZLAl5taL5OTrZG1HeC1rEurx/vfL0DyzYdZqdykDizHlNHZOGas/twUkJDGdmNXTe6pyxxJhQVO/DuNzuwbkdBjetDPRrPHtsVF0+le8odEZV78Qlm7h1KY7J002FOoKwxJiOzcO3ZfVmqOeiDCBVKPCEbbt3OQnyyYA/25pfU+Hu3jom4ZGpPjO7fgXubtsR901LPE3oG0HfuUGE53v9mJzbsrvk8SUu2cDXWhVN68PeTKjebm1iYh1rDrqVgJgXoPvh2J68vKqp99ykIPH5QB17/WI06/t629hyrFNeRvShe/hnse9bW7Hmh1bE8FFVaRFLQIpKJmsDFF198weUqhw4dQlZWFn7961/j7LPP5r/l5+fjD3/4A9asWcNlLZdccgnuvPNO6HQ1dZ1jzdiLBJo6NzJO1u4qxN8+aLrxKD3TSJszOz2Bs0KCDzGzxYAjx234dsUBnCh1wqjXsqwGaYdy5NbpOe3MfTIY8osq8Niry+FoxIDJ6ZyEv/xqAmc90GRHk+OyLUfx8kcbahhFtZk0tBPuvmIoN1Wlr1x8ggVvfbWNy3gbg7JcyWCn/QwGLSxWE554fSW25B5vcB8an8dvGo2enZMbLQVurvuSro/VYsTuQyVYsCaPK2TMRh2G901nqQc61r9/tLGOwU4OuLtOjUlzfy/a8ncumpFzi3wi0akSK2NLz1KnN4AH/r6Y57qGSEux4JlfT8QPqw/iv9/tUvTeM8d2xQ2z+taZE8ipEQyWfLPiAHIPl/DColNqPM6Z0A3xFgPPscGFPS14ExLNLDFFklaNzee/vHgwP+cpsaAhSzNWrl19yLlFJ3Ju0UssnF8kzrEtObZURUgZ0X96e02jkkpDe6Xx2qYlEp6aOrfEJCue+2AtyxQ2BTnFX390Os97lO1PzmitToN2CWas3n4MK7Yc43VmYpwBU4Z34UB/wQk7EqxG8uo1ezZxuL8TZCMYjJUyxhqtBn6/H5pApcQw2RZmixFlDg8e+seSRhUVOqXF49nfTAR8DY9JQ+dGgbA9+SX4wxuragRF6usf9eQtYzkgFi6p5YZ8ECRH9tA/ljbaWJ2aGT9z50ToNaQ97wrdsZ9gxhcLcznhsjEuOqMHrprRBxXljmZPGmip+zIh0cKVyn95l54ngUb70fz2xtFwOt3NrlIRC/NQuM+NpdYNejz4jyWNJjvTmuTPv5qA1ERTyMGLcAcugnjLTsBxYDP8Lge0Jgs34o6UnhbRQlRIRc2ZMwePPfYYHn30UUycOBFff/017r33XmRmZmLAgAG46aab0LVrV3z44YfIy8vjbakpCJWrCK0LOTzGDuiIi6eW4dMf9zbq5CA5oe4dk9jYCxIsVWwXZ2TJAJLOCCDARlBz6VlTcIQO4Pf/XtFo0ILIzS/lIMz91wzn/Y6dtOPljzc2GrQgFm84zI2Izhnblf9/0fr8JoMWxFtzt7N0VE6HBGj1Ovz7iy2NBi0IMtKefms1Xn9kOmfUtnQzVcqGIVmrLqlW3HhOP/gCAZa92nuoFL/528I62bhBSB+Wyq/PHkt6q9IcSxCEtgtX71mNePDZnxoNWhCURfjkf1bi+bsnceUdSVM0BS3kzOaBdQIX5ACiFwUwzhqdRS3zqv5GAfqy0poZoCQPNXdpZR+OxqBAxaufbuJgf4cUS4vLFwqCIAjRBTmryLH/l3caD1oQG3YX4c2vtuHamX3DrnVODWfvu3o4Hv3nMuw5VDM7vTpUnU+Nr4N9H0mq6K4XFqGoxIGBOe05W/iGc/ux7J3P78fBo+WsSkA/SRLrj3eMj9r+AmTDUFCCpF5ojbt6ewFL+yXGmTBtRBfuS8QNxM2VY9KUDDStHZ9+cxX+eLu6MeF1r69yHdxY0IIglYTXPt+Cm87v32qBC7K96PX4a8sbDVoQpGzAY3LHeGi1VM2q/vNIHoqkuZoKWhCk7pDTKQnDeqZVJZRGMvHxJpTa3Pjre2sbDVoQVG3y9tfbcPXMPoDYpxEhFfjUm6uaVGihKgz6rrzx2Jn1ygdGMhSkSBh0RmsfRlQT8YELyk4n7azrrrsOV199Nf/ujjvuwNq1a7F69WocPnyYO5p//PHHSEpK4mYiJ06cwDPPPIPbb78dRqOxtU+hTcO9EGxOXHlmb9YZ/PSnvdhdS/ZpUM9UXD6tF3qekpWobZxwI9EW1LPTG3T4buUBbv6ohFXbjqLc5mYD9f9+3Ko4C2Hukv245Iye/G8aB6VQGefvbhrNZaNNOYuCkLzIl0v24eIzcgB3eL6n5JiirJFXPtnMElhK+HLpPs7ooABXJMtiCIIgtLQDZ9Oe4/VK7tXHgaNl2JJ7giUMP56/u8ntSS9cp61svlgfwQBGU44J6mkxZ3GuomOkR/r/LdiDu68cJgtDQRAEoQaUkPXF97uadDAH+WF1Hq6f1Y+zfsPpsOKKQ42GK+7/78c9+G7lwRpV5NRnkKoLSSLRpNdyJjA54pZvPsJBC4Lm6wf+vqTBz9hx4CTyC8o5k/h017w6HcmyUL+P0NZVVC1RKbsVqFeHvja0La3/flqXj7fnboOtmiQlQYEMkjB+4JoRSG9nRZnCTGmqxCHJn9QE5WOi02u5mqCp3otBflp3CL84rz8HD1qj+S8lQZI01PESp+Ixofsks50V5eXqJaMo+eSTH5u2GYPQ/U7qCVSZEOkBNeoP8vnCvTVkthqDJFKvndWvUg4uAuTC2ir0HSAp8Y27ixRtT8laC9fnsxyfJL62LSI+cLF//34OTpx33nk1fv/GG2/wzyeeeAL9+/fnoEWQMWPGoKKiAjt27MDgwYPDfsxCTci4JDmgQTntMbJfBj+caNIlw4h0FCkbw+P2wFZOQYvwz4rUAOib5QcUb0+HOHfpPlw2vRcbpUopqXAhr6CcHUgNVSHUx+a9RdxIivT+lE7GxPerDuKas/tyQ9Vw6ULTp6zYomJMyl1Yu6MAA7q1a7aGY4IgCNEGNan8ask+VfuQfOL15/RTFLhISTDDc5pNKGlxsXFPUZMVIdVZue0YS5a0llNAEARBiDw4EG7Ss8SsUsgZ/eO6Q5g0uGPYHVacYOXz4/wJ3XD59F6chBdsHEvVEtQX0e/1VcmXkIP4awWV9dWZs2Qfbj6/f8gSTVRtQM2WaX1N8y6tb+0qkgbIgUvBpHjqQeH28TUiRQQKGjSW3UxyX9+uPMj9shqC1v6PvroMT906Fr+6ZDCe/2/TEtIEJeHddF5/5ZWrFiPLaCqF1tW0Xj5zZJdWsVH0ep3q+4TG5OYLBqj+LLq+J0od2Hmgbt/Qhth/pIyltNvHG2r0SIs0SE7baNBxIEoppLLx09pDmDKskwQuWhOthhNZ1TBv2X5MHdHltHpdCNFHVAQuCLvdzpJQ27dvR+fOnbnqYurUqTh27BhLRlUnPT2dfx49ejTkwAU5YZsLMiaq/4wl1JwbGT30SrLokdK9XY2KjOoZIuGEj1sD7qGhhkOFFZyF0lQpYn2TpNJG5dUDJWRAqt2PSk4dTg87jOrrAdLc9yV9DkmYqB0TMoqohFq+c8qQc4tOYvncQqW5vvOxMLbkYCCJPTXQ9u0TzYq2nTayC0shnM6Yk1OAntdqoDm+4KQdmSnmehMTYuHaNYScW3Qi5xa9xPr5qSWS7Wo6tnK7u052flMcPFoGDO7YKudGcxitY6lpdFZ6PLIzSCs9ALfr516LweMiJ6raOZ2S+qgJrdpzqwxYGDiZ7svF+7DrlLIBBVXOGN4Zs6f0QHK8iSWv6ktko0AH7U+yXXMW7OZEuaAKATUNP29i91N9Jb3c0Lj22s/u9OKNLxsOWlQPEvztv+vw70fO5EbstGZsikONjEnt60Y/7S5PkzJUtaGkQqI57yml0GceKlCezEhQFQrZY2qPl7bPa0TqrCEOHi1FRt8M6PX+iH2e0HegrMLN96IayLdCQcdIflZGEi1xbvRe9OxTQ/C5QAGrSK8EEtpQ4IIqJ4iHHnqIG3Lff//9+O677/DLX/4Sb731FpxOJxITE2vsYzKZ+KfLFXrjImo809wkJloQq0TzufkDAc4oqe/BZzLoMHZgB2S0s/J9Qdk1VFEQcnjlNB6udIzq99E0eW2a89qdVGksMppKg4OyZJqbaL4vm0LOLTqJ5XNr7Xk2mseWnAlqn/H0fFcCyVhMH5UFs1GPOGoAGiK0uAsFOs64ODPi4mLz2jWFnFt0IucWvcT6+cXKWrY0lAbDGg1L/MbHVa71I/m+UTpH/7wDuKG1mutGza8p+HPfS4ux73BpnWQ56oP1zYoDuPn8AZg5tisHVGpDSgArthzFix+ur5N8RrKU1MuRZB//9MvxSE60sMMwiMvtxcff7VTsPCRJJKq0nzmmKwcvmkKjYEyqXzdbCLKUmlNVry1xTyn9fHXb038I6futDcGZQPcxSZ/RK5K/c85q8m2KoftLE/nPykijWa8b9WINcf2TnNz8102IXCI+cGEwGPgnVVvMnj2b/923b1+uvKDAhdlshttd01kaDFhYrdaQHQhlZXY0ZySRvuBlZY56M9+jmVg4t8QkKzqnJ9TIjKH+FZdN68VZqsdLncjNL2GnDTUXu+WCARyhpyivQa9VJd9kNevRo3OyquOjZ7PZqEO3Dj/LoSkhOcHE2TYlJfYGM12b89rR+6WlWFWPCTX+ogym8mbsYxIL92VDyLlFJ7Fybs1l3DfnPBsLY2u2GpGdmYhjJ5SPSVZGQpV+dkOQTPX9Vw9niYviYnWVhfU1dezRWd08pNdpOPBfXu6oV2YiFq5dQ8i5RSdybtFLLJxfJM6xLTG23Bch3owEq0Fxj0Gie8dE+L1+FNtObz5r6fsmLt6MrMwEbgCtlOzMBHg9PtgrlDlgSanAYjXjsVeXNVoNSUvA1+dsRbzFgNH9M7lCJIjRZMCuQyV4/r/ruC9VQxw9YcPDryzFy/edAYfdVTWf0/26aL2yvobVe15cOr2nosBFVkZig2NS+7oFk/VSEkycaKgUqippDhspFKg3SHaHRGzbp/w+ofuKfBJqj5eqY2hftXTrmASbzcVN5yP1O0cVE0lxJr7HqYGzmmtPgh/Nee1jYR4K57kZTAZe/1DTeKXQfUzPIFpbqKUlglRCeIj4wEVGRgb/pKbb1enRowcWLlyIUaNGYffumvrOhYWFNfYNhZZo+kVf8HA2Ewsn0XxuNBHPGtcVr32+hf+fnCx/uG0c8gvL8dQbq7hhWnU6pcXjwsk5bGhceWYvvPvNTkWf0y7RjC4ZCVx0QQ6nYGlqUwzrnc4T8rRRWXhr7jbFTezOGp0Nu8PdpGZnc107eg+9UY8JgztygzalY0LnR8GVlujDEc33ZVPIuUUnsXxuamnucYjmsfV5fCzHsGrbMcX7XDC5e1VggOSYapOWbGEt6X7d2sFWcfo9pPx+Nwb2SEVaikWRxAMxZmAHDp40Vb4fzdeuKeTcohM5t+gl1s8vVtayVBFA1YCfL8xVtH2l9FEX2CqcEX9uJKl03oTuqgIXF0zKYQe60mMwGI1YvCFfsYTjO/N2YPKwznA6KiWjKDGO+h6QzJOSJRjZGXOX7cPZY7Lh9XpPNfBWXzlD28ebKxNTm4LW202NSfXrRuves8Zk48MflDWgNuq1OHN0NlwOd6s8M3xeP84Z301V4ILuE6p5UXu8tH1KshX9u7dX/Hk9uyQjs70VpZwEiWanOX0QOoOeE07nLN6nOJl0yqnvQ6Q/TyKN5jw3nc6H8yd2V9Xn79wJ3bj3TqyOr1A/ES++Ro234+LisGnTphq/p2BFVlYWRo4cydUXQUkpYuXKlbxPnz59WuGIhWiDsv3JcCb9zziLgRuHURnrH96sG7QgqLH2K/+3Ca/830ZcOKUHuneqKVXWmPFFPSfoQXvJtJ6K9iGj8tJpPeFx+RDwB9i4UjoZkxOMdFjDCTWmu2RqT3amKWH2lMoxqR20IEPaYjVywzdrnImzkUlOShAEIRahxoADurfn7C8lUOVery4pSDAb8NrD0/D07eNw5qgsljakhdsTN4/B649NR++sZHbynG7QgqDntMPhwUVTeijanpwal03tFfZ5SBAEQYh8aM1w4eQeXFWuhJljszkZKxqcVW63h6sbyOmrBKro75AaB1e1aogmq/FNBnyloqntyTIn1uwo4OpJwmw2sLyUmh6KJDtlsRh4fg/aFSRHqQba3ulu2i4Y1DOVx4/WzUohe+PcCd15HawEWlfT+ro1GnMTTqcb405JUithcM80ZLaPQ3m5M+SAGjWXVwr5IMg+jYo+Av4AP0+U3o+zxnWD1+eXxtytDI0/JbKO6Kss4Tw12YyJQzo3awWQEB1EfOCCpKBuvvlmvPLKK5g7dy7y8vLw6quvYtmyZbjxxhsxffp0pKWl4e6778bOnTsxf/58PP/88/jFL34Bo7H5tfiE2IMnZH8Af7h9HGZPzsGJUiden7OlyUl6wZpDrB36yPWjoDuVddIQ5FCi6DAZDC6nB+MGdsTFU3s0aZTecdEgdO+YxIYNlfbedH5/DO9T2Xy+IWgB8LubxkCv1XAz1nBCTq3UJAvuvXK4ojGhLJPqjd5MZj2Skq2wuf347w+78ez76/DsB+s4G8uv0SAhySIBDEEQYg4KCtjtbg6cN7WA7ZgahyduGcPbOxxurljL6ZCAG8/th99cNgQ3ndcfvbskoaTYDofd3awLTnpezxidjVljuza6HTk17rliKDJUOh0EQRCEtrP+Mum1+O0vRnNPwcagIMC1Z/fjZLNowOejQL8bT98+np1yTUlEPXbjKM78Vjpfa7Vadrrm5tfsa9EU63YUoCrso9FwIEMNVG1J62SSjKFjpeSzob0aX5fWZmTfDG4w3RiUxPHYDaPYhlEDrXtp/fv7m8Y0GRCj9TStq6tLZ4Ubj8fPPUb+eMe4Ju8TGpNHbxgJryf07wDZY327tsMvzuvf5LZXn9UHQ3uns98iGiA5qziLnu8bqqRpDEryueqsPvCG2U8i1A89+x68dgRympCjpSRjeqbSfRxrMlxC02gCzZGGFwaon8X777+PgoIC5OTk4M477+SgBXHw4EE8+eSTWLt2LZKSknDJJZfw32lSDwX6Ipw82XxadyTzQ3pqpJ8XDVkiSiHHOuklJiVZUVJi48k3molPNHO7K3KUr1Yo10FSHK8/dibyjpbivW92Yt3OghrltmRkUKXFpCGdeEINZnTQPUGVBOt3FuL/ftqLo8crkJJg5kbhlBHTs0sKZ0SQnrjd5qqqSKisRDDhm+X7MXfpftYcDULN0sYNqpyIk+OMrEHa2Le7pe5LcljRMVJlyv++31XvmFClxYTBnfjcgmNipgwenQ5/fW8NNuwqqvu+GmDikE74zeVDeYJrLEMiVr9zhJxbdBIr55aWpl4ft6Xn2VgZW4IyGTU6Hf73/U7MX3OoRqNJ0gKn6sArzuzNWYXOEJpQNgd6vQ5x8SYs33KEg8rVG4LSc5oWulfM6I3sjIQa81d9cwX1ikpMtMbEtWvN+5LGkvS9yaRvCdnF5jg3Or6gtAg1lI3U1UcsPU/a0rnFyvlF4hzbkmNLa0laM5wsd+G/3+3Eyq1HazSIpkA9VZCT/I/d5m6RLNuWvG+oepweyR98vws/rT3E8ljVnXAzRmfh0um9OCCjZk6nY9YbDbjqd9+oOh6S2rr1wv5wO70wmvT4fk0eflxziPsCUEBCCf96aCriTXq+FlS1cfiEHQ+8vETRvpQN/8GTM7kXIvW4IGnh+sbksum9OFGiscSHxq4brbFLbW588N1OLN9M99TPf6cqDqrKoIx7WitHQsY99bogKWj6DtQ/Jtk8Jn6fDxWhNLWv1RvFGmdmVYmP5+/G1lqyURTYoEoLkga1V7haxEHccj4IwBpPcqZ2vvarth7jfiBBOqfH49zx3XDW2K4ckKEEoOYmFuah1jg3epaYzAb834978O2KAzX61FAQkmS9yMdl0GlUBzRbYo4Vwk/UBC7CSbQYe60FnY/BqOcHDN0+dAdRQ2ab3cPOlGgt3aIgjNagx7W//0aR1meQP9w2Fv26tuPeFQ6XD7mHS/geolLOjmnxPB7kaKfsm+rQAt5qNUGj1cBo1FUGfjSVAQhy5pNxYrd76kh8VBqreljNBuw/WsaTM2Uq9cpK4fek0muqfGjN+5JLmM0G6A16Nr5yD5fymFAZNI0JGedURh0cE6qiMJoNuO+lxTWapNcHyak8ddtYVJQ3rHEba9+56si5RSexcm6R6FSJlbENQs9DnUEHk1GPnQdPosLu4aBFn+x2cLq93A+jtedZmmtogUFSFRRAP1JUwYvh7p2SWaKBstioUrA+C5PmBi0F780GuDw+GHRaXliSk6L6vBDttPR9SYEAs1nP86zRoOOsTfpJY+rzkMOH5B0CrX5ufD/rtYizGuH2+NmxoNVoOFOXnHWR9p2NtedJWzm3WDm/SJxjwzG2lWsGHQct9hwq5udYeoqVk53oWUHzQ0tl2Ibj3GhOp/XdroPFHCRIjjfyus3l9sHjpjWRusxvmm8Tk6y49JGvazjlG6Iy8SGbE8eoibHb64NBT+NNQeQAjHod9h8txbxlB7BoQz4fV0P896mzeY6htSqt95JT4vDnd9YoSvqjylAKQpEdo9PreO2988BJTtJISjChd1YKHE4vVxU0Jd/U1HVjSSu9ju2QXQdP8j2VlmxFt46JETn/xMeb2PNOigVk+9kcXiQnGFkWlIIafq+X/QLNZj9YDDw/l5S7cPBYGY8T9eJMTTKzZBkFdFoqEaKlv3OJiWYENBr2rezKK2b7iCqAu2Ym8r+pyqalVCliYR5qrXOj5wHNAxazAbvzivnepH9TMM3rI9vWd9qBRglcRC+iuSKowmIxcobGgjV5nPEfbDCdGGfE9JFZOH9Sd87EpKyYaIuJkSPm2HGbqqAFQdmm5EyfvzqPAwlkmFIJ7bqdhUiKN2LmmG4wW4w1xoQd+xYjPP4A5i7OxXcrD3KlRTAThCSUzhrTFWYL6kSVaaLwet1wOTzISDIjM9kCIACvu2kjL1xw+TAFTxweDsr06VJZ+kcGUEmxrY4ziyapd77e3mTQgqDMkK+XHcC0EZ15HARBEGIJDkq4vXA7PSwBRQFuu92FsjJHxJRGc78Lu5uD8kkWPVK6pvDv6fjKS+tv3E3zImVBFpU48MWivVi04XCVc4QaiJ8/MQejB2RyBly0SBO0FjSvUsYkOXy+WJyLtduPse1CdgxJqlClJzXVpKbsrWUXkGPEGmeEy+vHFwtz8cPqgyitcP+c9TihG6aPJM1672llzwmCEP1wZr3Tw46rXqfkQmjNVFxsj7r1ZHW4Ut5ixJbc4+zYD5zKHqYkhKWbjnCzbArOBALqntUU4CcH7PjBHbFofX6j2w7MSWWJoX1HSvHaZ1u4qoWSBagAblifDMwa1xUDclJ5Ppk1vitn9j/5n5X1rsmo7wQFyMvsldnQdGlonnn42hH4y3trGw1eXDmjN1c6UOIZ2zIuLztCu2UmVFUMlpbamy15oTKJr/o9pYnYe4ruE0oK3bz3ONacuk+oOsVF98nGI5gyvDOyMxOhd5MP4PTndDp/mnfpRUkyfbOSf16nl9gR7ZSd8qlQ8Gpg93Y1zi0cValCaNAzkF60BuicGocuafF8r9ptDSerCm0HCVwIqspdbU4v7npxEQqLazomymxufLZwL75csg/3Xz0Mw3qnn2oIiuhCE8IuGg1+Wp+PVz7dXO/fP1mwl8eE5DPsp8bEEmfC3vxSPP3mqjoNyo6dsOONL7fh0x/3cjUHZT7Ut6inB3k0aIdXZjT4Gq/gMejw49pDit9z3rL9OI96htTT2FsQBCEWoIU9ZRZR4IJ+RkrQojo0nynJfiKHOkkhfPbTXi7fr832/Sf5RRmoNO9RMN7ljM7qzZaG5ksKAD33wVqWwKgOzYcrthzlF5XVk7Qi2WLhDl5QcgYd4+bc43jmvbUsC1Kd/MIK/OuzLSwJ8Kc7xiPBSvKWErwQhLZO0HEVC1A2OyWpPf3WKk5mqw9aN88ck41bZw9CRblDlXOOKuypN2NjgQvKVH78ptH49xdbOMGuxv4BYO2OAn5RwPu+q4fj2ffX8jxMz+UHXl7Ma9LqXDgphytEqhOsAH3k+pHYfbC4Mpi+o4Cf+/EWAyYN7cTBdJJErgpa1EjG87fpe6rqPnmz8fuEeovdfOGARhUHQqG1K3hbEgpeKVGhECILsmWjwcclhJeIb84tRE52n0arxcP/XFonaFEdKjl95v112HOoBGazMeoekqSl2lRT6dpQpkxjlQLBMdmbXzkmlHlDY/jUf1bWCVpUp6TChUdfXQaXx89yELEKGWzUB6O6nmdTkDQJjTk5cARBEITIhhbl3686WG/QojpUGv74ays4WBPshyDUhAICf/9oY52gRW0Wrs/Ha59v5qSTcEO2zoFjZfjLO2vqBC2qc7zEiYdfWYYASDIzdu0cQRDaFhS8jYszceC2IWd0kG9XHsR732yH2WpS9Rnk2KPqNepHUB80hz5wzXC8O297naBFbVZtO4aXPtyA+64ajk9/3IMlmw7jN5cNrbHNtJFZGNIzrd5kBXJ+lxTbkZUeh3uvHIbP/noe5jx7Pv739Cxcd3ZfxBl1KI+gqtFIu0/+quA+mbfiAD74difbU4IgCG0NCVwIiiAdys8X7uVFppIAwJtzt7GmJ03I0QJlY1DQgqQqlEKyTgNy2mPR+sNNj8lX22AiPWqjDm/P3c56lU1Rbvfgwx92sUZnrEIVKycU3Ff1BXZoX0EQBCFyIYkockq//23jQYvqwYs1249xDw2hJpTEcLzEwUEJJVCwiOwIkqEIJ2TrvPnlthpNMRuCZDI//WkPdAZZkgiCEBvQGpj6BlBAQAlfLt7HQV5KFFQKVTzabS5ccWZv/OK8/oirleQ2ql8mb0NV6kpYtvkIjhy3YcrwLvjfdzvRKzsFWRkJLFl02bSe+NUlg2CzuRqsdGf5IYeHAxTHj1ewDv7x4+V8jJHQADtS7xPqLaKkPwgxZ3Eu94GRxD1BENoaskoQmoQyNqgxznerDireJze/FIcKy2EyRZfjgZqLXzatl+JMz9mTe2DV1mNV/SmaGhPajhqQbdjdeFZFdchBodfpWFIpFuEm5SFUlFhMesnIFQRBiHAoaLF4Qz7PfUqZs3ifZODXg1anw+eLchVvT04rcnSEM/mBHDEkL0INMZXyw+o8bvRODWcFQRDa2rOagrxfL9vPiYJqIMkgkg6aMSoL7z05E3ddPpRlAscN6oCrZ/bBtysOqOrdOG/5fu55QQHvpRsP496rhuH9J2fi4jN68ucolVyiIAYFOKJOMjrM0Nz8hYr7hIIWdI2oN6QgCEJbIjY9oUKzQg7zgpN2lJRXNuJSyprtBVHnWKay2w6pcbjr8iFNHjs1lpw4tJPiLFLiRKkTG3cXqTLkSE5q3+ES6GO46mJI73RuEqcUyirq1pEarYlFLAiCEMkENMCGXcqD9cS2fSe4UiPabIiWxmzSY/OeIlX7ULNPaqYaLrQ6Deubq4H6pB0pqoBOF7t2jiAIbQerxaD6Wb1pTxHPe2oh+SWqaigvc2JMvwzceuEA3HnJEHRob8WmvcdVvRcdc1ZmIism0PFktLPCYXNxryRpjtv8WM10n6i7Rhv3FEEbwn0iCIIQzUg6m9AkJMfjDqGpFe0TbW5lCiiQgTZ2QAd0TI1nmab1uwprBBpyOifhoik9MHpABzz26jIcLqqo970S44xITjBxxglVWthPNRp1hlAu64zgpmKnC42PUa/F8L4ZHOxSwtQRWbA5PfIAEwRBiHA00HCvplCcMZVygNFmSbQc5NRye32qbTG1vbtOu2F7SDajHxprixySIAhC2Aiq2KpdO9P2DQXrDQYtz4c+X6DBPhH0e4fDXfX/xmRrCMfg5/lCr9NWPscDlVUdQsvdJ64QrpEkdQiC0NYQv5+gyLGcFK+uYRjRPskMTZSeL2WWZKXH49EbRqHC7sG+I6X8e6rGoL4WHrcPDocXuw7WlEIgQ2JUvwzMGtcNg3umccNp+p1ep2Ht0HiLAWkp6lfm7RLMXHYbi9B5nSx34ZqZfbFl7/FGG5YTKQkmzD6jBzc9D6czRhAEQQjtGU/PbTWYjTquEojVeS9UyAmVkmBW1G8sCCVQNNYgu7nRajRs/6klKcEo11sQhKiHHmO0ZkxJNMPmrD+5raFnNUkBBaF+F0aTobKfAQUQ/H6YDLrKBLhAABUVjSshUCCD5t4DR6HqGFxuL88ZNNfIMzkM90mCif0FSqHtpcm5IAhtDakzE5qE9CzjLAb07dpO8T4GvRaThnZWrIUZaXDGoMvDjhPqk9anSxL6ZScjyaJHaYmdm5PFWfTo0zWlap8EqwF/umM8br1wILbkHsf1T36HK347D5c9+jXufG4hSsvdSG9nxZBeaUiMNyo+ls7p8fxyN+HQj1aoYRsFhKiC4vc3j+F7rSHSki34w23jsPdQMQdzYnVMBEEQYgUNAjhrTLaqfUij22Z3N9gEtK3idnsxbUQXVftMH5kFr8oqjdM9xgmDO7GDTSk9uyQjOd4UtTajIAhCdewON84Yru5ZfeaoLAT8lQ7p+AQz4uLMWL7lKB74+xLMfugrXPrI17yufGfeDpTaPUhIsjTa/9Dn9WP6qCxVx3DG8M5Yfar6/azR2ZULYqFF75OpI0K4T6oFuARBENoCErgQFOF2eXHh5BzF208Y3JFLIGNhEUrn4HB4+EVO9qANR4GNCyf1qMoOffLWcazTfPtfFuCTBXtQUi0ThuSk/vPlVtz25wUoLLbj9zeNUfz554zvBofTE7NZL5Q14nL7sG5HASocHrz+6HT84rz+6JQWz30vqGIlp1MSfn3pYPzzwanYtv8EisucVWNC9xk1A7VajfyifwuCIAiR0zsqOzMR3TtRXyJlXDi5B/y+6LcfmhsPBS5GZcFiUlYwTYkAk4Z2YhsunDZTwB/gz1XKBZNyathXgiAI0YzX4+Mm1yS5pIR2iWaM6pcJl9PDQQuSD/rN8wvx4ocbsDe/pGo7kh2mJt63/Xk+flxzCNY4c4N9MSiIPG5QRyQpTJYj2d7po7Ixb9l+lkXunJHAa12hhe+T8d1U3Scj+mbIdREEoc0hgQtBETRBjuyXgcnDOje5LWXP3zZ7ELzu8C2UWwNaZI/qn4HJQzvhyhm9ubT2mffWwN2IJAP1unjklWXc7OySqT2b/IzhfdJx1piu7KyIZXweL66c0Qcffr8LT7+5Cu2SzHj5/jMw57kLMOfZ8/GXX01gh8aD/1iC1duO4czRXdnYs1iMSE6JQ1GZE6t2FGD1jgKcqHAjIcHCUlKV+uiCIAhCq/aOcrrx0LUjGq2oC3L9rL4sNeQ81RdK+BnSGqfX/VcPb1LjmqQUH75uBNsq4ZaV8Hq8uOWCgVwtqiTRZdygDnCLI0YQhBiBArj0iL77iqFVvQwaUyl45PqRsDs8LAtFgYhH/rkMhwrKG9yHihFf/Wwzr4nMVmPDiWEuLx65flSTjnE61t9cMRT5heU4cLSM52unwy3B5DDcJzoa+8uHKLtPbhgJu9Mj1aiCILQ5pMeFoKLvgwt3XT4EqUlmzF26v04zKZpwh/ZKxwPXDOdMSTKW2sSYXDEUXm8AT76xsoY2aUNQJcYnC3bj6pl9cPS4DSu2HGEDtDpkYE4f2QW3zh4Ih90V843RSPJJq/VwgIKyi57/YB3+rtvAklpUNU2VLASVPN964QC4nG5Y40xYvuUIPv1xLxvZ1aEKjcum9+LAj7/CJVqggiAIrYjT4UGi1YTn75qEZ95bi9zDpXW2IbnFa8/ui6kjsrjPlFA/TrsLA3Pas7TiPz7ZiKJiR51tqBfXby4fipyOSbDbwj+WZP9R4sBzv5mEv32wDmt3FtRxgFF2L/UDu+6cfnyM1HRWEAQhVnDY3RjdPxOPXj+KgwyUvFYbqi6/96ph6JQWB3uFi6stvl91EPmFynpjvP31Nowb3BEuvbbetSIFhEmK7093jMMLH27gdWdtUpPNHGjOykzAa59twfN3T0KCxcDHL7Q8NM5jB3SA+bqR+NfnW+q9TygJ4N4rh6HjqftEEAShraEJxKr+zGlATs6TJ+tO7KFC+pMpKXEoLrZFnAOajs1g1HPUgRoqcgQ/UJmhUV80n7Y3mY3QaDVYsCYPOw+chM8f4AoCWoBSOSpVZ7hiIFNS6XUjeaIyhxe3/nm+4vemrNP3npgJv98Pu8vLZbn5RRV8Dbp3TMLMsV05+4VKhuuT26JMy/h4E+iwKGBEV4oSNXweH8tyNOf5hROTSc+N6BzBMTled0yo+sRsMeL9b3fg84W5VfsmxhlZ93PMgA6Itxrh8fr4Hu6SEY/yMmfMZKdE4nVrLuTcIp+0tISIm2djZWzbwrmRlB89v/MLyvHdqoMoLndyL4ThfTIwflBHON1euBzN29uCMlj11G9Bo6mcL2kYA36u6GhJE7glrx1LJFqMsFgM2LznOJZsPAybw4N4K0lDdcaA7u1ZTrGlMmaVnhvN6SazAeV2D8ubHDth40qQXlkpOHN0No+/uwE7p7WIte9cWzm3WDm/SJxjwzW2tZ/VNA9oAgFe17Skt0LNudH6y2QyVK6btRr46cACAV6b1LcvBXDNFgM/q9ftKOSeFfRspjUL9cDonZ3CfQ4ouK/VAsnJcfj1cz8pDlwQT946Fv2yU1Be7qzx7NXotNBq6aXhZ63JqMOO/Sfx07p8VNjdsJgNGDswk+df+n+q9KDEOVqb+r20nlS2lq8cE/IlaBWNSXMRC9/3hu+TI3A4vXyfUA8MmjOpIofm9Fgglq5dbeTc2sYcK4Qfqbhoo+h0GpgsRuh1OixYm4d1Owu5z0ByvJGz2gfmpNY7QbJMQYUTer0OU4Z2wpShnaHTa+EjJ7HPj7LSupl/sQ4Z19SfQQ3kYDhUUIbUBBO0AeD8id1r/N3janghHxdvgsmox668Yny38iBOlDo5c3Fgj1R27icY9ZxhE40VL3TM9DIa9Th/Uv1jYrEasWLL0aqgBTlBbjyvP84e2xU7DpzENysOVI3JgJz2yEyNgzXezFmq0TrJCoIgxALkgKLkhtREE66e0ZuTIMhZQlWaZWWOZq2Oo/cl5z59BmWwbtpdxFKOpBFNzcL7VHMYRaX8lt3NyQ09OyWiR6ckdnxw3ycEUFJij4hgfXBOJ6fWRdQnrZoUBgWoIilgIQhC68BJcRYjP9e+XXEAW/edgMfrR2qyhW176rfgiIBnNUkyWS0GbNpznJP3Sivc3OOQeg6Qc5meZ7Rurv7spWdy8FndLzsZfbNTajyri4vtVQF0g0EPm9OrKmhBrN9ZgN5dkmsEi8vsbny1aC8OHi1DAAF0SU/gXkIUKOnaIZG3peOgtRIFLeYs3of9R0o56EBVIOdN6I7UZCucTjr2hteTNMdWjkkRFqw9VDUmJC9NgZn6xkSo35dA14PGKS3Fwn4BsmFo3UoSUTx38jbSL10QhLaJBC7aIDQRUinqvOX78d/vdnF2e3UWbTjMFRQPXTcCHdvHwW6rW5Lo9fr4RcZmfFwcituwU5gMCGcIi2+S2iIjhRtsKUygiE8042SpE396eynyammfrtlRgPe/2YELp/Tgnhv8GVEYvAg2lKtvTIKNuP/vxz38/5TZ8/D1I/l+vfuFRXX0YGlMPvh2J2ZP6YHLz+yFinJnm71PBUEQImbOPFUZyDYEZ281r6Qfv2+CmecKkhOsLW3507pDLL3wyA2j0C7BBEc9dk40QE4ORxQEXqLVFhEEoWWhKgtKyHr/2534ask+DlhUhwIE5Gh/9IZRSLAaW02+iI4xr7ACz763FoW15PlWbTuGN7/ahutm9cWMUdmoqCDpO7/qZzWtaVzeUNaTlZ9lMuu5GuT5/67Hiq1Hazi4N+wqwpdL9rGE7kPXjuSqQ0o8fPGjDVi68XANyWLaliShB/dM4zWW2aypt5rfGm/CoYIKPPP+2jqShTQmb3y5jXtWUXVdRTklJojHvT5YRSHBjCUbj+A/X27l5MbaUN8vkhTr0SmZpRUleCEIQltDmnO3Qag3wKc/7WGDonbQIkjBSTse/sdSHD1h59JFoXHSki2q90mJN6vKQCGjubjMhfteWlwnaBGEskk/nr8b//58C1cmNNW8M9ogg5yCE8GeFlef1Qcd2sfhkVeWNtjEjsbko/m7+X6PizdLw25BEIQYhh7x1jgz3p67nZMzagctglBW6/0vLea+U2LnCIIghBdao9Ca9J+fbsZnP+2tE7QIQjb/fS8tgs3l4+SlcENVBRS0ePSfy+oELYLQevq1z7fgy6W5fE6hQI79eIv6tVsKVe9rNTCbjXjs1eUsR9WQY5sUFl75v03QG/T4/b9XYPGGmkGL6lAVxQN/Xwy9Uc+V8LXHhIIW1ES8vj5LwTGhng0UkKI5Wagful+oWuWljzbUG7QgSEngd6+twF/MHiUAAGxaSURBVJ78Eh57QRCEtoYELtoYVEJKTZ9oMd8U5PClJppWkloQZ2+j1QHUV8FC+p4KoebRVALt8SjPQqQS4uf/t57LiJuC5JL2HCrhPhixBBnmVPZMUCnyuRO64eWPNyoaE9LX3ne4FGazFJoJgiDEKuTYIilGyi5tCnKsPPf+Onb4iJkjCIIQ3jUpSbzOX53X5LbUJ+fFD9ezDFLYgysWA6+HGwqsVOfdeTtQZnNX9nwIpdocAYzql6l4H5q3ZozOZtnczxfuZRnhphg9IBNzl+5jSa6moAD/23O3VfYeqWdMvAoqJd+Ztx3l9tDGJNYxGnVwef0c9GoK6in67Ptr2cYhyW9BEIS2hAQu2hg6vQ5fLPq5oXFTHD1hw/b9J8XZ2wjc98PrwxnDOyve57yJ3eF0KW82l5Bg5mux62DTBmmQOYtyEYhBT0zwlKYM64wjRTZFRnqQzxft5e+AIAiCEJto9Tp8rsLOyT1cys3CueGqIAiCEBYo61/NmnRr7glOvgunA5w+iyoPGqoqqA2t6+YszuV5KCQCAVxI/YAUMrRXOjdwJtknSlpTUp0xZkAmy0UrhZp5c+N06rVwakw27i5CUUmYxiSGoTXp3CX7FCswUA+RlVuP1qmAEQRBiHUkcNGGIIdvnNWIxRvyVe03f01eTDrAmxOvx4cbz+2P7MyEJrcdO7ADJg3tDLcKzWdfIKAoI6m2vihl4JCxGSuQZmz3jkn872F9MrB4o7p7edXWY2x4U58XQRAEIbagTFCTQYflm4+q2u+HNXk/R8UFQRCEFiXoBKfG0mqgtZBGG0YbXqPF/DWHVO1C8kvx1tCq+CoqXOjZJZmbMzdFu0Qz7r5iKAL+APbklbCcUFMM6pnGwfpjJ+yKj4kqE9fuKPjZWa7VYv5adWvSRacxJrEM+WUWqfTL/LjmEDS62FnbC4IgKEG8d20Iknvy+f2KZHWqU1rhglYsjSbLe+n17G8mcSZLfcNl1Gu50uKBa0ZwYy01jUgpW6VMZfNQKil1uHwx1eeCxjgzNY6N+niLgTNPQhkTuZ0FQRBiD5rvnG6fIvmK2nYOZF4QBEEI25rU7vQ02F+hsWd1IMxzSqnK9Vdw+1Bklinz3ulw44Zz++O6s/vC2oDiQf/u7fHCPZNZNtfj8XGvJiXEWQwoU7l2Io6XOqrOh8ZE7XvwHBvimMQqNBQ0HmrXsnStRSpKEIS2htSZtTF0Wi30Og28PuVmn8mog1+pplEbxuX0cIThvquHc3OtecsO4MiJCug0GvTokswapIStwslGplpMIZSFUrDE5VX/WZEK3YYupxeXTesFp9vLBrtajAYt7OrWIIIgCEKUzBGGU5m8aqC5RMwcQRCE8BAIBEKSu6G1UDhdtrT+Nas8Tqr6C55jKLhcXt6X+vhdMDkHP607xDJZ1GOD+iOePa4r0lOoT6IPFeVO7nlgUXiMLreP1/VqsZoNVedDwRW17xEcw1DHJBYJDgXZH1TVomYslUpLCYIgxAoSuGhDcBaHy8t6mGt2KC/NpSZhEtdXbmzSi4zx2ZO7V2UF0fi5nZ6QAhaEXqvBqL4ZmLdMuSZpr6wU6HValHOzt9jB5fJgaO807D1UgqG90zFvedOarkF6Z6WwfJaaahdBEAQhOgg+2/t1a8f9uZQyun8HaMOaxysIgtC2n9UJRj26dkjEgaNlivfjqvZwPqv9AYzom47V244p3mVY73Q4nMr7GNaH2+3jF/WTmDSkE8YP6siLSQ00PFeVFP8s9UR9Fnt3TeFgAgUmGiM3vwS3zx6IOLNesQIDFe4P75POPR2ZQAAj+2ZgzXblvgRat53umMQidocHw/qkY4EKOTK6H/2yjhUEoY0hUlFtDK/Hy9kbSqGS0klDO6nqxyBUSho57G44T73o36EGLQiHw40hvdORlmLhJmzUSyMrM4HlkhrivInd2JiNxQCcrcKFnC7JGNE3A2nJFsX7nj+pO1dsCIIgCLEJ2SvnT1Ru55BO+PC+6Zx0IAiCILQ85MB2Oj0soauUDqlx6Nu1HZxhtOM9Hi+mjciCRUVDcGqu7Wum9RfNS1RV4bC54KhwwV7h5D4Y1aGAAgWCJg/t3OT7UZBo/9EyTB2ZpfgYhvfN4IoOWtsSHrcXU1WPSY9mG5NYgsbkwknK7RWqKJ05tiv31hQEQWhLSOCijUEGEOliUjaIEq4/py9nb0iGeutCRqnH68NffjUB7z4xE3/99UQ8e+dEvP/kTDx6wygM6ZVWo28DVRZMGNyJFwWxCN2PtnInZyPdcuEART0raEzGDepYZXgLgiAIsYfb7cHoAZns4FLCzecPgMPhEekFQRCEMEL2+BnDOyOnc5KirP9bLxzAfTHCKTcUXH9de3ZfRduP7JeBnlkpYQ2uEH6vH1fP7IOkeGOT2/6w6iCumN6Lg/ZNQTJGN57bnwM41cfE6/PhmrP7KDq2Uf0zuTdhuMckWlQEOqXHY/LQToq2v3x6L/55OsmQgiAI0YgELtoYtDC321x49MZRjQYvyBFMhsrU4V3gcqhv4iU0H3QtrHEmeDx+/Lj2EG56+ntc8dt5uPyxebjlz/NxqKAc9141jAMYpKvaOzsFT902lhcEsWzYBO/lIb3ScdflQ1kCqiH6dK0cE7vdJUE4QRCEGMbnq5wbnrx1LCdqNAQ1GP31pYPZ0cQ9qgRBEISwQfa40+HBH28fz47thqDejPddNRz9u7VvlTUpVc6fNSYb181qPHhB8kkPXzeSq8LD3cuBEtWMOg3+/MsJjQYkqGr/nAnduN8fJcNRJX9jqgt/uH0c2iWY+DpVh67DzDFdcW0TwQuaXx++dkSrjEk0QENC9spdVwzDhMEdG9320mk9cdEZPfh+FARBaGtoAjKL1GtInTxpa7b30+u1SEmJQ3Gx7Wd9yFaGNDPJGb7zwEnMWZyLDbuK4PL4kBxvwuRhnXDBpBwkWI2w25zsBGiucyNHQVyciX8Gx5qcyf7IGJaIvG50nQ4cK8eT/1nZYPMukox6/KbRyGhnRXKCiaUyapcSR+r5nS50L1msJm5CPnfJPixYewgny5zc32NgTioumNQdg3um8X0WC9k+sXLd6kPOLfJJS0uIuHk2Vsa2uc6NeizRfgSZeBTEbmwej6bzMxh0/KKAPtkNlAXa0H5k55C9sXnvcbZz6KfX50dKgpmzfM+flFPZFNNGNkjzj4/cl9GJnFv0EgvnF4lzbEuPLTWXtlqNWL+rEHMW78P2/Sf4Wd0+yYzpI7Nw7oTuMOi07OBtCbeFknPT6bS8HiutcPExLtqQjzKbm/tKDO+dzvJQVGlBDvrmruyuPaeTekJDc5bFauTtF67Px9fL9uPg0TLuCNI5PR6zxnXl8aSkNpIwtliMMJkNWLIxH3OX7se+w6W8bcfUOG4APmNUNvwBqnB3tdiYqJnTY/H7HsRo1CEuzox9R0rxxaJcrNl+DE63DwlWAyso0FjS94HGMhYS8GLp2tVGzq1tzLFC+JHARZQbe6eDRqOByayHwaCH2aRnY4h+V2F3w+/1KdJ7VnpuBoMWJrMRRoMOuYdLcfBYGTes7tklhUskyYhyOtwRNT6RcN0sFgOKbR7c8+KiJhuukdbo3++bgkSLHjabOyrOrzkhQ11n0CHe+nOZtIuqTtyVDdNjRQYk1q5bdeTcIp9IdKrEytie7rnRfKE36DnAvXlvES9601OsGNgjlbMxSRM50qrwlJ4fObZ0eh00Wg027Cpk5whJYgztlQ6f3w+fp36bhQLbJpMBBqMOJuPPetwVVH3n8beodKDcl9GJnFv0EgvnF4lzbDjGtvJZrYfBaGDHd5AKm4v7AJA0bEuh5tzoGLX6yrVGcN1Mc67P4+XkqOZ0q9CcbjDqUW73YGvucbi9fk5QG9C9PTe69rh99fYypPOh/WjepCQuguZJkkSkNVH1c6Tgg9FEfgBDVXCEtj163IbCk3Z0TItHSqIZHhedn7ve5tr1jonTw9etoTHhOd2g4/fbsLsQNoeHkyeH9k5nO8WnwA8RC9/36lDwJmjrWMyGqrG0kV/GVzmWsUKsXbvqyLlFNhK4iF6Ud1USYg6aEKn0k14VGg1PmPS75g5lkdPAYjXgp3WH8PnCXOQXVtT4e4/Oybhsek8M65PBmY+R5lhpTXQGPd77ZnOTQQuCDOcPvt2JOy4ehLYIO6DcXjjtLuj1OiQnWzk7K1onVkEQBCXQ3G2JM6Go2IF3523Eup0FqB6nJQf/WaOzcfmZvdnx4IqyxS9lhXoDAbwxZyuWbMhn500QcnCRNvT15/Tj7Ry1ZEQoYE2/czgqkzVays4RBEEQQqfyWe3hV/BZHYkJR+xMJye+3cXHSXNJc+eA0vtSJUN+UQXe+2YHNu0pqjFnkRQUVU+QbBBVTtR28HMPCq+b/0bVDLQeKit11LseogCXz+tHwBTAvGX7MH/tIRw4UgZPtW0pUEL9LLp3TOJ1Ve3ronZMqCrE6fHj7TmbsGzzUa6uqfqbSc8VkTSnU6JCbYmqWIaGjO5/wAOX043k5DiUlNhYKloQBKGtIz0uBIYMDDJEmnsxT5UWFLR4/YutePnjTXWCFsTe/BL86e01+HJRLjtftFq5LYNlo26PD6u2HVO8z7LNR/gakqHaVmmJRYQgCEKkQlJ5e/NLcd9Li7FmR82gBVFa4cbHC/bg0VeXwUyVj9UqDyIds8UAm9uHu55fhAVr8moELQgK6n+/Kg93P78ILp+fsxXDbecIgiAIzUfwWR3J0DxSOZ8oO06qbCCHPa1z4xLM/JOC7UHp5OpY44zYnHscD/1jCTburhm0IEgO9/1vd+LJ/6zi92xszdfU8VGiFwVJ/vLuWrz62RbsySupEbQgtu47gUdfXc7rUbI3TmdM6JyLK9y46/mFWLThcI2gRTAJb97yA7j7hUUInFKGaIsEh1DsFUEQhErEQyy0KCQPtWDNIXyz4kCT2777zQ5syz2BuLifpX7aMjqdDtv2nVBlvJOxuTuvmA1kQRAEIfYD3F5/AE+/uaqOU782uw4W4x+fbGRZiGiAHDqke/6715azo6Yxikoc+N2/V/D2lPEpCIIgCBExj8WZkJBoxsptBXjxww3441ur8Y9PNmHrgZNcDUHBh+C0RbJLFQ4vnnlvLbxN9KaiSow3vtwGg6nhgH1TGEx6/O+7nVjdRJIcrUXp2E+UORtNEGgMWpvSvo+/tpzlHhuDpKr+8OYqWC2mqrERBEEQ2i7i3RRaDKqcIKcKNZlSyqc/7YFWnO4MGWpOBX1GauN0e8VxIwiC0AYgLeS5S/dxPwslLN5wGB5fgOfmSIccONTP4shxZTrtecfKsW3/CZjbaIamIAiC0LJBCHK+00vJMou2j08wY/nWo7j299/ipY82cNXC5r3HuUKeAhi/ePoH7D1cCmu8md+T5vTPF+2tU/XQEPNX50Gn1XDlhFroPEhucZ6C5ELC5w/wOp2OMRQoaWLppsM4Udp4IkKQnQeKkVdQxpLTgiAIQttGPMRCi0GVE1RyeriorjxUQ5AxR1kYlDXZ1qFS29Rki+r92idZRCpJEAQhxmEdbKsR3608qHgfcjx8u+IAtDpdVPR4mrt0v6p9vlqyn5uECoIgCEJzBdFJ2imlXRxXT1AwghrUUqVEsKF1fdC2C9Yewt8/2ghbA72lqJrw96+v5IpIej/6rB/XHlJ8bC6Pj7c3hJCMQJ+1fPMRboytlCUbDkOno0CJNqTP+3q5ujn9yyX7oZGERkEQhDaPpKUJLQYZNvuPlqneL7+wHH2yUtDWoWZn/bu3R0qCCcXlLkX7pKdYkNM5CSXF9hY/PkEQBKH1oGxOt9uneH4IcqigHIiCojyTQVdvX6zGoEQJo0EHZTUagiAIgtBYk2wjHG4f5izYzdUN5fZKJ3/n9HicM74bzhyVBY/bB4fDXbdPodeP1z7f0uTnkAzTcx+swzu/P4uDCPYGghwNcbCgPKReCKREfOBouap96Jwo2GI1qA8mGPQ65BdUqPYJ6CVwIQiC0OaRmUBoUULxjYjM0c+GrMPpwcyxXRXvM2tcNzgcnohvaicIgiC0DjzFxugUwacWo+cmCIIghG+etMabsGF3EW7+43x8vjC3KmhBUFCdghK3/3UByhwerpaoT8ZR6XqM1AZWbD4CQwiVDKczpYe25A59na7282h7mdIFQRAECVwILYbPF0CPLsmq9tFqgKzMBPh8yvS6Yx2vx4dLp/ZE367tmtx2UI9UnD+pOzxu9X0xBEEQhOiCHCJUXZCmUlKwW8ekqGh26XJ70bVDoqp9sjokwO2ROVAQBEEIHbPFiNz8Ujzz/jp4fQ33mzhe4sQj/1yGADQwGn8WsiAZx0UbDqv6TJKV0mo0SIxTJ5fcvVMSr5/VQvvkdEpStY/FpEf7JDOv8dXi9viQrXJO75qZCF8j4y8IgiC0DSRwIbQYNpuTnQ5qHA9De6fDajbAXi2rpS3j8VSWHz99+zicMbwLN2CrjV6nwYzRWXji5jFw2N3wKmzoJgiCIEQv1MvI7nCrqsqjbM4Zo7M5KB7p+Lw+nD+xu6p9LpiYA7/MgYIgCEKIUGCf+jG8NXeboooJkk76bOFe6E5VS9D+FIAoq1An41ha4YZGq8H0kVmqAglThnUOKWnN5fJg9IAOSIpXHiiZTJ/l8YUUTHC7vKrn9PMn5SAggQtBEIQ2jwQuhBbD7690vF8yraei7cknf9n0XpWim0KNXhcUkLjj4kF494mZuGZmH0we2omNx+tn9cV7T8zEzecPgN3u4m0FQRCEtgEFIGaN64p4i0HR9uQQIacKzc3R0uepW0dlyQ+9slK4ypOcMYIgCIIQCiaTAUeKbNhzqETxPj+sPgiz2cD9HYNyhaZqFRhKMBt1HCi5cHIOBySUQH02aD4PJWmNqiacLi8H/JVg1GtxyRk94A9RFYHm5lH9MtEhNU7R9kN7pSGzvRVOp8zpgiAIbR0JXAgtitPhxtgBHXA5BSQagRwpt180mEtWKyocYTu+aMHt9qKizIGA14tzxnXFbbMH4rYLB+CsMdnw0t/KndykVRAEQWg7VAYgAnjq1rGwmht3dAzrnY5bLhwAd5Q4AciBY7e78dSt49ChfeOODmqUGqw6lB4XgiAIQqhotRqs3Vmgulri6HEbdDod/7/T7cWwPumq3iO4PQUIHr9pNEzGyvdqiHEDO+Cqs3pzJUOoeFweXDglB1NHdGl0O2qQ/fD1IxFvNcCpsnl4jTnd4cYfbx+H1GRzo9tSwgJ9Hs3pgiAIgqAuFUAQVEIZIGR0UCVFz6wUfPbTHmzff7JGwGJor3T+e4/OSbDbXFyp0ZAhSS9ySoRD79J4ymDU63URI7/EwQkJUAiCIAincNjc6Jgah5fvOwP//X4nlmw4DHe1OYuyG88d3w1nj+sGh90VFdUWQSjT0mIx4MV7J+OjH3Zj/po8bmIahCQuzhyVzTaE1+NtNDNTp9OyzUHOE6UNUwVBEITw0trP6sCpfgxqoX00Fj0fu9mo52qI+avzFMs4UjIaSQI77R7kdEzES/dMxn+/34X1uwqRkmDmbcpsLlhMBpw3oRumj8ridbPXG/qcTutbW4ULv750MPdK/GJRLg4cLav6Ox3P6AGZuHJGb6QnW/jzGkLJOt15qpH5S/eegQ9/2IUf1+TBVi0Q0i7RzONw8dSenGQhSgKCIAgCIYELISzVAn6/H4Nz2nPG54lSB/ILy6HRaNCtQxJnb/h9flSUO+oNWlDprVavZUONMlgo64O0Q50uD2eZNGcQw2TSwWQ2QqutLEayOz2wxplgMOq5l0RZmR3SN1wQBEGIJMiZYDEbcOsFA3Hb7EHYk1cMp8eH1CQzsjMT4XB6eI6NlCC8GhwOD4zGAC6d1hPXnt0Xuw8Vo8LuQYLVwAkRFKxwO931Vh2SA8xo0rP0B/UEoSar5FByuDzcC0MkKARBEFofvV4Lg0HPaz5/9We108PSRKFm+YcCSRe3T7Ko3i85wcSBFnLc00+qFKT+EwvX5ze5L81vtL6ll8li5J+Z7eNw52VD+N8k6+TzV45JZVAlgPIyZ7OsgSmZobzMgdF9M1iGmCpHCk7YOHGPGn8b9Br4PD4OcNSGgzS8TtexNJbL7eUAC40BrdFJHqp28IkSGqmHyFUzeuPGc/thd14xbA4vJyKQ3CPN+Q5bdCVZCIIgCC2LBC6EsEDOkvJyJ/87wWrEwO7tqzIySkvs9e5DOqHWODMHOj5flItF6/PhPOWYyOmchPMmdMekoZ1Zjqo5nA+JiWbo9Dqs3HoUXy7ehx0HKitDyACbMLgjLpzcg+Uo3C4PG1WCIAiCEClUzoMeGAw6dMtMqMpYLS62s9M+2hMg4PZyBmaX1Dg+NzqlslJHg44bcqZQZueSjYfx1ZJ9VXrlJL8xaUgnzJ7SA2mnMkjDUcUpCIIg1P+sNlsM7OCnZ/X+I5UZ/9TngRzpsyfncMVBZVV+y89lFASfOKQTXvtsc43qxcbo0zWF17fBNS1JIm3YXYRfXzaE599FGw43GrS4+Iye+HjBbnyxMBcuj4+D9BdM6o7FGw5j7tKaY0LBkNlTcpAcb2q2MaHAiIPX024kWfRIyUquXKd7vKiw+xoMNsXFm5F3rIzX6cs3H+WAE0H9qej4R/bL5GOsXTnB/++iOV2LrLT4qjm9pNguFZGCIAhCHSRwIYQd0qxuCio1JWPomxUH8OZX2+poVufml+LFDzfgs4V78ac7xrPBS+WnoZKUZIYvoMFjryzFroPFNf7m8frx07p8fl08tQeuPqsvG6HhzP4RBEEQBCVQlmKsZipSgEFJkIEcYdBqce+Li6ocPkFcbh9+WJ3Hr+tn9cV5E7tznyhxlgiCIIQXWr9RbOA3f1uI/MKKGn9zuLz4dsUBfL/yAG65cCBLI9nC8Kym+dNoCmDSsM6KpZ6owTUH2E/h9fgwsn8m60796tIhmDW+G+Yt24/V2wtYPYCCHMGkOKoefODlJdh3uJT3vf6cfhxcv/uFRfWOCa2Nv1t5ALdeOBDTmnlMaL2tRJ6JqhnjE8x4e+52fLlkX52/b9t3gl8UwHjiljEcmKhv3ax0ThdaHvK9UCVMdbkvkeoSBCFSiKrm3Pv378fQoUPx2WefVf1ux44duOaaazBkyBBMnToV7777bqseo9A8mMwGLNl0BG98WTdoUZ28Y+V49J/L2ElBmR+h9rKgZmq/+/fyOkGL2nz6417MWbQXeqMhpM8SBEEQBKHlIIeK2WLEo68uqxO0qM0783ZwUgJJcwiCIAjhg9ZtRpMBj/xzWR0HfXXIJ//a51uwausxXh+GA+qZRIEBqrRvCqoKoT4QrmrV/9R3giSe/vruGtz4h++xbNMRXDGjDz7+0zn48rkL8MFTZ+Oqs/qg4KQN1z/5XVXQond2CmaN64rf/XtFk2Pyr8+3YM228I1JdcxWI/7vxz31Bi2qQ8GLJ/+zElariR3iQuRBkmBUnZqcbMXeI2VYvOkoVu0ogNMbQGKShfuMUeBJEAShNYmaiguPx4P7778fdvvPskLFxcW48cYbOWDx5JNPYuPGjfwzLi4OF198caserxA6ZNiQVvd783Yo2j6voBw/rc3HuIGZ8HqbruaojcVixNqdhdh5oPGgRZCPF+zB+ZNyeCIXyShBEARBiByMRj2Wbjxco8FoY7z/7Q7O5CUZKsn8FARBCA/UP3D+qoM4XNSwg74678zbjglDpnOAoKWrLijTXKPV4G93TcLz/12P1duP1UmkI9nBc8d3w9Uz+3D/h+rHRAl1+4+UYs2OAv5/cvDTK85igMWkY+mrP/9qAp59f10NOSpq6E3VgErH5K2vt2Pc4PCMSRCSo6Q+lf+3YI+i7bfmnsDWfSeQ0yFB1s0RBlVYUNCCEjOpkudkWaWsd5BBPVNx7cy+yMpIYMmvaJcdFQQheomawMXLL7+M+PiaWQ8ff/wxDAYDnnrqKej1euTk5ODgwYP497//LYGLKJ9EydCrPXk2xldL92HqyC7c76K+OZWMLP0pQ4sMUTbu/KdKIDUafLkkV/FnUZnugjV5OGN4F0AMMEEQBEGIGExmfZNZoNUprXBjxZajGNqjPRwOCVwIgiC0NJTBTUlqc5ftV7xPUbEDm3YXoVfnpLA4wEmC2GQK4P5rhsPm8GDesgM4dtIGnVaL3tnJmDYyC36fn6UGqZdjdahZ9ReL6q4t6X3oNXtyDw6wl9l+TrgjyajxgzqybJaqMdlzHL060ZioT94LBVpPf7fyoOL+H8ScRbl48NoRsm6OIMg3QkGLJ/+zCpv2FNW7zeY9x/Fw7lK+dkN6ptbboF0QBCEcREXgYs2aNfjoo4/wxRdfYMqUKVW/X7t2LUaNGsVBiyBjxozBa6+9huPHjyM1NTXkzwxVdqgh2YLqP2OJlji3gEaD9TsLVe1DmZVu0iQ16mtkTNJxUUYPWcjfrzyIDbsL4fb40S7RhBmjs1l7k2IYSqstgmzcXcSNwZvzPgk3cl9GJ3Ju0Uksn1uoNNfzM5bHNpbPrSXOL6jNvDe/shG3UtbvKuRFudh+ypBzi05i+dzawvmpJZKfZ/Q+Nqe3UTmk+li3sxC9uiSH7dxoTemwuWAy6DB7SncKuXDWuVajqVGlV/t4rGYDNu893uBnZndIxI9rD9X4Xce0eA5kKK22CLJuRwEHc+obk5b4TlAC4Mbd6tbpm3OPw2zSw2nQNioBrYZY/r6H49yMZgNLcjcUtAji8we4MuhfD01FvNVYo5dLqMi1i05i+dyEyCfiAxdlZWV48MEH8dvf/hYdOnSo8bdjx46hV69eNX6Xnp7OP48ePRpy4IIWvikpcWhuEhMtiFWa89yoosHtVd9Y1OsL1DkOyoAhDc5PFuyukxmyaMNhdGgfh0dvGInHfzGaNTi9CmUiXB4fZwu1xH0SbuS+jE7k3KKTWD631p5nY3lsY/ncmvv87NV0xpVCiQ96nQ4JKSY0N7F87eTcopNYPre2cH6xspYtVlFZX/1ZrdPrEB8X+c9qavLdEEaDrs5a16iv+7vmGpPmPDenywuPimqL6mORnBzZ92Sk0ZLnRk3i569R1nye/COfLcrFjef2Q0ozfvfk2kUnsXxuQuQS8YGLJ554ghtyn3feeXX+5nQ6YTTWbKhoMlU+TF2u0EvZSEaorOznXhqnC0Ul6QteVuaIOf3kljg3g8mAdolmVftQAzSLSY/SUnuVxmdcvBnvztuOOYsblow4esKGB15egj/9cjx+cX5//PvzLYo+r32SmTNGiottiFbkvoxO5Nyik1g5t+ZyhDTnPBsrY9vWzq0lzo+cdfEJFs6OpQQDpZDN4fP5UFzcfDIIsXzt5Nyik1g+t1g5v0icY1tibOn9rGYjdFoNZ3QrpV2SGf4oeFYnJVmRkmiGzVl/9USF3Y12CTXXuhUON5LiTM06Ji1xbtRQnc5NDcHtm3PdHAvf99Y6N5PJgIUbD8PlVm4n/bT2EG45f0ANX0uoyLWLTmLh3GIh6bitEtGBC5KGIjmor776qt6/m81muN019RyDAQur1Xpan11bq7I5oC94S7xvJNCc56bT+XDW6Gx8orDpFzF+UAfO5nCfmoCpcXZ+YXmjQYsgTrcPL324AX+7ezL+991OlNubztYkmSmTQQt7RfRfT7kvoxM5t+gkls9NLc09DrE8trF8bs19ftS7asKQTtyLSikzx2Qj4A+I7acSObfoJJbPrS2cn1Ii+XlG70FSvqP7Z2L5lqOK9qFKd1p/+bwtc32b876xO9yYNqIL3pm3o96/r9p2DGeP7VqjH9PBo2Uc0BjVP5P7LqkZE6+n8WNvznMzGPy8Tl+0Pl/xPlOHd+Fzi/TrFmm01LnpDAEcKihXrYhRZndDG2i+Z4tcu+gkls9NiFwiWqDs008/xYkTJ7ivBVVd0Iv4/e9/j5tvvhmZmZkoLKypsRj8/4yMjFY5ZqF5nA6UmTGop3Kprwun9OBsk6aaojXEwWPl2JNXzI3WmiI7MwE9Oic3ayZTKOh0Gg7QxMUZYbUaub+HIAiCILRlfF4fZk/JUbx9v27tkJZihTMEiSlBEAThdJ7VPRRvP6x3OuItBl4nRjpejw9nj+sGQwO9OMjp3yE1Dr2zU6p+R0ns3648iHPGd1M1JnEWQ7P0HVCK0+nlebNjapziSsjzJnaHT0UVpNDyaKAJYR9BEITWIaIDF8899xzmzZvHlRfBF/Gb3/wGf/zjHzFy5EisW7eOy/uDrFy5Et26dUP79u1b8ciF04UcCPddOQwpCU3rKF4+vRe6pMdXOR3IQDIb9Vi66Yiqz1y84TAbgI0RZ9bjketHwuP1odptF1YMBh0scSYkJlmxO78UizYeweodBfAEgMQkCwczBEEQBKEt4nJ5kJFixdUz+zS5bWKcEfdfPVyCFoIgCGGGHODdOibiIgXBC5Lzu/uKoXBHQdDi554OAdxz5TCuiqiv2n/O4lz+O81DQb5fdRBZmQm4cHKOItniu68YBk+Yx4Sak9sdHjx6wyiWaW6KX148CGajLqzBFaFxtBqge+ckVfvQfUqvaJUIEgQhuonowAVVTWRnZ9d4ERSUoL9dfPHFqKiowGOPPYa9e/fis88+w9tvv43bbruttQ9dOE1cTg+MOi1euGcy+nevPwhFWTc3XzAAl03vBVuFi3tOEBqNhpubqW0cVmpzoUtGApIbCJbkdE5iOan2SRaUh9BQrjkwmfSIizPh84W5uOGp7/D711fin59uxvP/24BfPP09/vjWahwtdiAu3lSvoSwIgiAIsQzZAnabC7Mn98AdFw2q4RSqTp+uKXjxnsmwGHVscwiCIAhhdoDbXBxkpqa/VDlQHwNzUvlZrdNooirI7LC5MaJPOn77i9FIS67bzPZ/3+/C4cIKvHD35KrKizKbG0/9ZxUn5V1/Tj9OmGtoTGg/naYy2S/cOB1upCaZ8dxvJnLwqaFg0wPXDMfkoZ35OguRg8ftxeShnWBt4P6qj+kjs1gu6nT7WwiCIIRCVGvLUADjP//5D1dfzJ49G2lpaXjwwQf530JkYjYbOMxPzX3IuRDw++H3+est+3U43DCZDXjq1rE8UdKLSm6pYZnX50c6STu4vKgopwZBgRqGsEGnPiZHVRqUOfL24zNYe3TNjgJuWkWBjDNHZaFLegK8Ph/KyxxoDUgKymwx4revLcf2/Sfr/J3Gc8PuImzJPYHHbhyJvtntxFAUBEEQIgqqiqTGkGQL0L9pbqdmnxqN91SW6ulDGYFkG0wa2on1v5dvPoL1u4u4WjIlwYyzxmQjo12lPJTDXrNXmiAIghAeSCe9otzJPRNIToiq37fkHufkM0oUmzW2KzefdjrdcDo8YV936fRaaLQaToqj9Sr1QqKqvmCyXGPQepQS6/plp+D1x6Zj4+4iLN98lOe8hDgjpg7vjJzOyTzv/fmX43HshB3frTyIk2VOfLYwF+eM74oLJuVg8Yb8qjFJTbZwbwwKClDwINxjUh1aY6bEG/H8XZNx8FgZ5q/JQ2mFm6srqHfJiL4ZcDg9sFU425yzu9LO0dM/uNk6nT/dDxQwiITeAHQM5OM4d3w3fKygpyj5R+heFLkvQRBai6gLXOzatavG/w8aNAgfffRRqx2PoAwKQJCEUcFJO75asg/5RRWsrUhZGudP6s4SR5TxWDuAQVUDNOFTAynS/TxR6oBRr8PAHu1x9thu/DcyJqkcNwgZB15fgHtkbN5zXPExjhmQCb1Ow0ERyoyZNa4r9Dot/79OS4Yr4HG2nrFhNOnx6meb6w1aVIeO9y/vrMVrj0xjo1tKcwVBEIRIgILvVosBm/cexw+r81Bc7oTJoMPQXumYMSYbRlOAnTHNIUVAtoDD5oLb6cGwXmkY0isNVeZCIIDSktbtUyUIgiBUBprJCe5yajG6bwZG9s2oelZTglu4n9W0djJZDLA7vPhiYS725pfwfJLZPg7nTeiGTunxcDg8iir1yFlNwXHatnfnJPTqnFzZKCBAPwIoKbZVBUGSrQZcPr3nz8dh0MHr9XHiXHZmIjvDaY1Hx8LrX9L7aWUocELnlpFsxlVn9uZzo/Oh9MGSEnubC1hU2TlWA9btKMSPaw+xogMlR47sl4FpI7I4CdLFdk7rjo3H5cEVM3rjyAkblm5sWF7bZNTh8ZtGw2TQSkKkIAitRtQFLoTorLLQ6LR44vWV7KyozqY9RazxOXZAB9x71XD+XTB4YbEaUVzhxoOvLEN+YUWN/dbvKsQH3+7CBZO6c4kxZbRUz9T0uD24YGKO4sAFVVWM6pfJjpT/fLmVsxBqk5ZiwcPXjUSntDjYK1xh72tBlSYL1+Ur2t7l8eHLxftw6bSegAQuBEEQhFbGGmfCsZN2/PmlxZzEUJ11Owvx7rztuHR6L1w8pQcqKpzNlpVITjGq4BQEQRAil0h4VlOWPDmeX/54I1c6VPe70xqWelCQzOBjN4zm7SjQrjiQ3kR1RFDyidZ8JPn7ztfb8fWy/ZyMV5usjAQ8duMoJFqNrV41SIGKps5NCRSs0lKioKZyvKIx6EHXbf/RMjz7/jqcKK0pK712RwHe+mobrj27L2aO7cqVRq3ZL4JsLApE3HvlME4eIX9M3rHyqr9T8ub4QR1w5Yw+SE4wciKIIAhCayGBC6Hly2wNOtz30uI6wYfqBs/yLUdx8rVl+NMd4zkDQW/QotTmxv1/Xwy7s37HO2WdfPrTXlTYPbh19kCUlTo4syUY/BjWJ501QKm8tiluuWAADhdV4JX/29TgNkXFDjzyylL85VcTkEkSE2E0rvUGHQdV6JyV8sPqg7junH6c1RGNxp8gCIIQG5ip4rLYjof+sZQD6/Xh9vrxwbc74XB6cdVZfVBeZlckxyEIgiAIpwvJGFusJjz1xkqWdWqInQeKcd9Li/DiPVM40FGf3HGoUFUFOb9f/ngTflp3qMHt8grKeW1NvSCpN0hrSkadLnq9FgaSazYbeJ1LfgCzSc8yUz6vr1nHtyUxW40ctPjtv5bXG2wKNmV/fc5WtoPOm9C91eSng7jdPvh8Tozrn4mpI7rgSFEFCosdMBq0yOmUXBlE8vrCnrApCIIQVc25heiHHO7vzNvRYNCitiFIVQIGo46rNF7434YGgxbV+W7VQew5VAxTtQZT5KinLILf3zwag3umNWog/vrSwVxt8fhrK5r8LHKsPPP+Wpa6oH3DBTlvSC5LDeV2Dxt94TxOQRAEQagOyTlaLUbOQGwoaFGdzxbuxdHjFZV9MARBEAQhDOj0Osxfc7DRoEUQcu6+PmcL9IbmzQGleW9r7olGgxZBKhwevPThhqieK1lWKc6MxZuO4M6/LcQlj3yNy387D1c+Pg/vfbsTdo8f8QnmiF/L0vFZzQa2cxoKWlTnvW92cCN27oMRIZVOJFtGcmV9uiShW0YCS0nZyp3NUk0jCIJwukjgQmjZDAqDDj+tbdr4CjJv+X7uhVF40o5decWK9/tiUW4d45EyNEh388lbxnDTM2oURs2lyLhon2TGpVN74u3fzcDkYZ3x0fxd3AxNCdQ8bdu+E2E1NsgEYr1XtUS2nScIgiDEOGazHrvzihUlMAT5fFEuO5EEQRAEIRzQum7ukv2Kt1+66Qivs2it21zojTpe0ypl674TLEkUCQ5wtVgsRjjcPvzq2R/x6qebayToUeIiyWTd9pcFWLLpCKzx5lM9LSMTGv91uwrryEM1lpBI0kzaCLJz6JjId0KBCpItqy7BLQiC0NpE3ywnRJVM1LqdBXCoKPGkDJaDR8uRX6iuumD19gJuVEZlvtX1ImkCpjLIrhnxuP/q4Vx6GqTC5oLf5+eGWV8vO6Dq8xasPYRu5/dHOCOM3TslqdqnXaIZVpMBxa2sfSoIgiC0XQIaDeavUZ7AQCzdeBh3XzEUDrtGpA4FQRCEFqeo2M4STErxeP1YtvkIxg/IbBYnLyX8kfN4w+5CVfvNX5OHCyd2RzRBwR6dXotHXlzEa/+GoPn/H59sRFKcEf27tWv1fh4NotXiR5V2zuKNh3HLhQOld4QgCIICpOJCaDEoM+JkmfrJuKTCpdpRwU3PXN56szGo7wVlD5CO5PHj5ThxogJFReVs/Hi9pO3oVxVcIUorXNCGMfPD4/Fi2ogsmIzKMzPOGpMNu/S3EARBEFoRmpdJEkENJMvo9vgjOsNSEARBiB3UzlNEcZmr2Xox0Xxnd3pUv19ZhYsr86NNSvqrpfsbDVoEofF4a+527oERqTYBqTmovX/ougX3FQRBEBpHAhdCi0EBA2u1vhNKsZhCK5s0GnRVzbkbPqbKIEf1/6cqDb1OndFAVRrhjAd4vX4+7pljshVtT43azp3QnZuaCYIgCEJrQfOyWUXQnSDfhIGzT6PNHSMIgiCEE5LpocbIcfFmflmsRu6VqBZa26nex6QLTcq3Hmi+o7WsWkxGfdQoA3MvCKuR195ThnfGKw9MxX1XD0O/bu0a3e9wUQV2HSxm6clIhNboau0cum6E2DmCIAhNE5lPfyEmoGqGYb3T2UhRmvUfZ9ajR+dk1dUMvbJSOPhQXSZKCXRcTpcXQ3unY832AsX7Ub8MTZjzW9wuD64/pz8KTtqxcuuxBrejPh7U14NiMXa3BC4EQRCE1kMTCPCcuXB9vuJ9hvRMg8frk4pBQRAEoeGAhcWI4jInvvxxLw4cK+OmgJ0zEnD+xO5IS7bC6XTD5VRWVd85PR7JCSaUlCtTC6Cl6tgBHTi5rDmgNWyCUY9uHROx/0iZ4v3GDuwQ9jVpyI24LQZs3FOE+avzeJxJSWBwzzQ8/ovRXH3xtw/WNSjXtW5nIbpkRKgklj+Akf0ysGaHcl/C8L7pcIRQYSMIgtAWkcCF0GJQb4mERCM7LFZsOapon6kjs3g/Cl5ktLOyk14JF0zq3qBhqtNpuLSUDIP6AhtejxcXTs5RHLhIsBowcUgnlJU1Xd7anJB+qt3mwoPXjmCDj0psqzcyM+q1mDi0E644szcSLAbeVhAEQRBaE7fbizEDOyAp3ojSCmVSCjQne9zqJBwFQRCEtoHJbOBein99by1Wb6uZzLV573HMW7YfQ3ql4eHrRsJs1nCz4aagRLazRmfjo/m7FR3DoB5pSIwzoqy0edaDtE6l46Sgy0sfbVS0T6e0ePTJTkFxsbL1cmthjTPh8HEb/vLuGhTVkoeigMQH3+7EpdN64q+/noBHX11Wb+DGTSoCEerkJ0nnqSOyWNJKqfz07Mk9RBlBEARBISIVJbQoFBS48dx+iiSjqJn0FdN78SROGQjUsEpJ4UWfrikYN7AjO0eCUKDCYjEgIdGCxCQrDGb6txmJSRb+fXU9SWrg3a9bewzvk67onG44tz9cbq/q6o7mgM6xvMyJiYM74uX7puAf95+BJ24egz/9cjzee3Imbr1gIOKMOglaCIIgCBGBzxdgZ8zN5w9QtD05mwb1TOO5WRAEQRBqN3YmKShycNcOWlRn4+4iPPjyEhiMeg5yNEXA78dFZ/TgxLmmMBl0uPn8/jXWns0Bvd/kYZ05ga8paCl76+yBp/piBCK40sKAI8dteOSVpXWCFkFcHh/e/3YnPv1pL35/85h6ZZfITxChLS646sbr8+G6WX0VV8l075ikKKAmCIIgSOBCaGGoKXai1Yg/3TGes1IaIj3FwlkWZISRs8Lp8GBQTnvcd9XwRvtPkCbmU7eOg8PhqgokkEGblGzB/mMVePaDdbj44bm48rff8M+n31qNnYdKkJRk5RJjgqQoyNH/6A2jMLJvRuMG4oUDMHloJzgd6hu4NRfcTNzu5uyalDgDendJQtf0eLgcHtgqnGIECYIgCBGFy+nhqotfXjyY59KGGNorDb+9cTTPySITJQiCINTX2Pmzn/Ziz6GSJrcl2aH3v90BnYLeEVTZ7vP6eT1KlQyNyRo/detYpCVbeL3a3Gs8es+nbx+H3lkpDW6n12m5Ar9vdgpcrbgmbQpKJLRajLwedyuQ1Pq/H/ewhBQFb6qj02owbWRWRFcoOO1uzBiVjevP6ddogIWCFg9cMxw2W/M1dhcEQYh1RCoqhtDrdTAadadkkQJsgNGrpSDHPwUJgv/2+z31OhrIAZGRYsGbv52BpZsO42SZkw0QbpQdADLbWzB2QEd2uJNDnqDjp/1G9k3H2787C3OX7cOPaw7hRKmTG5f1796e5aEG5qTyxB/MzKTjocZsL324Hos2HK5xHPR5lH1DL6quoEAF/Y6yW4L7P3rjKDaEv1i0Fxt2FXEGSHK8CVOGdcb5k7rDatKjotwZEQ4VqhrRarX8k8aLJLG8EZigSvcGNWEjI47GjcY6EsYv3NB3k76jwe8n3XfNpYsrCIJwOtDz2WSqrEZsiWc1vY+t3IkpwzphzIBMfLlkH35ad4gdFDSnk8b1hZO7o3d2O577pdpCEARBqA3NURazAd+sOKB4n/lrDnGPQLfTU6Nant6L1ih6fWUeJ1Vl2O1urhD4xwNnYOXWo/hqyT7k5pfC5w+gQ2ocZo7Jxpmjs+H3+Vusup3WwyYzOICyaU8R5izeh+37T8Dj9SM12YLpI7Mwa3w36LSArSKynd/UTHv7/pM4etymeJ95yw9g1viu+G7lwarfjRnQAQadBhX2yA1ckJ1TUeHEueO7cZLj0k1H4PX5OchEP6l/54CcVOR0SmLfRXNX6wiCIMQyEriIAdgxbNBBp9Vi7c4C1pCOtxgwom8GjGYDfB5fs2Xhk9OVynNNZj33n9ixoxBeX4AbmvXr3o4rLDz1OGS9Hh90ei2mDO/MgYEDR8rYOUJNtbtkJPD2pA9Zx9FR4WKH7wUTuuOqGX3484M6pCRDVVJir+FYsViN+PcXW+oELWpDeprPvr8OD147nD+XjD5ylFB/jS6pVtx9xVBYTIaq7Svsbh5HClq0NhSc0Rt1MJsMHISh68DNzXqkshQWjT9dh9akxn1ywo4dB06y0VZ5n7Rv8D6JRWgBpDfo4fb4sHpnIexOL9onmjCsTwYHFum7IcarIAitATtu6FltMuBwUQV25xWztFN2ZgJ6Z6ewbKPb1TzSiLyoL3eyc+jiKTmclRikak4vtke05IUgCILQetD8QetISoJTis3h4QBAny5JcDj8HKgg+SiSDt6ae4LnPoNei/7d2iM12Qq3y4OyUjuG5LTHqH6ZHFwnKHjhcLg5s97bwpn/VKVI66RenZPw6A0jYToldUXzo40+3+Pl5LpIJ6DRYMHaPFX7LNl4GHdeNoQTB0sqXMhsb8WvLx0cFX2v2FYKBBBvNeKc8d2wflchJ12mJJgwtHc6S2AFAn5Z9wmCIKhEAhdRjsViZKPgjS+3YfHGw3C5fzZiKMI/blAH3Hhuf3boB6sZTsfBQc21dh4s5iZau/KKa/ydSmbPndAN503sDrvNXTUpkwPbbDFi/uqD+HxRbp2si24dE3HZtF4Y1T8TDjtlINQ0xPj/3T4+/mB1QX1+DTJmKxwefL9SWRYOZdIcLqxA+wRTVWCH3pud/g4PXAY3kpPjUFxsixgHOwWp6Jp/+MMufLvyIMpsP19TiulQ1irpa3ZKjYfd5myVLBy+T+LNHKz4bwP3Cd0jdK9QtlLt6x1L0PelxObG259sxurtx2oG2Ux6TB3RhZ13Wp2m2cvNBUEQGoMq4egZRQ6d//2wi7NKq0POggsm5eCsMdmcRNBcFZxsG7jBczoF4pOTrTz3R8o8KwiCIEQmtNYprVBf6VBc5oRGk8xrRZr3qLL+62X72alcnb5d2+GamX3Qs0syr1FoTRhMmgt3UJ3WDPT5lcdQmRQWbRXrdMxlFer8D9TcmoIy8VYDcjonsWw09R+JhkrMxEQLyJJ55+vt+HHtoRqNuml9TIEw6v3ZLtGC8rLmaeouCILQFpDARZRncrv9Adz/90X1NruiDPfFGw5zVv4zv57AvSZC7c1ABhMZelTJ8M9PN9XrEC8qceCtudux40Ax6276/f7KzHuLEf/6bDO+X/VzyWd19h8pw1/fW4urzuqNi8/oCZ/P2WB2Z2MGG1V0fP7TXpafUsoXi3NxywX1NwyNtKRPMrZpLB/713IOCtQmKIVF2UOUnUP9P+whGPenQ/A+WbjuEF79bHOD98mbX23DzgMncf81w+H3O2PSYUXBwiMnbHjs1eU1DNcg9DtaNJHT8NnfTILJHIDLGflGuSAI0Q/rTseZMG/5fp636+PYCTte+3wLdueV4M7LBqO8rOG5OVSkukIQBEFQCk0ZlPijFqulUgqR5r0/v70aa3YU1Lsdra8ef2057rx8KMYP7MC9AyNhnqJDiITjUAsds8nUdH+R6pCcNDU/f/q2cRy8oOqT6AhamOFlv8xi5BdW1OvDoKRJWvf96Y5x6JyeIMELQRAEhUhz7igl2Ozqyf+srDdoUR3Kyn/8tRVstFEfhFCgqokjx214tYGgRXVoUv5kwW4YTAaWqqJKi4aCFtX573e7sHnvcVitDTfxbqpZG+2vBtqetFKjAaNJj39+urneoEXtgNVf3lkDp9tX1YA8XNB9crjIxoGqpu6T5VuOchM2KteONbgM3aDDE6+vrDdoUR0ybul6mc2h3feCIAih6E6T3EZDQYvqUC8K0pymOUgQBEEQWguSaKJeSGqCF6RAQHK6lF313+92Nhi0CEIJcC9/vBGFxQ6WURRCRxMARvfLVLXPsN7pcLm9MGiBslJHVAQtCK1Ohz+9s7reoEV1aF34+9dXVvpyrHJ/CYIgKEECF1HsdCAt6n2Ha0o7NARlua/ceoyz9kNBp9exk1lpNQNlkptNeu69QPJQSvn0pz1cOREK1PSK+gioweOpbJYV6ZATnLRVF63PV7S92+vHnMW50OrVZbmcLiHdJ+bQA2qRCgVj5q/OqyHl1RiUfXO8xMHfa0EQhHA8oz75cbfi7Wk+sZzKWBUEQRCE1oAqtCl4MXlYZ8X7kGwyZfFTUpHSpt6UHV+5Jg3vOirWIGnICYM7IUGFg/6CyTnczyJaAhZEcrIFBSdt2LxHWQIlrQ/nrzkInV7WfYIgCEqQwEWUotFp8dXS/ar2mbtsHwxG9ZF9MvTIEU2VFEqhCTnvWBn25JXU6WnRGCRzRI24Qqm6oEqDdklmVfvQ9rRfpEPN139YnafqWMlxTtUk4XI06fU61fcJNZJfve1YzGU0kYNvnsLFUZAvl+wjAdQWOyZBEATCaNTB7vSytKBSqLKT5udwV/EJgiAIQnV8Xh+uPqs3khNMTW4bZzHg+ln9OElt+eYj3KhbKUs3HeEedLQOFkKD5CWdbi9+cV79ssy1Gd4nHf27t4+qoAXh8QVU+2W+WX4ABiorEQRBEJpEnpZRikGvw+GixksRa0ONqKkCQi1arRbHSx3w+tRpazpcPhw4Wqb68/ILKrhpqFr8Xh/OHJWtap/pI7OqGnNHNAHgUEG5ql3K7R7YnZ6wBS6oaqKoxM6VIWo4eKyMTi9moPHWabX8fVMDfZ8lm1kQhJaG5vRjJ2yq+zgdOFIGjTyjBEEQhFaEnNp6rQZ//dUEpCVbGtwuOd6Ev/xyPKxmPdxeWpOqW0d5vH6cLHWKbX6auBxuTBzSEbfPHojGhpKCFo/eMIqbokdjE/JQ1n20XtTppKpHEAShKSR1Lkohh4MmhEk1VELbM8DNmtUSqoFI5ahjB3ZgQ5WqNpqCgjjTR2XBaQ+tYXk4IfMtlLEMpwpW5T2p/gNP576MRILOQLWnFWPDIAhChMINPkOcT+QxJQiCILQ2DrsbiRYjXntkOpZuPIyvlu7DwaOViVBdMhIwa1xXnDG8C68NHTYXzBZjxK+jYhUKQlSUOzF1RBeMHdQRXy7OxcL1+Sgpd/FafEivNFw4uQd6dkmGrcLF1ywa7Sr1677gDtEVpBEEQWgNJHARpZC+Z9eOichV2OOC6NYxEc4QSi/9fj/S21lhMujgUtFDIt5iRM8uKao+i2IW2ZmJ8PnU9aogfL4AHE4PZ2s89q9lnCnT4OdoNXjgmuHw+yq1UiMdqj/p3ilJ1T7tEs2wmgw4qbDPwulC90lGeyuMei332FAKGaqxtC4g45U0eLt2SMLe/BLF+9H20ZZhJAhC9EHPmc7pCaz5raZCrldWijyjBEEQhIjA4XCzk3tkn3RMGNIJhlOSTiRPROtBW4WT7XFCc2q9oQZqAN4+ycJOd6F5ghfUa/OSqT1ww7n9q/7mcHng8/hQUmyvTKyIUrp2SFQlwUnbkwQ03a+CIAhC44hUVJRCDvcLJuWo2uf8id3h9agPXJDRR5PqxKGdFO/TPsmMzhnxyMpM4IlZKcP7ZLChaLeHJt9E1RNZmfH4y68mICsjod5tMttb8eQtYzAwJxUOe9OVGZGAx+PFtBFZqqS+Zo7tCrvDHTYjkO4Tvy+g6j6hEu8hPdOiQ65LBU6XB+dN7KZ4e0q6oe9nQIxXQRBaGI/HxzIbo/tnKt6nc3o8O32czujLhBQEQRBiEw5SONwoKbbh+PEKfp08aeOKjGDQgnC7PRjVL1NRX4wgU4Z15vlSHMvNR2UFjBtFReU4cYKuVzkqypxwODxRHbTQaTQ4f1KOqqqLcyd0i4o+m4IgCJGABC6iFJfLgy7pCex8VwIFEAb3TAu52ZXf68elU3tWZbM0xcVn9GAjhAy+y6f3Ulxtcdn0Xgj4T28St1e40KGdFS/dNwXP3jkRs8Z3w+ShndiR//Tt4/Cvh6Yhp1MS7BVO1RrfrQUHj/x+nD2mq6LtqRndueO7cQO7cEKfd8nUntAr7FFyEd0nTk/MZfG6XV5MHNIZaSkNa+9WZ9zAjoi3GqKuGZ0gCNEJPatpvqWqCyVcOq0XP6uj2bEgCIIgxC40PzU0RwWr8mdPVpb0RyoDtJYN9zqqLUFrv1gxKcrKHEiKM7JktRJofThpaGd4XLGVuCcIgtBSSOAiSqGJ3uFw4be/GNVkRQNVGDx92zjOvg/VQUwZ8cnxRjx03cgmndKkKzpzTFd23jodbozqn4mrzurd6D7kO/n1ZUNY/spmO/2SXOep7JvOqVZcM6M3bps9ENfN7IOcDgkoKbFzZUa0GUselxfXndMPYwY0bhRRE7qnbh3LWR9ud3gNbrpP2iWY8PB1I5q8TyiwMmN0Nt8nsQZlZ7mcHvzx9vEs2dUY/bq1wz1XDoM7xqpOBEGIXOhZ3SE1DvdcObTJvlKXTuuJ8YM68jNNEARBEKIRWm+cN7E7ZozOanQ7StJ75IaRklAkqCLgD+C+q4ajb9d2jW5H60JaH1K1hVSxCoIgKEN6XEQxNNmZocFzv5mIT3/ai+9WHsTJsp+d/olxRpw5KoszJQM+H1dAnA52mxsDu7fDC/dMwoff78bKrUdr6GOTjMTsyT0wekAmKipcVaW1JMd08Rk9ud/Fpz/twdbcE1X7kL+E5KEund6Le3BQA7XTLLioFdyJHUcLVa/YbS48eO0ILFiTh6+W7ENeQXmN7CDSeL1yRi/Emw28bWvA90lOKp6/exI+/GEXVm09VuM+IZ302VNyuGSb9GdjtQSbStcTrEb8/b4pPA4/rj0EezUDNaOdFWeP7YrzJ3XnknZZHAmCEC54frS5+Dn83J0T8dH83Vizo6BGcgMFVS86oyeG9EyF3eaMuco4QRAEoe1A6w1q/nzb7EEY1CMNny/ai9z8n3tF6nUajB3YEVec2Yslj2mOFARVVRdJZvzxjnGYsygX36w4gMJiR9Xf48x6TB2RhcvP7MVr9vLSn/8mCIIgNI4mIHX/9Ro2pI/ZXOj1WqSkxKG42FZDb7O5MBh00Bt0sJgN2HekFOU2N0sF5XSu1KOmMlfSlGwuLBYDdHodvP4ADh4tg9cXQEY7C1KTLXA5vSxjVdvBodNpYbUaodNrUVLuQn5hBWd5UiNu6mlB8lBUadFcQYtouG6hQmNpNOlhNhtwpKgCJ0qdMBq06N4pmbM96Hor6RnR0udXdZ/4AjhwrIy/Vxnt4pCabG7wPonFa2cy6aHV67g/yd5DJXC4vEiKN3GlFJWte9xeVccYSefW3Mi5RT5pafX3DmrNeTZWxrY1zo0q82gu0Rv0cHl8yDtWzoHmDqlWpCSYOUO1rTyrmxs5t+hEzi16iYXzi8Q5NlbGNgitP00mA0xmPYqKHSgstnOVONnlJJ9I66hYSXyLpesWLedG/g5a99Ha/MCRMvZ7kK+jZ1YyPF4/98OgIEc0nltzEcvnJ+fWNuZYIfxIxUWMZOLTi2QcMpLM6JBiYY3PslJHi2SzVxpzHg6YdMtMQFycCRUVTpSW2BuUX6LjKC93Vk3ofbNTTv3eh7JSe7MfY8w3orO7WQ4r2WpASpyRf++0uyJqEql+n3RNj+ffkfOrpLhtXW+upHB54XZq0Tk1jh2FPA4ldslgFgShVamqTHScelZn/PyspjldEARBEGIJmt+oKtrpdCPepEdy5yRYrSaUlztQYZPqZ+H0sNvd/JNsqi7p8cjOTGC/jNPhUZRYKAiCINRFAhcxZoiFU26GgiU0EVPgovLf6iZ04fSg8Y4GeaFgYK2tQwGnWJXFEgQh+pFntSAIgtDW1lE+H6kCmCIq+UuIfsieKhU5KEEQhGZBmnMLgiAIgiAIgiAIgiAIgiAIghAxSOBCEARBEARBEARBEARBEARBEISIQQIXgiAIgiAIgiAIgiAIgiAIgiBEDBK4EARBEARBEARBEARBEARBEAQhYpDm3IIgCIIgCIIgCIIgCBGATqeFRlPZRNznk8bhgiAIQttFAheCIAiCIAiCIAiCIAithFargcmkh8Fo4KCFy+2DyaiDPxCA1+2Dy+WB3x9o7cMUBEEQhLAigQtBEARBEARBEARBEIRWgAIWVqsJG3YXYs7ifdi0p4h/r9UAQ3un48LJOejfPRV2mxNut6+1D1cQBEEQwoYELgRBEARBEARBEARBEMKM0aiH2WLE02+twrqdhTX+RgUW9Dt6TRjSEfdeORyBgBMejwQvBEEQhLaBNOcWBEEQBEEQBEEQBEEIIyQJZY0z4m8frKsTtKjN0o1H8Oqnm2CxGsN2fIIgCILQ2kjgQhAEQRAEQRAEQRAEIYyYTAYcOW7D8i1HFW0/f00eyu0elpYSBEEQhLaAzHiCEEWYzQZotFrodBpQaza/LwC/j5q1eVv70ARBEIRmQqfTwmjS8/OemnMazQYYfAG43V74fP7WPryowmIxQKvXUV4ra4VTk1OdRgO73QWvV8ZSEARBaD20ei2+WJSrePtAAJizOBeXT+vZosclCIIgCJGCBC4EIQowmfWwWIw4dsKOr5bsw6GCcvLBIDszERdM6o72yVY4HW4JYAiCIEQxWq2Gda6NRh0WrT+MZZuPwObwIN5iwIQhnTBxSCe43F447W4EyHshNAhlo1LAx+cLYO7S/di89zg8Xh/aJZpx5uhsDO6ZynOmrcLV2ocqCIIgtFEsJgO27D2uap8tucdxzcw+cLTYUQmCIAhC5CCBC0GIgioLyhZ94j8rsXlPTcN2a+4JfL1sP4b3ScdD142ERqOB0+lptWMVBEEQQg9axMWbsXLrUfzrs82wOWsGotfsKMDrX2zBry4ZjBF9M2CrcMJPXTuFeqU3rFYj3v92B+Ys3gdvrSqVpZuOIKOdFY/eMAodUq2oKHO22rEKgiAIbXvu96is/vN4/NBqRfFbEARBaBtExYxXUlKC3/3ud5g0aRKGDRuGK6+8EmvXrq36+4oVK3DRRRdh8ODBmDlzJr7++utWPV5BaC4o69Zg1OPBl5fUCVpUh5q5PfzK0ko5EQNJYgiCIAjRhMVq4gqLv/13fZ2gRZAKhwfPvL8Wq7Ydg1mac9YL+XIsVgNe+2ILPv1pb52gRZCCk3Y89I8lKDjpQFy8KezHKQiCIAhUCdg+yaxqn3ZJZnh9vhY7JkEQBEGIJKIicHHvvfdiw4YNeP755/Hpp5+ib9++uOmmm7Bv3z7k5ubitttuw8SJE/HZZ5/h0ksvxYMPPsjBDEGIdvQGPT74bifySBqqCfYdLsUn83dDL4ELQRCEqIIDzhrgn/+3qcltSSHq5U82ch8MvT4qzLiwEhdnRm5+Kb5dcaDJbZ1uH/72wToYjXoOeAiCIAhCOCHJwmkju6jaZ8aoLPikR5MgCILQRoj4ZdrBgwexbNkyPPHEExgxYgS6deuGxx9/HOnp6fjqq6/wzjvvoHfv3rjnnnuQk5PDAQ2quvjPf/7T2ocuCKcFOaVMRj3mr85TvM93qw7CQtJS1IFUEARBiAoo4PzdyoNwK3REUMPuBavzuCJPqIVWw41LlXLgaBkOHClDfLy6jFdBEARBOF08bi+mj8yCxaRsPk+ON2HsoI5wS19DQRAEoY0Q8YGLlJQU/Pvf/8bAgQOrfkc6/vQqKytjyaixY8fW2GfMmDFYt26dNK4Uol4mavPeIpYGUUpJuQu78oo5e1QQBEGInkbSizbkq9rnp/X5Ig1YC4NBC4Ney31C1LBgbR680i9EEARBCDNer597XDx07Qjomkg8o/ntkRtGcj9DXwMyiIIgCIIQa0S8dzMxMRGTJ0+u8bvvvvuOKzEeffRRfP7558jMzKzxd6rGcDj+v707AY+yOhc4/maZbOw7ShAVRHYhUhZBNpFyBTe01bLUSlFsa3tFaAtaq9QroijgUsBea6sXfNp7EbRWFFG0LhVBEbBsAgIiImEJJCSZ/bvPe+zEhHUCM5nvzPx/z5OHZOYb/d45yXnPN+855yuXoqIiadiw4Wn9f2O5/YLOnK/8bzIhtvj+/7UQUV2HS3xybrM6p/wdTnR88URsdiK21BKrPJsM760nM0OKS/3Veo0er++hzdtFxbrtMjMzzGqUYMip9nupc10Y+0WH2OyUzLGlQnzVRX9mT2xBf0A6nNdQfje+t/z+/9bKV/tLjznmnOZ15D9v6Cb5TWuLr/yb/G9DbPFCbPZK5viIDUjRwsXRVq9eLVOmTJEhQ4bIgAEDxOv1SlZW1RtURn72+6v3IUCEbrPToEEtibW6dXMlWRFbfOTlek7rNXl5WeYrGrSdnYjNTskcW6LzrM3vrd5AOqeaK+V0W4lw2InLeKWmxbLtdOZqdel7ryt5GftVD7HZKZljS4X4osG1rJ2xtT+3ocz59SDZtKNI3lmzW8q9AamdmyUDLs6X1i3qSdhxzESHvByPdbHFC7HZK5njIzYghQsXb7zxhkyaNEkKCgrkkUceMY9lZ2cfU6CI/Jybe3p/VPpBQHFxmcSKViX1D7y4uDzplnUSW/zoTJrOrRubZcHRfhCTk5Uh7Vo1jOqcEx1fPBGbnYjN/WL1QUgs82wyvLdZOR7p3r6p7NpbEvVrurdvJn5/SI6UeMVW8Wi72nVypFPrRvKvbQeifk2vTs1FJ68WFR07yzWVfy9PhNjslMyxJUt8bsyxyfLe2hJbWprIec1ryznfvVAiG0fpDlIlJeVmZaDNscUSsdkrmeMjNndLhsleqcqawsX8+fPlgQceMDfefuihhypWVZx11llSWFhY5Vj9OS8vT+rUqXNG+03Gmv6Bx+O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+      "text/plain": [
+       "<Figure size 1592.5x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "favorite_types = pokemon[pokemon.type.isin([\"Fire\", \"Water\", \"Grass\"])]\n",
+    "sns.relplot(data=favorite_types, x=\"hp\", y=\"speed\", hue=\"legendary\", col=\"type\", s=100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c6a20904-416d-44be-a4f3-2107200fb3c2",
+   "metadata": {},
+   "source": [
+    "#### Your turn\n",
+    "\n",
+    "**1.5.0.** Plot a histogram of peoples' heights."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "3b268a30-42ff-4ab8-b2cd-c58a76121f9c",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'histplot' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[35], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mhistplot\u001b[49m(data\u001b[38;5;241m=\u001b[39mpeople, x\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mheight\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'histplot' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "histplot(data=people, x=\"height\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "f145ae04-2796-4420-8d98-ce74d5bd4c83",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='attack', ylabel='Count'>"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.histplot(data=pokemon, x=\"attack\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9b0c9120-fff4-42b2-8ab6-3aa2eba47806",
+   "metadata": {},
+   "source": [
+    "**1.5.1.** Plot a bar chart showing peoples' average hours of sleep by age. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "ee30c851-14b1-4901-9182-4304d54d53a6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your code here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "15d94323-2d65-4100-9916-101516f6ccf1",
+   "metadata": {},
+   "source": [
+    "**1.5.2.** Plot a bar chart showing peoples' likelihood of getting exercise by income. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "13eeecd8-2518-4ed9-aac5-727a96b5bf80",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your code here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b2705fef-470d-494c-86c1-8b3bd34b3660",
+   "metadata": {},
+   "source": [
+    "**1.5.3.** Plot a bar chart showing average reported health by age. For each age, show average health for those who get exercise and those who don't."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "4ee2eb69-2f9a-42e7-b5d3-9499631bfd06",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your code here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "84b1e240-4f75-4c86-8c1f-1026aa223717",
+   "metadata": {},
+   "source": [
+    "**1.5.4.** Create a plot showing the number of people at each income level, for each education level. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "d7e02da8-beab-40e7-95d0-74a5c2bc838e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your code here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ac717580-4157-402c-9262-b2b50dfe606f",
+   "metadata": {},
+   "source": [
+    "**1.5.5.** Plot side-by-side scatter plots showing the relationship between height and weight for males and females. (There are so many overlapping dots that the plot will be more informative if you lower the opacity of each dot. Try using `alpha=0.1` and `edgecolor=None`.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "b00dd7d6-226b-469c-86d8-b71b328aa576",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your code here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e9ff7225-5d08-428b-90e8-ee60f4a4049a",
+   "metadata": {},
+   "source": [
+    "## 2. Crafting a data argument\n",
+    "\n",
+    "Everything up to here are just tools, worthless without a clear research question and a convincing argument. Choose a research question that interests you which might be answerable using the `people` dataset. Then do your best to find the answer in the space below. This answer should include data analysis (code cells) as well as written argument (text cells) explaining what the data means and why you believe it answers your question. \n",
+    "\n",
+    "Examples of research questions might include:\n",
+    "\n",
+    "- Do older people tend to have higher incomes?\n",
+    "- Do people who sleep at least 6 hours a night tend to report better health? \n",
+    "- Is it more common for males to be bisexual than females?\n",
+    "\n",
+    "**A note of caution:** this lab has given you tools for exploring associations--patterns that tend to co-occur. These tools *do not* equip you to argue that one variable causes another to change. For example: Plot 1.5.4 showed that people who are taller also tend to be heaver, with a lot of individual variation. But are people heavier *because* they are taller? Are they taller because they are heavier? Or maybe neither variable causes the other--perhaps they're both caused by something else. If you want to be able to answer questions like these, take a course on statistics."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "6f934273-b829-4bc2-a7f4-a27a3fc44a99",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your code here. Feel free to add new text cells and code cells as necessary."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8b4b852b-402c-45d4-b3bb-840e47b249ed",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/.ipynb_checkpoints/proposal-checkpoint.md b/.ipynb_checkpoints/proposal-checkpoint.md
new file mode 100644
index 0000000..10ee5c6
--- /dev/null
+++ b/.ipynb_checkpoints/proposal-checkpoint.md
@@ -0,0 +1,88 @@
+# Project proposal
+
+This planning document will also form the introduction of your 
+argument.
+
+## Overarching Question
+DO PEOPLE WHO EXIBIT RISKY BEHAVIORS (SMOKING, GAMBLING, DRINKING) ORDER THEIR STEAK IN THE SAME RISKY MANNER AS THEIR BEAHVIORS (RARE). IS THERE A RELATIONSHIP BETWEEN THE TWO.
+
+### What central question are you interested in exploring? Why are you interested in exploring this question?
+
+THE CENTRAL QUESTION WOULD ASK DO PEOPLE WHO HAVE AN AFFINITY FOR RISKY BEHAVIORS, SPECIFICALLY (SMOKING, DRINKING, GAMBLING) ALSO HAVE A RISKY BEHAVIOR WHEN IT COMES TO EATING THEIR FOOD. SPECIFICALLY IN THIS STUDY, IT IS ABOUT ORDERING STEAK.
+I AM INTERESTED IN THIS QUESTION BECASUE IT IS INTERESTING TO SEE IF FOOD RISK BEHAVIORS ARE ASSOCIATED WITH ACTIVITY RISK BEHAVIORS. I ALSO AM INTERESTED TO SEE IN THIS STUDY HOW INCOME LEVELS EFFECT THESE DECISIONS AS WELL AS EDUCATION LEVELS, WHICH IS ALSO IN THE STUDY.
+
+
+
+### What specific research questions will you investigate?
+
+*List 2-4 specific research questions. Each should be answerable 
+using your data set.*
+
+1. DO MORE MALES THAN FEMALES ORDER RARE STEAK?
+2. DO PEOPLE WITH A BACHELORS DEGREE GAMBLE MORE THAN PEOPLE WITH A GRADUATE DEGREE?
+3. DO PEOPLE WHO ORDER RARE STEAK ALSO DRINK?
+4. DO ALL FEMALES WHO ORDER RARE STEAK ALSO SMOKE?
+5. DO ALL MALES WHO ORDER RARE STEAK ALSO DRINK
+6. Are males more likely to order their steak rare than females
+
+ 
+   
+
+
+## Data source
+538 WEBSITE
+### What data set will you use to answer your overarching question? 
+
+*Give the title of your data set and provide a link to your data.*
+
+TITLE OF STUDY IS: STEAK-RISK-SURVEY
+
+WEBSITES: 
+https://github.com/fivethirtyeight/data/tree/master/steak-survey
+https://fivethirtyeight.com/features/how-americans-like-their-steak/
+https://docs.google.com/spreadsheets/d/125buas5LwUrDXEgQh0DPniGkA6-fWeupBgaXgUqXoOI/edit?gid=1253281910#gid=1253281910
+
+### Where is this data from?
+
+*Describe the source of the data set--not just where you downloaded it, but
+the person or organization who gathered the data. Explain why you trust them.*
+
+May 16, 2014, at 5:08 PM
+
+How Americans Like Their Steak
+By Walt Hickey
+Filed under FiveThirtyAte
+DATA LAB
+https://github.com/fivethirtyeight/data/tree/master/steak-survey
+https://fivethirtyeight.com/features/how-americans-like-their-steak/
+
+
+### What is this data about?
+
+*Describe the nature of the data in the dataset, including the number of rows 
+and some of the columns which will be important to you.*
+
+number of rows= 433
+COLUMNS= 8
+COLUMNS WHIC HARE IMPORTANT ARE:
+1.gender
+2.how do you like steak
+3.income level
+4. degree
+5. gamble
+6. drink
+7. smoke
+
+## Methods 
+
+### How will you use your data set to answer your quantitative questions?
+
+*For each research question, explain what you will do with the data set 
+to answer the question, and how you will present your answer (e.g. a chart or a table).*
+
+1. DO MORE MALES THAN FEMALES ORDER RARE STEAK? table
+2. DO PEOPLE WITH A BACHELORS DEGREE GAMBLE MORE THAN PEOPLE WITH A GRADUATE DEGREE? chart
+3. DO PEOPLE WHO ORDER RARE STEAK ALSO DRINK?
+4. DO ALL FEMALES WHO ORDER RARE STEAK ALSO SMOKE?
+5. DO ALL MALES WHO ORDER RARE STEAK ALSO DRINK
+6. Are males more likely to order their steak rare than females
\ No newline at end of file
diff --git a/.ipynb_checkpoints/steak-checkpoint.csv b/.ipynb_checkpoints/steak-checkpoint.csv
new file mode 100644
index 0000000..6227563
--- /dev/null
+++ b/.ipynb_checkpoints/steak-checkpoint.csv
@@ -0,0 +1,433 @@
+smoke,drink,gamble,cooked,Gender,Age,Household Income,Education
+Yes,Yes,Yes,Medium,Male,> 60,"$50,000 - $99,999",Bachelor degree
+No,Yes,No,Medium,Male,> 60,"$50,000 - $99,999",Graduate degree
+Yes,Yes,Yes,Medium,Male,18-29,"$50,000 - $99,999",Bachelor degree
+No,Yes,Yes,Medium,Male,18-29,"$50,000 - $99,999",Bachelor degree
+No,Yes,Yes,Medium,Male,18-29,"$50,000 - $99,999",Bachelor degree
+No,Yes,No,Medium,Male,45-60,"$25,000 - $49,999",Bachelor degree
+No,Yes,No,Medium,Male,> 60,"$25,000 - $49,999",Graduate degree
+No,No,No,Medium,Male,18-29,"$0 - $24,999",Graduate degree
+Yes,Yes,No,Medium,Male,45-60,"$50,000 - $99,999",Graduate degree
+No,Yes,Yes,Medium,Male,30-44,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,No,Medium,Male,18-29,,Some college or Associate degree
+No,No,No,Medium,Male,30-44,"$50,000 - $99,999",Bachelor degree
+Yes,Yes,Yes,Medium,Male,30-44,,High school degree
+No,Yes,Yes,Medium,Male,30-44,"$100,000 - $149,999",Bachelor degree
+No,Yes,Yes,Medium,Male,45-60,"$50,000 - $99,999",Bachelor degree
+No,Yes,Yes,Medium,Male,30-44,"$50,000 - $99,999",Bachelor degree
+No,Yes,Yes,Medium,Male,> 60,"$100,000 - $149,999",Bachelor degree
+Yes,Yes,Yes,Medium,Male,30-44,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,Yes,Medium,Male,45-60,"$150,000+",Bachelor degree
+No,Yes,Yes,Medium,Male,> 60,"$50,000 - $99,999",Bachelor degree
+No,Yes,Yes,Medium,Male,> 60,"$100,000 - $149,999",Some college or Associate degree
+No,Yes,No,Medium,Male,> 60,,Graduate degree
+No,Yes,No,Medium,Male,18-29,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,Yes,Medium,Male,> 60,"$50,000 - $99,999",Bachelor degree
+No,Yes,No,Medium,Male,45-60,,Bachelor degree
+No,Yes,Yes,Medium,Male,45-60,,Some college or Associate degree
+No,Yes,No,Medium,Male,18-29,"$25,000 - $49,999",Graduate degree
+No,No,No,Medium,Male,18-29,"$100,000 - $149,999",Some college or Associate degree
+No,Yes,No,Medium,Female,> 60,"$50,000 - $99,999",Graduate degree
+No,Yes,Yes,Medium,Male,> 60,"$25,000 - $49,999",Some college or Associate degree
+No,Yes,Yes,Medium,Female,> 60,"$25,000 - $49,999",High school degree
+No,No,No,Medium,Male,30-44,"$25,000 - $49,999",Some college or Associate degree
+No,No,Yes,Medium,Male,30-44,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,Yes,Medium,Male,30-44,"$100,000 - $149,999",Bachelor degree
+No,Yes,No,Medium,,,,
+No,Yes,No,Medium,Male,30-44,"$100,000 - $149,999",Bachelor degree
+No,Yes,Yes,Medium,Male,30-44,"$50,000 - $99,999",Some college or Associate degree
+Yes,Yes,Yes,Medium,Male,30-44,"$150,000+",Graduate degree
+Yes,Yes,Yes,Medium,Male,18-29,"$0 - $24,999",High school degree
+Yes,Yes,Yes,Medium,Female,18-29,"$25,000 - $49,999",High school degree
+Yes,Yes,Yes,Medium,Male,30-44,"$25,000 - $49,999",Some college or Associate degree
+No,No,Yes,Medium,Male,45-60,"$150,000+",Some college or Associate degree
+No,No,No,Medium,Male,18-29,"$0 - $24,999",Bachelor degree
+No,Yes,Yes,Medium,Male,30-44,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,Yes,Medium,Female,> 60,"$0 - $24,999",Some college or Associate degree
+No,Yes,Yes,Medium,Male,45-60,"$150,000+",Graduate degree
+No,Yes,Yes,Medium,Female,> 60,,Graduate degree
+No,No,No,Medium,Female,> 60,"$25,000 - $49,999",Some college or Associate degree
+No,No,Yes,Medium,Male,> 60,"$0 - $24,999",Bachelor degree
+No,Yes,No,Medium,Male,30-44,"$100,000 - $149,999",Bachelor degree
+No,Yes,No,Medium,Female,18-29,"$50,000 - $99,999",Graduate degree
+No,Yes,Yes,Medium,Male,30-44,"$100,000 - $149,999",Bachelor degree
+No,No,No,Medium,Female,45-60,,Graduate degree
+No,Yes,No,Medium,Male,18-29,"$50,000 - $99,999",Bachelor degree
+No,Yes,No,Medium,Female,45-60,"$150,000+",Bachelor degree
+No,Yes,No,Medium,Male,> 60,"$50,000 - $99,999",Graduate degree
+No,No,No,Medium,Female,45-60,"$25,000 - $49,999",Some college or Associate degree
+No,Yes,Yes,Medium,Male,45-60,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,Yes,Medium,Female,30-44,"$150,000+",Bachelor degree
+No,Yes,No,Medium,Female,30-44,"$50,000 - $99,999",Bachelor degree
+No,No,No,Medium,Male,18-29,,High school degree
+No,Yes,No,Medium,Male,> 60,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,No,Medium,Female,> 60,"$150,000+",Bachelor degree
+No,Yes,Yes,Medium,Male,30-44,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,Yes,Medium,Female,30-44,"$100,000 - $149,999",Graduate degree
+No,Yes,No,Medium,Female,> 60,"$25,000 - $49,999",High school degree
+Yes,Yes,Yes,Medium,Male,18-29,"$100,000 - $149,999",Bachelor degree
+No,Yes,No,Medium,Female,45-60,"$25,000 - $49,999",Some college or Associate degree
+No,Yes,No,Medium,Female,18-29,"$50,000 - $99,999",Bachelor degree
+Yes,Yes,Yes,Medium,Male,18-29,"$0 - $24,999",Some college or Associate degree
+No,No,No,Medium,Male,18-29,"$50,000 - $99,999",Bachelor degree
+No,Yes,No,Medium,Male,18-29,"$25,000 - $49,999",Bachelor degree
+No,Yes,No,Medium,Female,30-44,"$150,000+",Bachelor degree
+No,Yes,Yes,Medium,Female,18-29,"$100,000 - $149,999",Bachelor degree
+No,Yes,No,Medium,Female,18-29,"$150,000+",Some college or Associate degree
+No,Yes,Yes,Medium,Female,30-44,"$150,000+",Graduate degree
+No,Yes,Yes,Medium,Male,45-60,"$150,000+",Graduate degree
+No,Yes,No,Medium,,,,
+No,Yes,No,Medium,,,,
+No,Yes,No,Medium,Female,45-60,"$50,000 - $99,999",Some college or Associate degree
+Yes,Yes,Yes,Medium,Male,30-44,"$50,000 - $99,999",Bachelor degree
+No,Yes,No,Medium,Male,30-44,"$50,000 - $99,999",Graduate degree
+No,Yes,No,Medium,Female,18-29,"$100,000 - $149,999",Some college or Associate degree
+No,No,Yes,Medium,Male,45-60,,Some college or Associate degree
+No,Yes,Yes,Medium,Male,45-60,"$0 - $24,999",High school degree
+Yes,Yes,Yes,Medium,Female,> 60,"$25,000 - $49,999",Graduate degree
+No,Yes,Yes,Medium,Female,45-60,"$150,000+",Graduate degree
+No,Yes,No,Medium,Female,18-29,"$0 - $24,999",Some college or Associate degree
+No,Yes,Yes,Medium,Female,45-60,"$150,000+",Bachelor degree
+No,Yes,Yes,Medium,,,,
+Yes,Yes,Yes,Medium,Male,45-60,"$100,000 - $149,999",Bachelor degree
+No,Yes,No,Medium,Female,45-60,"$100,000 - $149,999",Some college or Associate degree
+No,Yes,No,Medium,Male,30-44,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,Yes,Medium,Female,30-44,"$100,000 - $149,999",Graduate degree
+No,No,Yes,Medium,Male,> 60,"$50,000 - $99,999",Bachelor degree
+No,No,No,Medium,Female,30-44,"$50,000 - $99,999",Bachelor degree
+No,Yes,Yes,Medium,Female,30-44,"$25,000 - $49,999",Some college or Associate degree
+No,Yes,Yes,Medium,Male,> 60,"$150,000+",Some college or Associate degree
+No,Yes,Yes,Medium,Female,> 60,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,Yes,Medium,Female,> 60,"$50,000 - $99,999",Graduate degree
+Yes,Yes,Yes,Medium,Male,45-60,"$50,000 - $99,999",Bachelor degree
+No,Yes,No,Medium,Female,30-44,"$150,000+",Bachelor degree
+No,Yes,Yes,Medium,Female,45-60,"$50,000 - $99,999",Graduate degree
+No,Yes,Yes,Medium,Female,> 60,"$50,000 - $99,999",Graduate degree
+No,Yes,No,Medium,,,,
+No,No,No,Medium,Female,18-29,,High school degree
+No,Yes,Yes,Medium,Male,45-60,"$150,000+",Graduate degree
+No,Yes,Yes,Medium,Female,> 60,,Graduate degree
+No,No,Yes,Medium,Male,45-60,"$100,000 - $149,999",Bachelor degree
+No,No,Yes,Medium,Female,> 60,"$50,000 - $99,999",High school degree
+No,No,No,Medium,Female,45-60,"$50,000 - $99,999",Graduate degree
+No,Yes,No,Medium,Female,30-44,"$100,000 - $149,999",Graduate degree
+No,No,No,Medium,Female,45-60,,Graduate degree
+No,Yes,Yes,Medium,Female,30-44,"$50,000 - $99,999",Bachelor degree
+,No,Yes,Medium,Female,45-60,,Bachelor degree
+No,Yes,No,Medium,Female,18-29,"$0 - $24,999",Bachelor degree
+No,Yes,Yes,Medium,Male,30-44,"$100,000 - $149,999",Graduate degree
+No,Yes,No,Medium,Male,18-29,"$25,000 - $49,999",Graduate degree
+Yes,Yes,No,Medium,Male,18-29,"$0 - $24,999",Some college or Associate degree
+Yes,Yes,Yes,Medium,Female,45-60,"$100,000 - $149,999",Some college or Associate degree
+No,Yes,No,Medium,Female,18-29,,Some college or Associate degree
+No,Yes,No,Medium,Female,18-29,"$0 - $24,999",Some college or Associate degree
+No,Yes,Yes,Medium,Male,45-60,"$0 - $24,999",Bachelor degree
+Yes,Yes,No,Medium,Female,45-60,"$150,000+",Bachelor degree
+No,Yes,No,Medium,Male,> 60,"$50,000 - $99,999",Graduate degree
+No,No,No,Medium,Female,> 60,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,Yes,Medium,Female,45-60,"$150,000+",Graduate degree
+No,Yes,Yes,Medium,Female,30-44,"$50,000 - $99,999",Some college or Associate degree
+Yes,Yes,Yes,Medium,Female,30-44,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,Yes,Medium,Male,30-44,"$100,000 - $149,999",Bachelor degree
+No,Yes,Yes,Medium,Male,> 60,,Graduate degree
+Yes,Yes,Yes,Medium,Male,18-29,"$0 - $24,999",
+No,Yes,No,Medium rare,Male,> 60,"$50,000 - $99,999",Some college or Associate degree
+No,No,No,Medium rare,Male,18-29,"$0 - $24,999",Some college or Associate degree
+No,Yes,Yes,Medium rare,Male,18-29,"$25,000 - $49,999",Some college or Associate degree
+No,Yes,Yes,Medium rare,Male,> 60,"$25,000 - $49,999",Some college or Associate degree
+No,Yes,No,Medium rare,Male,> 60,"$50,000 - $99,999",High school degree
+No,No,Yes,Medium rare,Male,18-29,"$100,000 - $149,999",Graduate degree
+No,Yes,Yes,Medium rare,Male,18-29,"$50,000 - $99,999",Bachelor degree
+No,No,No,Medium rare,Male,30-44,"$50,000 - $99,999",Graduate degree
+No,Yes,Yes,Medium rare,Male,18-29,"$25,000 - $49,999",Bachelor degree
+No,No,No,Medium rare,Male,> 60,"$25,000 - $49,999",Some college or Associate degree
+No,No,No,Medium rare,Male,45-60,"$0 - $24,999",Some college or Associate degree
+Yes,Yes,Yes,Medium rare,Male,45-60,"$50,000 - $99,999",Graduate degree
+No,No,No,Medium rare,Male,> 60,,Graduate degree
+No,Yes,Yes,Medium rare,Male,> 60,"$0 - $24,999",High school degree
+No,No,Yes,Medium rare,Male,45-60,"$0 - $24,999",Some college or Associate degree
+No,Yes,Yes,Medium rare,Male,> 60,"$150,000+",Bachelor degree
+No,Yes,No,Medium rare,Male,45-60,,High school degree
+No,No,No,Medium rare,Male,18-29,"$100,000 - $149,999",Some college or Associate degree
+No,Yes,Yes,Medium rare,Male,> 60,"$50,000 - $99,999",Bachelor degree
+No,Yes,No,Medium rare,Male,45-60,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,Yes,Medium rare,Male,45-60,,Some college or Associate degree
+No,Yes,No,Medium rare,,,,
+Yes,Yes,No,Medium rare,Male,18-29,"$0 - $24,999",Some college or Associate degree
+Yes,Yes,Yes,Medium rare,Male,45-60,,Graduate degree
+No,Yes,No,Medium rare,Female,> 60,"$100,000 - $149,999",Bachelor degree
+No,Yes,No,Medium rare,Male,45-60,,Some college or Associate degree
+No,No,Yes,Medium rare,Female,> 60,,Some college or Associate degree
+No,Yes,Yes,Medium rare,Male,45-60,"$100,000 - $149,999",Bachelor degree
+Yes,Yes,No,Medium rare,Male,18-29,,High school degree
+No,Yes,No,Medium rare,Female,> 60,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,Yes,Medium rare,Female,> 60,"$25,000 - $49,999",Graduate degree
+No,Yes,No,Medium rare,Female,> 60,"$100,000 - $149,999",Graduate degree
+Yes,No,Yes,Medium rare,Female,> 60,"$0 - $24,999",Bachelor degree
+No,Yes,No,Medium rare,Male,30-44,,Some college or Associate degree
+No,Yes,No,Medium rare,Male,18-29,"$0 - $24,999",Bachelor degree
+No,Yes,Yes,Medium rare,Male,45-60,"$100,000 - $149,999",High school degree
+No,No,Yes,Medium rare,Male,18-29,"$50,000 - $99,999",High school degree
+No,Yes,No,Medium rare,Male,45-60,"$100,000 - $149,999",Bachelor degree
+No,Yes,Yes,Medium rare,Male,30-44,"$100,000 - $149,999",Graduate degree
+No,No,No,Medium rare,Female,> 60,"$25,000 - $49,999",Bachelor degree
+No,Yes,Yes,Medium rare,Female,> 60,"$150,000+",Graduate degree
+No,Yes,No,Medium rare,Male,45-60,"$50,000 - $99,999",Graduate degree
+No,No,No,Medium rare,Male,45-60,"$50,000 - $99,999",Bachelor degree
+No,No,No,Medium rare,,,,
+No,No,No,Medium rare,Male,30-44,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,Yes,Medium rare,Male,18-29,"$25,000 - $49,999",Some college or Associate degree
+Yes,Yes,Yes,Medium rare,Female,18-29,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,Yes,Medium rare,Male,30-44,"$50,000 - $99,999",Bachelor degree
+No,Yes,No,Medium rare,Female,18-29,,Graduate degree
+No,Yes,No,Medium rare,,,,
+No,Yes,No,Medium rare,Female,> 60,,Some college or Associate degree
+No,No,No,Medium rare,Male,18-29,"$50,000 - $99,999",Some college or Associate degree
+Yes,Yes,No,Medium rare,Female,45-60,"$100,000 - $149,999",Graduate degree
+No,Yes,Yes,Medium rare,,,,
+No,Yes,No,Medium rare,Male,30-44,"$50,000 - $99,999",Graduate degree
+Yes,Yes,Yes,Medium rare,Female,18-29,"$150,000+",Graduate degree
+No,Yes,Yes,Medium rare,Male,> 60,,Bachelor degree
+No,Yes,No,Medium rare,Male,30-44,"$25,000 - $49,999",Some college or Associate degree
+No,Yes,Yes,Medium rare,Female,18-29,"$150,000+",Bachelor degree
+No,Yes,No,Medium rare,Male,> 60,,Graduate degree
+No,Yes,Yes,Medium rare,Male,45-60,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,No,Medium rare,Female,30-44,"$100,000 - $149,999",Graduate degree
+No,Yes,Yes,Medium rare,Female,45-60,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,Yes,Medium rare,Male,30-44,"$25,000 - $49,999",Bachelor degree
+No,Yes,Yes,Medium rare,Female,30-44,"$50,000 - $99,999",Bachelor degree
+No,Yes,No,Medium rare,Female,> 60,"$150,000+",Bachelor degree
+No,Yes,Yes,Medium rare,Female,> 60,"$25,000 - $49,999",High school degree
+No,Yes,No,Medium rare,Female,30-44,"$50,000 - $99,999",Bachelor degree
+No,Yes,Yes,Medium rare,Female,45-60,"$50,000 - $99,999",Graduate degree
+No,Yes,Yes,Medium rare,Male,> 60,"$50,000 - $99,999",Graduate degree
+No,Yes,No,Medium rare,Female,> 60,,Bachelor degree
+No,Yes,No,Medium rare,Female,45-60,,Graduate degree
+No,No,No,Medium rare,Female,> 60,"$25,000 - $49,999",Some college or Associate degree
+No,No,No,Medium rare,Male,45-60,,Some college or Associate degree
+No,Yes,Yes,Medium rare,Female,30-44,"$25,000 - $49,999",Bachelor degree
+Yes,Yes,Yes,Medium rare,Male,30-44,"$25,000 - $49,999",Some college or Associate degree
+Yes,Yes,No,Medium rare,Female,18-29,"$0 - $24,999",Graduate degree
+No,Yes,No,Medium rare,Female,45-60,"$50,000 - $99,999",Bachelor degree
+Yes,Yes,Yes,Medium rare,Female,18-29,"$100,000 - $149,999",Bachelor degree
+No,Yes,Yes,Medium rare,Male,> 60,"$50,000 - $99,999",Bachelor degree
+No,Yes,No,Medium rare,Female,18-29,"$25,000 - $49,999",Bachelor degree
+No,Yes,Yes,Medium rare,Male,30-44,"$100,000 - $149,999",Bachelor degree
+No,Yes,No,Medium rare,Female,30-44,"$0 - $24,999",Some college or Associate degree
+Yes,Yes,Yes,Medium rare,Female,18-29,"$25,000 - $49,999",Some college or Associate degree
+No,Yes,No,Medium rare,Male,30-44,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,No,Medium rare,Female,45-60,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,Yes,Medium rare,Male,30-44,"$25,000 - $49,999",Graduate degree
+No,Yes,Yes,Medium rare,Male,30-44,"$25,000 - $49,999",Some college or Associate degree
+No,Yes,Yes,Medium rare,Female,> 60,"$25,000 - $49,999",Some college or Associate degree
+Yes,Yes,No,Medium rare,Female,30-44,,Some college or Associate degree
+No,Yes,Yes,Medium rare,Male,45-60,"$25,000 - $49,999",Graduate degree
+No,Yes,Yes,Medium rare,Female,30-44,"$100,000 - $149,999",Bachelor degree
+No,Yes,No,Medium rare,Female,30-44,"$25,000 - $49,999",Graduate degree
+Yes,No,No,Medium rare,Female,45-60,"$100,000 - $149,999",Bachelor degree
+No,Yes,Yes,Medium rare,Female,45-60,"$150,000+",Bachelor degree
+No,No,Yes,Medium rare,Male,> 60,,High school degree
+No,No,No,Medium rare,Male,30-44,"$50,000 - $99,999",Graduate degree
+No,Yes,No,Medium rare,Female,30-44,"$100,000 - $149,999",Graduate degree
+No,Yes,No,Medium rare,Male,> 60,"$150,000+",Graduate degree
+Yes,Yes,Yes,Medium rare,Female,45-60,"$50,000 - $99,999",Some college or Associate degree
+Yes,No,No,Medium rare,Male,30-44,"$100,000 - $149,999",Bachelor degree
+No,Yes,No,Medium rare,Female,30-44,"$150,000+",Bachelor degree
+No,No,No,Medium rare,Female,> 60,"$50,000 - $99,999",Graduate degree
+No,No,No,Medium rare,Female,> 60,"$50,000 - $99,999",Graduate degree
+Yes,Yes,Yes,Medium rare,,,,
+No,Yes,Yes,Medium rare,,,,
+No,Yes,No,Medium rare,Male,> 60,"$100,000 - $149,999",Graduate degree
+No,Yes,Yes,Medium rare,Female,> 60,,High school degree
+Yes,Yes,No,Medium rare,Male,> 60,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,Yes,Medium rare,Female,> 60,"$50,000 - $99,999",Graduate degree
+Yes,Yes,Yes,Medium rare,Female,45-60,,Bachelor degree
+No,No,No,Medium rare,Male,> 60,"$50,000 - $99,999",Bachelor degree
+No,No,No,Medium rare,Female,> 60,,Some college or Associate degree
+No,No,No,Medium rare,Male,30-44,"$100,000 - $149,999",Some college or Associate degree
+No,Yes,Yes,Medium rare,Female,> 60,"$50,000 - $99,999",Graduate degree
+No,Yes,Yes,Medium rare,,,,
+No,Yes,Yes,Medium rare,Female,45-60,"$100,000 - $149,999",Bachelor degree
+No,Yes,Yes,Medium rare,Male,30-44,"$100,000 - $149,999",Bachelor degree
+No,Yes,Yes,Medium rare,Female,45-60,,Graduate degree
+No,Yes,Yes,Medium rare,Female,45-60,"$100,000 - $149,999",Graduate degree
+No,Yes,Yes,Medium rare,Male,> 60,"$50,000 - $99,999",Bachelor degree
+No,Yes,Yes,Medium rare,Female,45-60,"$50,000 - $99,999",Bachelor degree
+No,Yes,Yes,Medium rare,Male,45-60,"$50,000 - $99,999",Graduate degree
+No,No,No,Medium rare,Female,> 60,,Some college or Associate degree
+No,No,No,Medium rare,Female,18-29,"$25,000 - $49,999",High school degree
+No,No,Yes,Medium rare,Female,> 60,"$50,000 - $99,999",Bachelor degree
+No,Yes,No,Medium rare,Male,> 60,"$50,000 - $99,999",Graduate degree
+Yes,Yes,Yes,Medium rare,Female,18-29,"$50,000 - $99,999",Some college or Associate degree
+Yes,Yes,Yes,Medium rare,Female,30-44,"$50,000 - $99,999",Graduate degree
+No,Yes,No,Medium rare,Male,45-60,"$100,000 - $149,999",Bachelor degree
+No,Yes,No,Medium rare,Male,18-29,"$100,000 - $149,999",Bachelor degree
+No,Yes,No,Medium rare,Female,30-44,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,No,Medium rare,Female,> 60,"$50,000 - $99,999",Graduate degree
+Yes,Yes,Yes,Medium rare,Male,45-60,"$100,000 - $149,999",Bachelor degree
+No,Yes,Yes,Medium rare,,,,
+No,Yes,No,Medium rare,Female,30-44,"$50,000 - $99,999",Bachelor degree
+No,Yes,Yes,Medium rare,Male,30-44,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,Yes,Medium rare,Female,45-60,"$100,000 - $149,999",Some college or Associate degree
+No,Yes,Yes,Medium rare,Male,45-60,"$100,000 - $149,999",Bachelor degree
+No,No,No,Medium rare,Female,18-29,"$25,000 - $49,999",Some college or Associate degree
+Yes,Yes,Yes,Medium rare,Male,30-44,"$100,000 - $149,999",Bachelor degree
+No,Yes,No,Medium rare,Female,> 60,"$50,000 - $99,999",Graduate degree
+Yes,No,No,Medium rare,Female,45-60,"$0 - $24,999",Bachelor degree
+No,Yes,Yes,Medium rare,Male,45-60,"$150,000+",Graduate degree
+No,Yes,Yes,Medium rare,Female,45-60,,Graduate degree
+No,No,No,Medium rare,Female,45-60,"$25,000 - $49,999",High school degree
+No,No,Yes,Medium rare,Female,18-29,"$25,000 - $49,999",Graduate degree
+Yes,Yes,Yes,Medium rare,Female,30-44,,Some college or Associate degree
+No,Yes,No,Medium rare,Female,30-44,,Graduate degree
+Yes,Yes,No,Medium rare,Male,30-44,"$50,000 - $99,999",Some college or Associate degree
+No,No,No,Medium rare,Female,45-60,"$0 - $24,999",Some college or Associate degree
+No,No,No,Medium rare,Female,> 60,"$50,000 - $99,999",Graduate degree
+Yes,Yes,No,Medium rare,Male,18-29,"$0 - $24,999",Some college or Associate degree
+No,Yes,No,Medium rare,Male,18-29,"$25,000 - $49,999",High school degree
+No,Yes,No,Medium rare,Female,> 60,,Graduate degree
+No,Yes,No,Medium rare,Female,30-44,"$50,000 - $99,999",Some college or Associate degree
+No,No,No,Medium rare,Male,30-44,"$50,000 - $99,999",Graduate degree
+Yes,Yes,Yes,Medium rare,Male,45-60,,High school degree
+No,Yes,Yes,Medium rare,Male,30-44,"$100,000 - $149,999",Bachelor degree
+No,Yes,Yes,Medium rare,Female,30-44,"$100,000 - $149,999",Graduate degree
+No,Yes,No,Medium rare,Male,> 60,"$150,000+",Bachelor degree
+No,Yes,No,Medium rare,Female,45-60,,Some college or Associate degree
+Yes,Yes,Yes,Medium rare,Female,18-29,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,Yes,Medium rare,Male,> 60,"$50,000 - $99,999",Bachelor degree
+No,No,No,Medium rare,Female,18-29,"$25,000 - $49,999",High school degree
+Yes,Yes,Yes,Medium rare,Male,30-44,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,No,Medium Well,Male,> 60,"$50,000 - $99,999",Bachelor degree
+No,Yes,No,Medium Well,Male,18-29,"$25,000 - $49,999",Some college or Associate degree
+Yes,Yes,Yes,Medium Well,,,,
+Yes,Yes,Yes,Medium Well,Male,18-29,"$50,000 - $99,999",High school degree
+No,Yes,Yes,Medium Well,Male,45-60,"$50,000 - $99,999",Some college or Associate degree
+No,No,No,Medium Well,Male,45-60,"$50,000 - $99,999",Bachelor degree
+No,Yes,Yes,Medium Well,Male,45-60,,Some college or Associate degree
+Yes,Yes,No,Medium Well,Male,> 60,"$50,000 - $99,999",Graduate degree
+No,No,,Medium Well,Male,> 60,"$50,000 - $99,999",Some college or Associate degree
+Yes,Yes,Yes,Medium Well,Male,30-44,"$50,000 - $99,999",Bachelor degree
+No,No,Yes,Medium Well,Male,> 60,"$25,000 - $49,999",Some college or Associate degree
+No,Yes,No,Medium Well,Male,> 60,"$150,000+",Bachelor degree
+No,Yes,Yes,Medium Well,Male,45-60,"$100,000 - $149,999",Bachelor degree
+No,Yes,Yes,Medium Well,Female,> 60,"$0 - $24,999",High school degree
+No,Yes,Yes,Medium Well,Male,> 60,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,Yes,Medium Well,Male,30-44,"$25,000 - $49,999",Some college or Associate degree
+No,Yes,No,Medium Well,Female,> 60,,Bachelor degree
+No,No,No,Medium Well,Female,> 60,,Graduate degree
+No,Yes,Yes,Medium Well,Male,> 60,"$25,000 - $49,999",Some college or Associate degree
+No,Yes,Yes,Medium Well,Male,> 60,"$150,000+",Graduate degree
+Yes,No,No,Medium Well,Female,18-29,"$25,000 - $49,999",High school degree
+No,Yes,No,Medium Well,Male,45-60,"$50,000 - $99,999",Some college or Associate degree
+No,No,No,Medium Well,Female,18-29,"$100,000 - $149,999",Some college or Associate degree
+No,No,No,Medium Well,Female,18-29,"$0 - $24,999",Some college or Associate degree
+No,Yes,Yes,Medium Well,Female,30-44,"$50,000 - $99,999",Graduate degree
+No,Yes,No,Medium Well,Female,45-60,,Graduate degree
+No,Yes,Yes,Medium Well,Male,45-60,"$100,000 - $149,999",Bachelor degree
+No,No,No,Medium Well,Female,> 60,,High school degree
+No,Yes,Yes,Medium Well,Female,30-44,"$100,000 - $149,999",Graduate degree
+No,Yes,No,Medium Well,Female,45-60,"$50,000 - $99,999",Graduate degree
+No,Yes,Yes,Medium Well,Female,30-44,"$25,000 - $49,999",Bachelor degree
+No,Yes,No,Medium Well,Female,18-29,"$50,000 - $99,999",Graduate degree
+No,Yes,No,Medium Well,Female,18-29,"$0 - $24,999",Some college or Associate degree
+No,Yes,Yes,Medium Well,Female,> 60,,Some college or Associate degree
+Yes,Yes,Yes,Medium Well,Female,30-44,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,No,Medium Well,Female,30-44,"$50,000 - $99,999",Bachelor degree
+No,Yes,No,Medium Well,Male,45-60,"$50,000 - $99,999",Bachelor degree
+No,No,No,Medium Well,Male,18-29,,Some college or Associate degree
+No,No,No,Medium Well,Female,30-44,"$25,000 - $49,999",Some college or Associate degree
+No,Yes,No,Medium Well,Male,18-29,"$25,000 - $49,999",Some college or Associate degree
+No,No,No,Medium Well,Female,45-60,"$150,000+",Bachelor degree
+No,Yes,No,Medium Well,Male,18-29,"$0 - $24,999",Bachelor degree
+No,Yes,Yes,Medium Well,Female,18-29,"$50,000 - $99,999",Graduate degree
+No,Yes,Yes,Medium Well,Female,45-60,"$50,000 - $99,999",Bachelor degree
+No,Yes,Yes,Medium Well,,,,
+No,Yes,No,Medium Well,Female,30-44,"$150,000+",Graduate degree
+No,Yes,No,Medium Well,,,,
+No,Yes,Yes,Medium Well,Male,45-60,"$100,000 - $149,999",Some college or Associate degree
+No,Yes,No,Medium Well,Female,45-60,"$150,000+",Some college or Associate degree
+No,Yes,No,Medium Well,Female,18-29,"$0 - $24,999",Bachelor degree
+No,No,,Medium Well,Female,> 60,"$50,000 - $99,999",Graduate degree
+No,Yes,No,Medium Well,Female,30-44,"$25,000 - $49,999",Bachelor degree
+No,Yes,No,Medium Well,Female,18-29,"$50,000 - $99,999",Bachelor degree
+No,Yes,No,Medium Well,,,,
+No,Yes,No,Medium Well,Female,30-44,"$25,000 - $49,999",Some college or Associate degree
+No,Yes,No,Medium Well,Female,30-44,"$50,000 - $99,999",Graduate degree
+No,Yes,Yes,Medium Well,Female,> 60,"$50,000 - $99,999",Some college or Associate degree
+Yes,Yes,No,Medium Well,Female,30-44,"$100,000 - $149,999",Graduate degree
+Yes,Yes,No,Medium Well,Male,18-29,"$0 - $24,999",Bachelor degree
+No,Yes,Yes,Medium Well,Female,45-60,"$25,000 - $49,999",Some college or Associate degree
+No,Yes,No,Medium Well,Female,18-29,,Bachelor degree
+Yes,Yes,Yes,Medium Well,Male,> 60,"$0 - $24,999",Bachelor degree
+No,No,No,Medium Well,Male,> 60,"$100,000 - $149,999",Graduate degree
+No,Yes,No,Medium Well,Male,45-60,"$150,000+",Bachelor degree
+No,Yes,Yes,Medium Well,Male,18-29,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,Yes,Medium Well,Male,45-60,,Graduate degree
+Yes,No,No,Medium Well,Female,45-60,"$25,000 - $49,999",Some college or Associate degree
+No,Yes,Yes,Medium Well,,,,
+No,Yes,Yes,Medium Well,Male,30-44,"$50,000 - $99,999",Graduate degree
+No,No,Yes,Medium Well,Female,30-44,"$0 - $24,999",Some college or Associate degree
+No,Yes,No,Medium Well,Male,> 60,"$150,000+",Graduate degree
+Yes,Yes,Yes,Medium Well,Male,45-60,"$100,000 - $149,999",Some college or Associate degree
+No,Yes,No,Medium Well,Female,> 60,"$100,000 - $149,999",Bachelor degree
+No,No,Yes,Medium Well,Male,30-44,"$50,000 - $99,999",Graduate degree
+,No,,Medium Well,Female,30-44,"$0 - $24,999",Bachelor degree
+No,Yes,Yes,Rare,Male,> 60,"$150,000+",Graduate degree
+Yes,Yes,Yes,Rare,Male,18-29,"$50,000 - $99,999",Graduate degree
+No,Yes,No,Rare,Male,> 60,,Graduate degree
+No,Yes,No,Rare,Male,30-44,"$25,000 - $49,999",Bachelor degree
+Yes,Yes,Yes,Rare,Male,> 60,,Bachelor degree
+No,Yes,Yes,Rare,Male,45-60,"$100,000 - $149,999",Bachelor degree
+Yes,No,No,Rare,Female,> 60,"$0 - $24,999",Some college or Associate degree
+,Yes,No,Rare,Male,18-29,"$50,000 - $99,999",High school degree
+No,Yes,Yes,Rare,Female,30-44,"$150,000+",Some college or Associate degree
+No,Yes,No,Rare,Female,> 60,"$25,000 - $49,999",Graduate degree
+No,Yes,Yes,Rare,Male,45-60,"$100,000 - $149,999",Graduate degree
+Yes,Yes,Yes,Rare,Female,30-44,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,Yes,Rare,Female,> 60,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,Yes,Rare,Female,45-60,"$100,000 - $149,999",Graduate degree
+No,Yes,No,Rare,Female,> 60,"$100,000 - $149,999",Bachelor degree
+No,Yes,No,Rare,Female,45-60,,Bachelor degree
+No,No,Yes,Rare,Female,30-44,"$0 - $24,999",Some college or Associate degree
+No,No,No,Rare,Female,45-60,"$50,000 - $99,999",Bachelor degree
+No,Yes,No,Rare,Female,45-60,"$50,000 - $99,999",Graduate degree
+Yes,Yes,Yes,Rare,,,,
+Yes,Yes,Yes,Rare,Male,18-29,"$25,000 - $49,999",Some college or Associate degree
+No,Yes,No,Rare,Female,18-29,"$50,000 - $99,999",Bachelor degree
+No,Yes,No,Rare,Male,> 60,"$150,000+",Graduate degree
+No,Yes,No,Well,Male,18-29,"$25,000 - $49,999",High school degree
+Yes,No,No,Well,Male,18-29,"$150,000+",
+No,No,Yes,Well,Male,45-60,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,No,Well,Male,18-29,,Some college or Associate degree
+No,No,Yes,Well,Female,18-29,"$25,000 - $49,999",Some college or Associate degree
+Yes,Yes,Yes,Well,Male,45-60,"$50,000 - $99,999",Bachelor degree
+No,Yes,No,Well,,,,
+No,No,Yes,Well,Female,45-60,,Some college or Associate degree
+No,Yes,Yes,Well,Male,45-60,"$100,000 - $149,999",Bachelor degree
+No,No,No,Well,Male,30-44,,Graduate degree
+No,Yes,No,Well,Female,> 60,"$50,000 - $99,999",Bachelor degree
+No,Yes,Yes,Well,Male,> 60,"$0 - $24,999",Bachelor degree
+Yes,Yes,No,Well,Female,18-29,"$100,000 - $149,999",Less than high school degree
+No,Yes,Yes,Well,Female,30-44,"$50,000 - $99,999",Graduate degree
+No,No,No,Well,Female,30-44,"$50,000 - $99,999",Bachelor degree
+No,Yes,Yes,Well,Female,45-60,"$100,000 - $149,999",Bachelor degree
+No,Yes,No,Well,Female,45-60,"$50,000 - $99,999",Bachelor degree
+No,No,No,Well,Female,45-60,"$150,000+",Bachelor degree
+No,Yes,Yes,Well,Female,45-60,"$100,000 - $149,999",Some college or Associate degree
+No,No,No,Well,Male,45-60,"$50,000 - $99,999",Bachelor degree
+No,No,No,Well,Female,30-44,,High school degree
+Yes,Yes,Yes,Well,Female,18-29,,Some college or Associate degree
+Yes,Yes,Yes,Well,Male,30-44,"$0 - $24,999",High school degree
+No,Yes,No,Well,Female,45-60,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,No,Well,Female,30-44,,Some college or Associate degree
+No,Yes,Yes,Well,Female,> 60,,Graduate degree
+No,No,No,Well,Female,30-44,,Graduate degree
+No,Yes,Yes,Well,Male,30-44,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,No,Well,Female,> 60,"$50,000 - $99,999",Bachelor degree
+No,Yes,Yes,Well,Male,30-44,,Bachelor degree
+No,Yes,Yes,Well,Female,45-60,,Some college or Associate degree
+No,Yes,Yes,Well,Female,45-60,"$150,000+",High school degree
+,No,No,Well,Male,45-60,"$50,000 - $99,999",Bachelor degree
+No,Yes,No,Well,Male,30-44,"$0 - $24,999",Some college or Associate degree
+No,No,Yes,Well,Female,30-44,,Some college or Associate degree
+No,Yes,Yes,Well,Female,> 60,"$100,000 - $149,999",Bachelor degree
\ No newline at end of file
diff --git a/.ipynb_checkpoints/untitled-checkpoint.txt b/.ipynb_checkpoints/untitled-checkpoint.txt
new file mode 100644
index 0000000..e69de29
diff --git a/538.jpg b/538.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..397bb082dfbad579bdf9481fb3d722902054f6a0
GIT binary patch
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diff --git a/Untitled.ipynb b/Untitled.ipynb
new file mode 100644
index 0000000..363fcab
--- /dev/null
+++ b/Untitled.ipynb
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Untitled1.ipynb b/Untitled1.ipynb
new file mode 100644
index 0000000..363fcab
--- /dev/null
+++ b/Untitled1.ipynb
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/argument.ipynb b/argument.ipynb
index 4ed27b4..aaf5cc1 100644
--- a/argument.ipynb
+++ b/argument.ipynb
@@ -2,89 +2,306 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "worldwide-blood",
+   "id": "42901bdc-1637-4bad-b7fd-d901aaa7488b",
    "metadata": {},
    "source": [
-    "# Introduction"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "understanding-numbers",
-   "metadata": {},
-   "source": [
-    "*✏️ Write 2-3 sentences describing your research.*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "greater-circular",
-   "metadata": {},
-   "source": [
-    "## Overarching Question: [✏️ PUT YOUR QUESTION HERE ✏️]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "appreciated-testimony",
-   "metadata": {},
-   "source": [
-    "*✏️ Write 2-3 sentences explaining why this question.*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "permanent-pollution",
-   "metadata": {},
-   "source": [
-    "# Data"
+    "## **OVERARCHING QUESTION:** *DO PEOPLE WHO EXIBIT RISKY BEHAVIORS (SMOKING, GAMBLING, DRINKING) ORDER THEIR STEAK IN THE SAME RISKY MANNER AS THEIR BEAHVIORS (RARE). IS THERE A RELATIONSHIP BETWEEN THE TWO.*\n",
+    "![alt text](risk.jpg)\n",
+    "\n",
+    "**Why this question**\n",
+    "\n",
+    "THE CENTRAL QUESTION WOULD ASK DO PEOPLE (males and females) WHO HAVE AN AFFINITY FOR RISKY BEHAVIORS, SPECIFICALLY (SMOKING, DRINKING, GAMBLING) ALSO HAVE A RISKY BEHAVIOR WHEN IT COMES TO EATING THEIR FOOD. SPECIFICALLY IN THIS STUDY, IT IS ABOUT ORDERING STEAK.\n",
+    "I AM INTERESTED IN THIS QUESTION BECASUE IT IS INTERESTING TO SEE IF FOOD RISK BEHAVIORS ARE ASSOCIATED WITH ACTIVITY RISK BEHAVIORS. I ALSO AM INTERESTED TO SEE IN THIS STUDY HOW INCOME LEVELS EFFECT THESE DECISIONS AS WELL AS EDUCATION LEVELS, WHICH IS ALSO IN THE STUDY.\n",
+    "\n",
+    "![alt text](r.jpg)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "id": "technical-evans",
    "metadata": {},
    "outputs": [],
    "source": [
     "#Include any import statements you will need\n",
     "import pandas as pd\n",
-    "import matplotlib.pyplot as plt"
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set_theme()\n",
+    "steak = pd.read_csv(\"steak.csv\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "id": "overhead-sigma",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>smoke</th>\n",
+       "      <th>drink</th>\n",
+       "      <th>gamble</th>\n",
+       "      <th>cooked</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Household Income</th>\n",
+       "      <th>Education</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Medium</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>&gt; 60</td>\n",
+       "      <td>$50,000 - $99,999</td>\n",
+       "      <td>Bachelor degree</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Medium</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>&gt; 60</td>\n",
+       "      <td>$50,000 - $99,999</td>\n",
+       "      <td>Graduate degree</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Medium</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>18-29</td>\n",
+       "      <td>$50,000 - $99,999</td>\n",
+       "      <td>Bachelor degree</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Medium</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>18-29</td>\n",
+       "      <td>$50,000 - $99,999</td>\n",
+       "      <td>Bachelor degree</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Medium</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>18-29</td>\n",
+       "      <td>$50,000 - $99,999</td>\n",
+       "      <td>Bachelor degree</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>427</th>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Well</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>45-60</td>\n",
+       "      <td>$150,000+</td>\n",
+       "      <td>High school degree</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>428</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Well</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>45-60</td>\n",
+       "      <td>$50,000 - $99,999</td>\n",
+       "      <td>Bachelor degree</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>429</th>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Well</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>30-44</td>\n",
+       "      <td>$0 - $24,999</td>\n",
+       "      <td>Some college or Associate degree</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>430</th>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Well</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>30-44</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Some college or Associate degree</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>431</th>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Well</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>&gt; 60</td>\n",
+       "      <td>$100,000 - $149,999</td>\n",
+       "      <td>Bachelor degree</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>432 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    smoke drink gamble  cooked  Gender    Age     Household Income  \\\n",
+       "0     Yes   Yes    Yes  Medium    Male   > 60    $50,000 - $99,999   \n",
+       "1      No   Yes     No  Medium    Male   > 60    $50,000 - $99,999   \n",
+       "2     Yes   Yes    Yes  Medium    Male  18-29    $50,000 - $99,999   \n",
+       "3      No   Yes    Yes  Medium    Male  18-29    $50,000 - $99,999   \n",
+       "4      No   Yes    Yes  Medium    Male  18-29    $50,000 - $99,999   \n",
+       "..    ...   ...    ...     ...     ...    ...                  ...   \n",
+       "427    No   Yes    Yes    Well  Female  45-60            $150,000+   \n",
+       "428   NaN    No     No    Well    Male  45-60    $50,000 - $99,999   \n",
+       "429    No   Yes     No    Well    Male  30-44         $0 - $24,999   \n",
+       "430    No    No    Yes    Well  Female  30-44                  NaN   \n",
+       "431    No   Yes    Yes    Well  Female   > 60  $100,000 - $149,999   \n",
+       "\n",
+       "                            Education  \n",
+       "0                     Bachelor degree  \n",
+       "1                     Graduate degree  \n",
+       "2                     Bachelor degree  \n",
+       "3                     Bachelor degree  \n",
+       "4                     Bachelor degree  \n",
+       "..                                ...  \n",
+       "427                High school degree  \n",
+       "428                   Bachelor degree  \n",
+       "429  Some college or Associate degree  \n",
+       "430  Some college or Associate degree  \n",
+       "431                   Bachelor degree  \n",
+       "\n",
+       "[432 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "### 💻 FILL IN YOUR DATASET FILE NAME BELOW 💻 ###\n",
-    "\n",
-    "file_name = \"YOUR_DATASET_FILE_NAME.csv\"\n",
-    "dataset_path = \"data/\" + file_name\n",
-    "\n",
-    "df = pd.read_csv(dataset_path)"
+    "steak"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "heated-blade",
+   "execution_count": 30,
+   "id": "8d7c325c-ef3f-4072-a51c-1e87325a3c96",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Gender\n",
+       "Male      212\n",
+       "Female    200\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "df.head()"
+    "steak.Gender.value_counts()"
    ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "2899053c-9446-4772-8cc2-113a238b967a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Gender', ylabel='Count'>"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.histplot(data=steak, x=\"Gender\")"
+   ]
+  },
+  {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "continental-franklin",
    "metadata": {},
    "source": [
-    "**Data Overview**\n",
+    "**What is the data about and where is it from?**\n",
     "\n",
-    "*✏️ Write 2-3 sentences describing this dataset. Be sure to include where the data comes from and what it contains.*"
+    "![alt text](538.jpg)\n",
+    "\n",
+    "This is a data set from the website 538. It was a data set collected by Walt Hickey. It is a steak risky survey. The data examines how people value and interpret risk in life, and does this have a correlation to how they order their steak. 550 people were surveyed. It seeks to interpret if people who do risky behaviors (smoke, gamble, drink) order their steak in the same manner (rare) and risk food borne illness."
    ]
   },
   {
@@ -95,22 +312,12 @@
     "# Methods and Results"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "basic-canadian",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Import any helper files you need here"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "recognized-positive",
    "metadata": {},
    "source": [
-    "## First Research Question: [✏️ PUT YOUR QUESTION HERE ✏️]\n"
+    "## First Research Question: *Do people (males and females) who drink also order their steak rare?*\n"
    ]
   },
   {
@@ -129,9 +336,18 @@
     "*Explain how you will approach this research question below. Consider the following:* \n",
     "  - *Which aspects of the dataset will you use?* \n",
     "  - *How will you reorganize/store the data?* \n",
-    "  - *What data science tools/functions will you use and why?* \n",
-    "  \n",
-    "✏️ *Write your answer below:*\n",
+    "  - *What data science tools/functions will you use and why?*\n",
+    "\n",
+    "\n",
+    "**I will use the columns Drink/Cooked to answer this question.\n",
+    "I will...\n",
+    "\n",
+    "-get the value of people who drink\n",
+    "-get the value of people who order steak rare\n",
+    "-get the value of people who do both drinking and order their steak rare\n",
+    "\n",
+    "I will use pandas to handle my data (as data frames), seaborne to create visualizations\n",
+    "\n",
     "\n"
    ]
   },
@@ -140,31 +356,231 @@
    "id": "portuguese-japan",
    "metadata": {},
    "source": [
-    "### Results "
+    "# Results "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "68678a65-6dd4-4fff-bb4e-70492249e021",
+   "metadata": {},
+   "source": [
+    "### Number of people who Drink"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 32,
    "id": "negative-highlight",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "drink\n",
+       "Yes    339\n",
+       "No      93\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "#######################################################################\n",
-    "### 💻 YOUR WORK GOES HERE TO ANSWER THE FIRST RESEARCH QUESTION 💻 \n",
-    "### \n",
-    "### Your data analysis may include a statistic and/or a data visualization\n",
-    "#######################################################################"
+    "steak.drink.value_counts()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 33,
    "id": "victorian-burning",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='drink', ylabel='Count'>"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# 💻 YOU CAN ADD NEW CELLS WITH THE \"+\" BUTTON "
+    "sns.histplot(data=steak, x=\"drink\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e0ec8c49-e490-4bac-927a-0f3c50c72c8d",
+   "metadata": {},
+   "source": [
+    "### Number of people who like their steak rare\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "46f2704d-6336-4b76-a7db-8cde388f7d55",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "cooked\n",
+       "Medium rare    166\n",
+       "Medium         132\n",
+       "Medium Well     75\n",
+       "Well            36\n",
+       "Rare            23\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "steak.cooked.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "45451f54-ebc8-4acc-aec7-64ecd9842709",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='cooked', ylabel='Count'>"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.histplot(data=steak, x=\"cooked\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ec996656-8880-4077-bc4f-01e7d600dece",
+   "metadata": {},
+   "source": [
+    "### Number of people who like their steak rare, and who drink"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "5d1555da-3c4f-4105-b7b7-83a1bfb3f833",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "drink  cooked     \n",
+      "No     Medium          23\n",
+      "       Medium Well     17\n",
+      "       Medium rare     38\n",
+      "       Rare             3\n",
+      "       Well            12\n",
+      "Yes    Medium         109\n",
+      "       Medium Well     58\n",
+      "       Medium rare    128\n",
+      "       Rare            20\n",
+      "       Well            24\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "counts = steak.groupby([\"drink\", 'cooked']).size()\n",
+    "print(counts)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "32ca9aa2-e1ad-4563-b2de-55326f187e1f",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='drink', ylabel='count'>"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(x='drink', hue='cooked', data=steak)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "a72f700e-4d9b-46d9-a216-69b754fd409f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of people who drink and order their steak rare: 20\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = steak.dropna(subset=['drink', 'cooked'])\n",
+    "drink_rare = df[(df['drink'].str.lower() == 'yes') & (df['cooked'].str.lower() == 'rare')]\n",
+    "count_drink_rare = len(drink_rare)\n",
+    "print(f\"Number of people who drink and order their steak rare: {count_drink_rare}\")"
    ]
   },
   {
@@ -172,7 +588,7 @@
    "id": "collectible-puppy",
    "metadata": {},
    "source": [
-    "## Second Research Question: [✏️ PUT YOUR QUESTION HERE ✏️]\n"
+    "## Second Research Question: *Do people (males and females) who gamble also order their steak rare?*\n"
    ]
   },
   {
@@ -193,7 +609,16 @@
     "  - *How will you reorganize/store the data?* \n",
     "  - *What data science tools/functions will you use and why?* \n",
     "\n",
-    "✏️ *Write your answer below:*\n"
+    "✏️ *Write your answer below:*\n",
+    "\n",
+    "**I will use the columns Gamble/Cooked to answer this question.\n",
+    "I will...\n",
+    "\n",
+    "-get the value of people who gamble\n",
+    "-get the value of people who order steak rare\n",
+    "-get the value of people who do both gamble and order their steak rare\n",
+    "\n",
+    "I will use pandas to handle my data (as data frames), seaborne to create visualizations\n"
    ]
   },
   {
@@ -201,64 +626,501 @@
    "id": "juvenile-creation",
    "metadata": {},
    "source": [
-    "### Results "
+    "# Results "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8d239cc7-d973-452d-a462-7317ab8fe5f2",
+   "metadata": {},
+   "source": [
+    "### Number of people who gamble\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 39,
    "id": "pursuant-surrey",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "gamble\n",
+       "Yes    216\n",
+       "No     213\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "#######################################################################\n",
-    "### 💻 YOUR WORK GOES HERE TO ANSWER THE SECOND RESEARCH QUESTION 💻 \n",
-    "###\n",
-    "### Your data analysis may include a statistic and/or a data visualization\n",
-    "#######################################################################"
+    "steak.gamble.value_counts()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 40,
    "id": "located-night",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='gamble', ylabel='Count'>"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# 💻 YOU CAN ADD NEW CELLS WITH THE \"+\" BUTTON "
+    "sns.histplot(data=steak, x=\"gamble\")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "infectious-symbol",
+   "id": "e213a7eb-6a17-4db7-bda6-200200421688",
    "metadata": {},
    "source": [
-    "# Discussion"
+    "### Number of people who like their steak rare\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "c520afe4-e348-4573-a6f3-9968cc8b594b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "cooked\n",
+       "Medium rare    166\n",
+       "Medium         132\n",
+       "Medium Well     75\n",
+       "Well            36\n",
+       "Rare            23\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "steak.cooked.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "b7fd5c7a-a8cb-4253-912b-d062f88caf25",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='cooked', ylabel='Count'>"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.histplot(data=steak, x=\"cooked\")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "furnished-camping",
+   "id": "996d3496-8057-4c0f-b083-ead62e394fde",
+   "metadata": {},
+   "source": [
+    "### Number of people who like their steak rare, and who gamble"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "f75ee0b2-8ed2-40d2-9a6e-dd839663f364",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "gamble  cooked     \n",
+      "No      Medium         59\n",
+      "        Medium Well    40\n",
+      "        Medium rare    85\n",
+      "        Rare           11\n",
+      "        Well           18\n",
+      "Yes     Medium         73\n",
+      "        Medium Well    32\n",
+      "        Medium rare    81\n",
+      "        Rare           12\n",
+      "        Well           18\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "counts = df.groupby([\"gamble\", 'cooked']).size()\n",
+    "print(counts)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "8082a773-1acd-4da5-beef-e78b82e4bca7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='gamble', ylabel='count'>"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(x='gamble', hue='cooked', data=df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "506a2001-d2dc-4e66-b330-699d5194db2d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of people who gamble and order their steak rare: 12\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = df.dropna(subset=['gamble', 'cooked'])\n",
+    "gamble_rare = df[(df['gamble'].str.lower() == 'yes') & (df['cooked'].str.lower() == 'rare')]\n",
+    "count_gamble_rare = len(gamble_rare)\n",
+    "print(f\"Number of people who gamble and order their steak rare: {count_gamble_rare}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "87df3b25-3df9-42a8-884f-2a9c1a362bf4",
+   "metadata": {},
+   "source": [
+    "## Third Research Question: *Do people (males and females) who smoke also order their steak rare?*\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f20e80e3-f582-4422-ba20-5ff133730a79",
+   "metadata": {},
+   "source": [
+    "### Methods\n",
+    "*Explain how you will approach this research question below. Consider the following:* \n",
+    "  - *Which aspects of the dataset will you use?* \n",
+    "  - *How will you reorganize/store the data?* \n",
+    "  - *What data science tools/functions will you use and why?* \n",
+    "\n",
+    "✏️ *Write your answer below:*\n",
+    "\n",
+    "**I will use the columns Smoke/Cooked to answer this question.\n",
+    "I will...\n",
+    "\n",
+    "-get the value of people who smoke\n",
+    "-get the value of people who order steak rare\n",
+    "-get the value of people who do both smoke and order their steak rare\n",
+    "\n",
+    "I will use pandas to handle my data (as data frames), seaborne to create visualizations\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e3a6ce78-a232-440d-be07-d5263b922bd7",
+   "metadata": {},
+   "source": [
+    "# Results "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fdf13331-0905-49a9-9da6-97124c967248",
+   "metadata": {},
+   "source": [
+    "### Number of people who smoke\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "22f25f57-c47a-4b73-9fc6-12a5dcd85d2f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "smoke\n",
+       "No     356\n",
+       "Yes     72\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "steak.smoke.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "2377f2bc-3aa3-4d92-a128-c4cc9eaf7d35",
    "metadata": {
-    "code_folding": []
+    "scrolled": true
    },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='smoke', ylabel='Count'>"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "## Considerations"
+    "sns.histplot(data=steak, x=\"smoke\")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "bearing-stadium",
+   "id": "baefe767-40fb-4afe-ae12-2374ce33ece4",
    "metadata": {},
    "source": [
-    "*It's important to recognize the limitations of our research.\n",
-    "Consider the following:*\n",
-    "\n",
-    "- *Do the results give an accurate depiction of your research question? Why or why not?*\n",
-    "- *What were limitations of your datset?*\n",
-    "- *Are there any known biases in the data?*\n",
-    "\n",
-    "✏️ *Write your answer below:*"
+    "### Number of people who like their steak rare\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "2e62dead-0ed2-422b-abdb-c0d359732689",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "cooked\n",
+       "Medium rare    166\n",
+       "Medium         132\n",
+       "Medium Well     75\n",
+       "Well            36\n",
+       "Rare            23\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "steak.cooked.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "09aec01a-465f-49e7-8b2f-f6e3c74720da",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='cooked', ylabel='Count'>"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.histplot(data=steak, x=\"cooked\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ee1b058c-71de-4141-b9e6-c1e66ebb6033",
+   "metadata": {},
+   "source": [
+    "### Number of people who like their steak rare, and who smoke"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "6abbe66a-565d-47a1-a6b0-b676c0362261",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "smoke  cooked     \n",
+      "No     Medium         111\n",
+      "       Medium Well     61\n",
+      "       Medium rare    136\n",
+      "       Rare            16\n",
+      "       Well            30\n",
+      "Yes    Medium          20\n",
+      "       Medium Well     11\n",
+      "       Medium rare     30\n",
+      "       Rare             6\n",
+      "       Well             5\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "counts = df.groupby([\"smoke\", 'cooked']).size()\n",
+    "print(counts)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "4751cff3-a23a-449d-b8f3-c52072d02768",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='smoke', ylabel='count'>"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(x='smoke', hue='cooked', data=df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "81274425-b884-4604-a2d4-7f2cc6b4a1c2",
+   "metadata": {},
+   "source": [
+    "### Number of people who like their steak rare, and who do all 3 risky behaviors(smoke, drink, gamble)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "8817c157-0528-47c7-8680-c886dd5499f4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of people who smoke, drink, and gamble who order their steak rare: 5\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = df.dropna(subset=['smoke', 'drink', 'gamble', 'cooked'])\n",
+    "smoke_drink_gamble_rare = df[\n",
+    "    (df['smoke'].str.lower() == 'yes') &\n",
+    "    (df['drink'].str.lower() == 'yes') &\n",
+    "    (df['gamble'].str.lower() == 'yes') &\n",
+    "    (df['cooked'].str.lower() == 'rare')\n",
+    "]\n",
+    "count_smoke_drink_gamble_rare = len(smoke_drink_gamble_rare)\n",
+    "print(f\"Number of people who smoke, drink, and gamble who order their steak rare: {count_smoke_drink_gamble_rare}\")\n"
    ]
   },
   {
@@ -274,14 +1136,70 @@
    "id": "about-raise",
    "metadata": {},
    "source": [
-    "*Summarize what you discovered through the research. Consider the following:*\n",
     "\n",
-    "- *What did you learn about your media consumption/digital habits?*\n",
-    "- *Did the results make sense?*\n",
-    "- *What was most surprising?*\n",
-    "- *How will this project impact you going forward?*\n",
     "\n",
-    "✏️ *Write your answer below:*"
+    "**OVERARCHING QUESTION**\n",
+    "*DO PEOPLE WHO EXIBIT RISKY BEHAVIORS (SMOKING, GAMBLING, DRINKING) ORDER THEIR STEAK IN THE SAME RISKY MANNER AS THEIR BEAHVIORS (RARE). IS THERE A RELATIONSHIP BETWEEN THE TWO?*\n",
+    "\n",
+    "## *Findings \n",
+    "-The number of total people in the survey was **432** \n",
+    "\n",
+    "-The number of people who smoke was **72** or **17%** of people in survey\n",
+    "\n",
+    "-The number of people who drink was **339** or **78%** of people in survey\n",
+    "\n",
+    "-The number of people who gamble was **216** or **50%** of people in survey\n",
+    "\n",
+    "-The number of people that order their steak rare was **23** or **5%** \n",
+    "\n",
+    "-The number of people who smoke and order steak rare was **6** or **1%** of people in survey\n",
+    "\n",
+    "-The number of people who drink and order steak rare was **19** or **4%** of people in survey\n",
+    "\n",
+    "-The number of people who Gamble and order steak rare was **12** or **3%** of people in survey\n",
+    "\n",
+    "# The most surprising stat was that the Number of people who do all 3 risky behaviors (smoke, drink, gamble) and order steak rare was **5** or **1%*\n",
+    "\n",
+    "\n",
+    "\n",
+    "**What did I learn about my data?**\n",
+    "I learned that you can extract a bunch of different data about a survey. This was kind of a silly survey, but fun to see the results. What I learned was that out of small sample size of 432 people, doing risky behaviors such as smoking, drinking, and gambling, have nothing to do with food choices, specifically risky food choices such as ordering steak rare.\n",
+    "\n",
+    "**Did the results make sense?**\n",
+    "Yes. The results made sense to me in the fact that I came in with a preconceived notion that the two wouldnt be related. What would have been interesting would be if you did a study between people who do risky behaviors who also live shorter lives.\n",
+    "\n",
+    "**What was most surprising?**\n",
+    "# The most surprising stat was that the Number of people who do all 3 risky behaviors (smoke, drink, gamble) and order steak rare was **5** or **1%*\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "infectious-symbol",
+   "metadata": {},
+   "source": [
+    "# Discussion"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bearing-stadium",
+   "metadata": {},
+   "source": [
+    "*It's important to recognize the limitations of our research.\n",
+    "Consider the following:*\n",
+    "\n",
+    "**Do the results give an accurate depiction of your research question? Why or why not?**\n",
+    "Yes becasue it answers the question.\n",
+    "  \n",
+    "**What were limitations of your datset?**\n",
+    "I only had a small sample size.\n",
+    "\n",
+    "**Are there any known biases in the data?**\n",
+    "I dont believe so.\n",
+    "\n",
+    "\n"
    ]
   }
  ],
@@ -310,7 +1228,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.12.3"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/lab_pokemon.ipynb b/lab_pokemon.ipynb
new file mode 100644
index 0000000..cc49dc7
--- /dev/null
+++ b/lab_pokemon.ipynb
@@ -0,0 +1,2307 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "90041b00-672b-4bd4-a8e8-0cab3f0548af",
+   "metadata": {},
+   "source": [
+    "# Lab 04: Data Science Tools\n",
+    "\n",
+    "## 0. Jupyter Notebooks\n",
+    "\n",
+    "Welcome to your first Jupyter notebook! Notebooks are made up of cells. Some cells contain text (like this one) and others contain Python code.\n",
+    "\n",
+    "Each cell can be in two different modes: editing or running. To edit a cell, double-click on it. When you're done editing, press **shift+Enter** to run it.  You can use [Markdown](https://www.markdownguide.org/cheat-sheet/) to add basic formatting to the text. Before you go on, try editing the text in this cell."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "5923b0d7-c0e0-48fa-b765-4aa6002c2d4f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Other cells are code cells, containing Python code. (This is a comment, of course!)\n",
+    "# Try running this cell (again, shift+Enter). You'll see the result of the final statement \n",
+    "# printed below the cell. \n",
+    "# Then try changing the Python code and re-run it.\n",
+    "\n",
+    "1+1+1+2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "257ef44f-8f53-4136-9d0d-23a811ec53e9",
+   "metadata": {},
+   "source": [
+    "### 0.1 Cells share state\n",
+    "\n",
+    "Even though code cells run one at a time, anything that happens in a cell (like declaring a variable or running a function) affects the whole notebook. Try running these two cells a few times, in different orders. What happens when you run *Cell B* over and over?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "0e2a2927-f6d1-4b13-97ae-ff97416723e9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Cell A\n",
+    "x = 9\n",
+    "x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "69dd7908-b213-4d0f-8016-e46a4a491961",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "18"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Cell B\n",
+    "x = x * 2\n",
+    "x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "adc581ac-db13-40a8-bcfc-bf5d6e5472c5",
+   "metadata": {},
+   "source": [
+    "### 0.2 Saving your work\n",
+    "\n",
+    "When you finish working on a notebook, save your work using the icon in the menu bar above. Your notebook is stored in the file `lab_pokemon.ipynb` in the lab directory. You can commit your changes to `ipynb` files just like any other file. Once you finish with Jupyter, you can stop the server by pressing **Control + C** in the Terminal. \n",
+    "\n",
+    "*If you're doing this lab on a cloud-based platform like Binder, then you can't save your work. So don't close the tab!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0269bf0f-b993-4dfe-99cd-a7d38e94546c",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "## 1. Pandas\n",
+    "\n",
+    "Pandas is probably the most important Python library for data science. Pandas provides an object called a **DataFrame**, which is basically a table with rows and columns. Most of the time, you will load data into Pandas using a `.csv` file. CSV files can be exported from Excel or Google Sheets, and are a common format for public data sets. \n",
+    "\n",
+    "In this lab, we'll be working with two data sets: The first contains Pokémon characteristics and the second comes from a wide-scale survey conducted by the US Centers for Disease Control ([details](https://www.cdc.gov/brfss/annual_data/annual_2020.html)). We will demonstrate techniques with Pokémon; your job is to replicate these tasks with the CDC dataset. \n",
+    "\n",
+    "**Note:** Pandas has *extensive* capabilities, and there's no way we could possibly present them all here. If you have a clearly-formed idea of what you want to do with tabular data, there's a way to do it. This lab introduces *some* of what Pandas can do, but expect to spend time reading the documentation and Stack Overflow when you start working on new tasks. \n",
+    "\n",
+    "### 1.0 Getting started\n",
+    "\n",
+    "First, we'll import pandas (using the conventional variable name `pd`) and load the two datasets. *Run these cells and every code cell you encounter in this notebook.*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f60aa4b0-7050-4e43-9619-5f8500770cb0",
+   "metadata": {},
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "pokemon = pd.read_csv(\"pokemon.csv\")\n",
+    "people = pd.read_csv(\"brfss_2020.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d4e0b811-b8bf-4e9a-a934-3aad8f0520bb",
+   "metadata": {},
+   "source": [
+    "### 1.1 A first look\n",
+    "\n",
+    "#### Demo\n",
+    "\n",
+    "Let's start by learning the *shape* of the data. How many columns are there? How many rows? What kinds of data are included?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "579d8dda-ca39-48b1-8819-b17651029729",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "pokemon = pd.read_csv(\"pokemon.csv\")\n",
+    "people = pd.read_csv(\"brfss_2020.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ee8b0718-56f9-4fc8-bd35-fa0ccb445179",
+   "metadata": {},
+   "source": [
+    "OK, 800 Pokémon, with 12 columns for each. And you can see all the columns. Not all the data is shown in this preview, of course. If there were more columns than could be displayed, you could see them all by typing `pokemon.columns`. \n",
+    "\n",
+    "#### Your turn\n",
+    "\n",
+    "Now do the same for your data set, `people`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "c9e5e4ec-b197-450c-ae2d-318006fa0a2f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>income</th>\n",
+       "      <th>education</th>\n",
+       "      <th>sexual_orientation</th>\n",
+       "      <th>height</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>health</th>\n",
+       "      <th>no_doctor</th>\n",
+       "      <th>exercise</th>\n",
+       "      <th>sleep</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>55</td>\n",
+       "      <td>female</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>other</td>\n",
+       "      <td>1.55</td>\n",
+       "      <td>83.01</td>\n",
+       "      <td>2</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>65</td>\n",
+       "      <td>female</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1</td>\n",
+       "      <td>heterosexual</td>\n",
+       "      <td>1.65</td>\n",
+       "      <td>78.02</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>35</td>\n",
+       "      <td>female</td>\n",
+       "      <td>8</td>\n",
+       "      <td>4</td>\n",
+       "      <td>heterosexual</td>\n",
+       "      <td>1.65</td>\n",
+       "      <td>77.11</td>\n",
+       "      <td>4</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>55</td>\n",
+       "      <td>male</td>\n",
+       "      <td>8</td>\n",
+       "      <td>4</td>\n",
+       "      <td>heterosexual</td>\n",
+       "      <td>1.83</td>\n",
+       "      <td>81.65</td>\n",
+       "      <td>5</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>55</td>\n",
+       "      <td>female</td>\n",
+       "      <td>8</td>\n",
+       "      <td>4</td>\n",
+       "      <td>heterosexual</td>\n",
+       "      <td>1.80</td>\n",
+       "      <td>76.66</td>\n",
+       "      <td>4</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>166420</th>\n",
+       "      <td>45</td>\n",
+       "      <td>female</td>\n",
+       "      <td>8</td>\n",
+       "      <td>3</td>\n",
+       "      <td>heterosexual</td>\n",
+       "      <td>1.63</td>\n",
+       "      <td>86.18</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>166421</th>\n",
+       "      <td>25</td>\n",
+       "      <td>male</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "      <td>heterosexual</td>\n",
+       "      <td>1.78</td>\n",
+       "      <td>86.18</td>\n",
+       "      <td>4</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>166422</th>\n",
+       "      <td>25</td>\n",
+       "      <td>female</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>heterosexual</td>\n",
+       "      <td>1.91</td>\n",
+       "      <td>45.36</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>166423</th>\n",
+       "      <td>35</td>\n",
+       "      <td>female</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>heterosexual</td>\n",
+       "      <td>1.60</td>\n",
+       "      <td>68.04</td>\n",
+       "      <td>4</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>166424</th>\n",
+       "      <td>35</td>\n",
+       "      <td>male</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "      <td>heterosexual</td>\n",
+       "      <td>1.75</td>\n",
+       "      <td>86.18</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>166425 rows × 11 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        age     sex  income  education sexual_orientation  height  weight  \\\n",
+       "0        55  female       5          2              other    1.55   83.01   \n",
+       "1        65  female       8          1       heterosexual    1.65   78.02   \n",
+       "2        35  female       8          4       heterosexual    1.65   77.11   \n",
+       "3        55    male       8          4       heterosexual    1.83   81.65   \n",
+       "4        55  female       8          4       heterosexual    1.80   76.66   \n",
+       "...     ...     ...     ...        ...                ...     ...     ...   \n",
+       "166420   45  female       8          3       heterosexual    1.63   86.18   \n",
+       "166421   25    male       7          2       heterosexual    1.78   86.18   \n",
+       "166422   25  female       1          2       heterosexual    1.91   45.36   \n",
+       "166423   35  female       5          4       heterosexual    1.60   68.04   \n",
+       "166424   35    male       7          2       heterosexual    1.75   86.18   \n",
+       "\n",
+       "        health  no_doctor  exercise  sleep  \n",
+       "0            2       True      True      7  \n",
+       "1            3      False     False      8  \n",
+       "2            4       True      True      7  \n",
+       "3            5      False      True      8  \n",
+       "4            4      False      True      8  \n",
+       "...        ...        ...       ...    ...  \n",
+       "166420       1      False     False      6  \n",
+       "166421       4      False      True      6  \n",
+       "166422       1      False     False      8  \n",
+       "166423       4       True      True      6  \n",
+       "166424       3      False     False      8  \n",
+       "\n",
+       "[166425 rows x 11 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "people\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7fab76ef-d453-4568-a916-4d4c29535a42",
+   "metadata": {},
+   "source": [
+    "### 1.2 Descriptive Statistics\n",
+    "\n",
+    "#### Demo\n",
+    "\n",
+    "Let's get a sense of the data contained in some of the columns. For categorical data like `generation`, it makes sense to look at value counts--showing us how many of each category there are. You can use the optional keyword `normalize=True` to see percentage of total instead of frequencies. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "9afca362-9edc-423c-981b-dc42107d5de0",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'people' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[9], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mpeople\u001b[49m\u001b[38;5;241m.\u001b[39mgeneration\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'people' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "people.generation\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a9b98eee-bdc2-4c63-bab2-ee82e2466d0f",
+   "metadata": {},
+   "source": [
+    "For numeric data, we could start by looking at the mean value. We can select multiple columns and get all the column means at once."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "5fe580d0-5939-4152-9f8c-4c32d35a4479",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "hp         69.25875\n",
+       "attack     79.00125\n",
+       "defense    73.84250\n",
+       "speed      68.27750\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pokemon[[\"hp\", \"attack\", \"defense\", \"speed\"]].mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0d8e6e78-fcfc-4c38-a418-545fe4216a44",
+   "metadata": {},
+   "source": [
+    "We can also compute the mean of boolean data. In this case, True will map to 1 and False will map to 0. So the mean value equals the percentage of data which is True. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "dc69ef53-70cd-4ae0-80e7-c9c8e28de76f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(0.08125)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pokemon.legendary.mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "69333e87-8df2-4b46-9005-2b8c9df3a7b4",
+   "metadata": {},
+   "source": [
+    "Just over 8% of Pokemon are legendary."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f563d97d-d9d3-4f2d-a46a-5d5dfc6382de",
+   "metadata": {},
+   "source": [
+    "#### Your turn\n",
+    "\n",
+    "**1.2.0.** In this survey, people are grouped into age bands of 18-24, 25-34, 35-44, 45-54, 55-64, and 65+, with the lower bound reported. What percentage of people are in each age band? (When we talk about \"people\" in this lab, we're referring to the people who responded to the survey, not the whole US population.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "8fbcc766-8399-4f93-a6c8-e0607250a72a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(48.76603274748385)"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "people.age.mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38006e7b-4771-4c29-86a8-19d04a50fc25",
+   "metadata": {},
+   "source": [
+    "**1.2.1.** What are the mean height and weight of people in this survey?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "b7f910c8-3d40-49ae-b270-678734c04100",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "height     1.705082\n",
+       "weight    83.053588\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "people[[\"height\", \"weight\"]].mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f74634bb-8664-46e4-b371-6f45cbb7c8ef",
+   "metadata": {},
+   "source": [
+    "**1.2.2.** The `exercise` column indicates whether a person has done any physical activity or exercise in the last 30 days, outside of work. What percentage of people have done exercise?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "f3891188-a85f-4089-8388-d4d81c7438ad",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'people' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[5], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mpeople\u001b[49m\u001b[38;5;241m.\u001b[39mexercise\u001b[38;5;241m.\u001b[39mmean()\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'people' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "people.exercise.mean()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f6082e65-321c-4ee0-9457-74f9bb1b0363",
+   "metadata": {},
+   "source": [
+    "### 1.3 Filtering\n",
+    "\n",
+    "Sometimes we're just interested in a selection of the data set. The way to do this is to create a boolean series, and then use this to select which rows you want to include. Vocabulary note: A dataframe is two-dimensional, with rows and columns. A series (a single row or a single column) is one-dimensional. \n",
+    "\n",
+    "#### Demo\n",
+    "`pokemon.legendary` is already boolean, so we can use this to select just the legendary pokémon. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "12c0c6c9-c07b-4183-82f6-5e346c74aac9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>type</th>\n",
+       "      <th>subtype</th>\n",
+       "      <th>total</th>\n",
+       "      <th>hp</th>\n",
+       "      <th>attack</th>\n",
+       "      <th>defense</th>\n",
+       "      <th>special_attack</th>\n",
+       "      <th>special_defense</th>\n",
+       "      <th>speed</th>\n",
+       "      <th>generation</th>\n",
+       "      <th>legendary</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>156</th>\n",
+       "      <td>Articuno</td>\n",
+       "      <td>Ice</td>\n",
+       "      <td>Flying</td>\n",
+       "      <td>580</td>\n",
+       "      <td>90</td>\n",
+       "      <td>85</td>\n",
+       "      <td>100</td>\n",
+       "      <td>95</td>\n",
+       "      <td>125</td>\n",
+       "      <td>85</td>\n",
+       "      <td>1</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>157</th>\n",
+       "      <td>Zapdos</td>\n",
+       "      <td>Electric</td>\n",
+       "      <td>Flying</td>\n",
+       "      <td>580</td>\n",
+       "      <td>90</td>\n",
+       "      <td>90</td>\n",
+       "      <td>85</td>\n",
+       "      <td>125</td>\n",
+       "      <td>90</td>\n",
+       "      <td>100</td>\n",
+       "      <td>1</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>158</th>\n",
+       "      <td>Moltres</td>\n",
+       "      <td>Fire</td>\n",
+       "      <td>Flying</td>\n",
+       "      <td>580</td>\n",
+       "      <td>90</td>\n",
+       "      <td>100</td>\n",
+       "      <td>90</td>\n",
+       "      <td>125</td>\n",
+       "      <td>85</td>\n",
+       "      <td>90</td>\n",
+       "      <td>1</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>162</th>\n",
+       "      <td>Mewtwo</td>\n",
+       "      <td>Psychic</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>680</td>\n",
+       "      <td>106</td>\n",
+       "      <td>110</td>\n",
+       "      <td>90</td>\n",
+       "      <td>154</td>\n",
+       "      <td>90</td>\n",
+       "      <td>130</td>\n",
+       "      <td>1</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>163</th>\n",
+       "      <td>MewtwoMega Mewtwo X</td>\n",
+       "      <td>Psychic</td>\n",
+       "      <td>Fighting</td>\n",
+       "      <td>780</td>\n",
+       "      <td>106</td>\n",
+       "      <td>190</td>\n",
+       "      <td>100</td>\n",
+       "      <td>154</td>\n",
+       "      <td>100</td>\n",
+       "      <td>130</td>\n",
+       "      <td>1</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>795</th>\n",
+       "      <td>Diancie</td>\n",
+       "      <td>Rock</td>\n",
+       "      <td>Fairy</td>\n",
+       "      <td>600</td>\n",
+       "      <td>50</td>\n",
+       "      <td>100</td>\n",
+       "      <td>150</td>\n",
+       "      <td>100</td>\n",
+       "      <td>150</td>\n",
+       "      <td>50</td>\n",
+       "      <td>6</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>796</th>\n",
+       "      <td>DiancieMega Diancie</td>\n",
+       "      <td>Rock</td>\n",
+       "      <td>Fairy</td>\n",
+       "      <td>700</td>\n",
+       "      <td>50</td>\n",
+       "      <td>160</td>\n",
+       "      <td>110</td>\n",
+       "      <td>160</td>\n",
+       "      <td>110</td>\n",
+       "      <td>110</td>\n",
+       "      <td>6</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>797</th>\n",
+       "      <td>HoopaHoopa Confined</td>\n",
+       "      <td>Psychic</td>\n",
+       "      <td>Ghost</td>\n",
+       "      <td>600</td>\n",
+       "      <td>80</td>\n",
+       "      <td>110</td>\n",
+       "      <td>60</td>\n",
+       "      <td>150</td>\n",
+       "      <td>130</td>\n",
+       "      <td>70</td>\n",
+       "      <td>6</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>798</th>\n",
+       "      <td>HoopaHoopa Unbound</td>\n",
+       "      <td>Psychic</td>\n",
+       "      <td>Dark</td>\n",
+       "      <td>680</td>\n",
+       "      <td>80</td>\n",
+       "      <td>160</td>\n",
+       "      <td>60</td>\n",
+       "      <td>170</td>\n",
+       "      <td>130</td>\n",
+       "      <td>80</td>\n",
+       "      <td>6</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>799</th>\n",
+       "      <td>Volcanion</td>\n",
+       "      <td>Fire</td>\n",
+       "      <td>Water</td>\n",
+       "      <td>600</td>\n",
+       "      <td>80</td>\n",
+       "      <td>110</td>\n",
+       "      <td>120</td>\n",
+       "      <td>130</td>\n",
+       "      <td>90</td>\n",
+       "      <td>70</td>\n",
+       "      <td>6</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>65 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    name      type   subtype  total   hp  attack  defense  \\\n",
+       "156             Articuno       Ice    Flying    580   90      85      100   \n",
+       "157               Zapdos  Electric    Flying    580   90      90       85   \n",
+       "158              Moltres      Fire    Flying    580   90     100       90   \n",
+       "162               Mewtwo   Psychic       NaN    680  106     110       90   \n",
+       "163  MewtwoMega Mewtwo X   Psychic  Fighting    780  106     190      100   \n",
+       "..                   ...       ...       ...    ...  ...     ...      ...   \n",
+       "795              Diancie      Rock     Fairy    600   50     100      150   \n",
+       "796  DiancieMega Diancie      Rock     Fairy    700   50     160      110   \n",
+       "797  HoopaHoopa Confined   Psychic     Ghost    600   80     110       60   \n",
+       "798   HoopaHoopa Unbound   Psychic      Dark    680   80     160       60   \n",
+       "799            Volcanion      Fire     Water    600   80     110      120   \n",
+       "\n",
+       "     special_attack  special_defense  speed  generation  legendary  \n",
+       "156              95              125     85           1       True  \n",
+       "157             125               90    100           1       True  \n",
+       "158             125               85     90           1       True  \n",
+       "162             154               90    130           1       True  \n",
+       "163             154              100    130           1       True  \n",
+       "..              ...              ...    ...         ...        ...  \n",
+       "795             100              150     50           6       True  \n",
+       "796             160              110    110           6       True  \n",
+       "797             150              130     70           6       True  \n",
+       "798             170              130     80           6       True  \n",
+       "799             130               90     70           6       True  \n",
+       "\n",
+       "[65 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "legendary = pokemon[pokemon.legendary]\n",
+    "legendary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b4ad804a-f5f0-441f-bb83-51f360c1c154",
+   "metadata": {},
+   "source": [
+    "Let's get all the ice pokémon. We can create a boolean series from another series..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ea42d67-4dd0-47c8-b4da-1c29fb4507ea",
+   "metadata": {},
+   "source": [
+    "pokemon.type == \"Ice\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a5ea9e89-f466-48de-9133-346c99f4a6c1",
+   "metadata": {},
+   "source": [
+    "And then use this series to select just the ice pokémon. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c65eb1bd-72b6-487e-adac-ae2071762ca2",
+   "metadata": {
+    "scrolled": true
+   },
+   "source": [
+    "ice = pokemon[pokemon.type == \"Ice\"]\n",
+    "ice"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8161cdbd-76af-4a45-bed9-665ad37cd267",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'ice' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[6], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mice\u001b[49m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'ice' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "ice"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "3ce81665-f1c6-4528-9d0b-56b5da9ccbe3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>type</th>\n",
+       "      <th>subtype</th>\n",
+       "      <th>total</th>\n",
+       "      <th>hp</th>\n",
+       "      <th>attack</th>\n",
+       "      <th>defense</th>\n",
+       "      <th>special_attack</th>\n",
+       "      <th>special_defense</th>\n",
+       "      <th>speed</th>\n",
+       "      <th>generation</th>\n",
+       "      <th>legendary</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>156</th>\n",
+       "      <td>Articuno</td>\n",
+       "      <td>Ice</td>\n",
+       "      <td>Flying</td>\n",
+       "      <td>580</td>\n",
+       "      <td>90</td>\n",
+       "      <td>85</td>\n",
+       "      <td>100</td>\n",
+       "      <td>95</td>\n",
+       "      <td>125</td>\n",
+       "      <td>85</td>\n",
+       "      <td>1</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>157</th>\n",
+       "      <td>Zapdos</td>\n",
+       "      <td>Electric</td>\n",
+       "      <td>Flying</td>\n",
+       "      <td>580</td>\n",
+       "      <td>90</td>\n",
+       "      <td>90</td>\n",
+       "      <td>85</td>\n",
+       "      <td>125</td>\n",
+       "      <td>90</td>\n",
+       "      <td>100</td>\n",
+       "      <td>1</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>158</th>\n",
+       "      <td>Moltres</td>\n",
+       "      <td>Fire</td>\n",
+       "      <td>Flying</td>\n",
+       "      <td>580</td>\n",
+       "      <td>90</td>\n",
+       "      <td>100</td>\n",
+       "      <td>90</td>\n",
+       "      <td>125</td>\n",
+       "      <td>85</td>\n",
+       "      <td>90</td>\n",
+       "      <td>1</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>162</th>\n",
+       "      <td>Mewtwo</td>\n",
+       "      <td>Psychic</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>680</td>\n",
+       "      <td>106</td>\n",
+       "      <td>110</td>\n",
+       "      <td>90</td>\n",
+       "      <td>154</td>\n",
+       "      <td>90</td>\n",
+       "      <td>130</td>\n",
+       "      <td>1</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>163</th>\n",
+       "      <td>MewtwoMega Mewtwo X</td>\n",
+       "      <td>Psychic</td>\n",
+       "      <td>Fighting</td>\n",
+       "      <td>780</td>\n",
+       "      <td>106</td>\n",
+       "      <td>190</td>\n",
+       "      <td>100</td>\n",
+       "      <td>154</td>\n",
+       "      <td>100</td>\n",
+       "      <td>130</td>\n",
+       "      <td>1</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>795</th>\n",
+       "      <td>Diancie</td>\n",
+       "      <td>Rock</td>\n",
+       "      <td>Fairy</td>\n",
+       "      <td>600</td>\n",
+       "      <td>50</td>\n",
+       "      <td>100</td>\n",
+       "      <td>150</td>\n",
+       "      <td>100</td>\n",
+       "      <td>150</td>\n",
+       "      <td>50</td>\n",
+       "      <td>6</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>796</th>\n",
+       "      <td>DiancieMega Diancie</td>\n",
+       "      <td>Rock</td>\n",
+       "      <td>Fairy</td>\n",
+       "      <td>700</td>\n",
+       "      <td>50</td>\n",
+       "      <td>160</td>\n",
+       "      <td>110</td>\n",
+       "      <td>160</td>\n",
+       "      <td>110</td>\n",
+       "      <td>110</td>\n",
+       "      <td>6</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>797</th>\n",
+       "      <td>HoopaHoopa Confined</td>\n",
+       "      <td>Psychic</td>\n",
+       "      <td>Ghost</td>\n",
+       "      <td>600</td>\n",
+       "      <td>80</td>\n",
+       "      <td>110</td>\n",
+       "      <td>60</td>\n",
+       "      <td>150</td>\n",
+       "      <td>130</td>\n",
+       "      <td>70</td>\n",
+       "      <td>6</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>798</th>\n",
+       "      <td>HoopaHoopa Unbound</td>\n",
+       "      <td>Psychic</td>\n",
+       "      <td>Dark</td>\n",
+       "      <td>680</td>\n",
+       "      <td>80</td>\n",
+       "      <td>160</td>\n",
+       "      <td>60</td>\n",
+       "      <td>170</td>\n",
+       "      <td>130</td>\n",
+       "      <td>80</td>\n",
+       "      <td>6</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>799</th>\n",
+       "      <td>Volcanion</td>\n",
+       "      <td>Fire</td>\n",
+       "      <td>Water</td>\n",
+       "      <td>600</td>\n",
+       "      <td>80</td>\n",
+       "      <td>110</td>\n",
+       "      <td>120</td>\n",
+       "      <td>130</td>\n",
+       "      <td>90</td>\n",
+       "      <td>70</td>\n",
+       "      <td>6</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>65 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    name      type   subtype  total   hp  attack  defense  \\\n",
+       "156             Articuno       Ice    Flying    580   90      85      100   \n",
+       "157               Zapdos  Electric    Flying    580   90      90       85   \n",
+       "158              Moltres      Fire    Flying    580   90     100       90   \n",
+       "162               Mewtwo   Psychic       NaN    680  106     110       90   \n",
+       "163  MewtwoMega Mewtwo X   Psychic  Fighting    780  106     190      100   \n",
+       "..                   ...       ...       ...    ...  ...     ...      ...   \n",
+       "795              Diancie      Rock     Fairy    600   50     100      150   \n",
+       "796  DiancieMega Diancie      Rock     Fairy    700   50     160      110   \n",
+       "797  HoopaHoopa Confined   Psychic     Ghost    600   80     110       60   \n",
+       "798   HoopaHoopa Unbound   Psychic      Dark    680   80     160       60   \n",
+       "799            Volcanion      Fire     Water    600   80     110      120   \n",
+       "\n",
+       "     special_attack  special_defense  speed  generation  legendary  \n",
+       "156              95              125     85           1       True  \n",
+       "157             125               90    100           1       True  \n",
+       "158             125               85     90           1       True  \n",
+       "162             154               90    130           1       True  \n",
+       "163             154              100    130           1       True  \n",
+       "..              ...              ...    ...         ...        ...  \n",
+       "795             100              150     50           6       True  \n",
+       "796             160              110    110           6       True  \n",
+       "797             150              130     70           6       True  \n",
+       "798             170              130     80           6       True  \n",
+       "799             130               90     70           6       True  \n",
+       "\n",
+       "[65 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "legendary = pokemon[pokemon.legendary]\n",
+    "legendary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0af5f534-0bec-4577-beee-29b350102265",
+   "metadata": {},
+   "source": [
+    "Let's get the high-speed ice pokémon. You can join conditions together using the `&` (and) and `|` (or) operators. `~` means \"not\", so `pokemon[~(pokemon.type == \"Ice\")]` would select all the non-ice pokémon. Due to order of operations, each condition needs to be wrapped in parentheses."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "bbbeeeef-3490-48f1-aadf-c39c31c6c41b",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'high_speed' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[29], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mhigh_speed\u001b[49m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'high_speed' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "high_speed"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c84dc7ce-24f2-4ac7-92d7-99ed331488e0",
+   "metadata": {},
+   "source": [
+    "You could get the pokémon who are fire or ice by selecting `pokemon[(pokemon.type == \"Fire\") | (pokemon.type == \"Ice\")]`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1f0e9625-b194-450d-b003-b88798cc2f45",
+   "metadata": {},
+   "source": [
+    "#### Your turn\n",
+    "\n",
+    "**1.3.0.** `no_doctor` indicates whether there was a time in the last year when the person needed to see a doctor, but could not afford to do so. Create a dataframe containing only these people. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "198cb0c6-3f43-43c2-9eee-3939c12ea537",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0          True\n",
+       "1         False\n",
+       "2          True\n",
+       "3         False\n",
+       "4         False\n",
+       "          ...  \n",
+       "166420    False\n",
+       "166421    False\n",
+       "166422    False\n",
+       "166423     True\n",
+       "166424    False\n",
+       "Name: no_doctor, Length: 166425, dtype: bool"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "people.no_doctor"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9d213707-a15b-4751-8df9-48aa568af209",
+   "metadata": {},
+   "source": [
+    "**1.3.1.** `health` asks people for their general health, with the meanings of numbers shown below. Create a dataframe which contains people whose general health is good or better. \n",
+    "\n",
+    "| number | health status | \n",
+    "| ------ | ----------- |\n",
+    "| 1      | Poor        |\n",
+    "| 2      | Fair        |\n",
+    "| 3      | Good        |\n",
+    "| 4      | Very good   |\n",
+    "| 5      | Excellent   |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "8a8c1ad6-4c1e-4996-ab5e-5212dadb1851",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0         2\n",
+       "1         3\n",
+       "2         4\n",
+       "3         5\n",
+       "4         4\n",
+       "         ..\n",
+       "166420    1\n",
+       "166421    4\n",
+       "166422    1\n",
+       "166423    4\n",
+       "166424    3\n",
+       "Name: health, Length: 166425, dtype: int64"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "people.health\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "a50cfa93-8ff7-46ee-b4c4-5c07255ca870",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'DataFrame' object has no attribute 'health_status'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m/tmp/ipykernel_1131/1799491424.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhealth_status\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpeople\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpeople\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhealth_status\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/.cache/pypoetry/virtualenvs/lab-pokemon-MIddldub-py3.12/lib/python3.12/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   6295\u001b[0m             \u001b[0;32mand\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_accessors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6296\u001b[0m             \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6297\u001b[0m         \u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6298\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6299\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'health_status'"
+     ]
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "ef61ce67-b36f-4e80-9d57-8b80a7d9eb2b",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'DataFrame' object has no attribute 'good'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m/tmp/ipykernel_1131/4081568713.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mpeople\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhealth\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"good\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mpeople\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgood\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/.cache/pypoetry/virtualenvs/lab-pokemon-MIddldub-py3.12/lib/python3.12/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   6295\u001b[0m             \u001b[0;32mand\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_accessors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6296\u001b[0m             \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6297\u001b[0m         \u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6298\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6299\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'good'"
+     ]
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7add542b-bfd2-481a-b5b4-4e1ca744078a",
+   "metadata": {},
+   "source": [
+    "**1.3.2.**. `education` indicates the highest level of education completed, with codes as follows. Create a dataframe which only contains female college graduates who needed a doctor but couldn't afford one. (The survey asked people for their current sex, and only had options for male and female.)\n",
+    "\n",
+    "| number | education level      | \n",
+    "| ------ | ----------- |\n",
+    "| 1      | Did not graduate from high school        |\n",
+    "| 2      | Graduated from high school        |\n",
+    "| 3      | Attended some college        |\n",
+    "| 4      | Graduated from college   |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "315682ae-7d54-4d78-9a63-d23c83ba1576",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(3.0645515998197386)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "people.education.mean()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f4b023b1-b754-4b6a-a7e6-b840ae180501",
+   "metadata": {},
+   "source": [
+    "### 1.4. Grouping\n",
+    "\n",
+    "Now things get crazy. You can group a dataframe using one or more columns, and then compare their statistics. \n",
+    "\n",
+    "#### Demo\n",
+    "\n",
+    "Do different types of pokémon move at different speeds? We'll use `sort_values` to put these in order from slow to fast."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "069ea0ab-eff6-4985-9f46-db956fe1df91",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "type\n",
+       "Fairy        48.588235\n",
+       "Steel        55.259259\n",
+       "Rock         55.909091\n",
+       "Bug          61.681159\n",
+       "Grass        61.928571\n",
+       "Ice          63.458333\n",
+       "Poison       63.571429\n",
+       "Ground       63.906250\n",
+       "Ghost        64.343750\n",
+       "Water        65.964286\n",
+       "Fighting     66.074074\n",
+       "Normal       71.551020\n",
+       "Fire         74.442308\n",
+       "Dark         76.161290\n",
+       "Psychic      81.491228\n",
+       "Dragon       83.031250\n",
+       "Electric     84.500000\n",
+       "Flying      102.500000\n",
+       "Name: speed, dtype: float64"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pokemon.groupby(\"type\").speed.mean().sort_values()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bdc801b7-d3ae-45bb-80f4-ebeb474e20a1",
+   "metadata": {},
+   "source": [
+    "Do types differ in other stats? Let's sort by hit points. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "5c420c0e-b5d2-49ae-ab98-3305ee076169",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>hp</th>\n",
+       "      <th>attack</th>\n",
+       "      <th>defense</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>type</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Bug</th>\n",
+       "      <td>56.884058</td>\n",
+       "      <td>70.971014</td>\n",
+       "      <td>70.724638</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Electric</th>\n",
+       "      <td>59.795455</td>\n",
+       "      <td>69.090909</td>\n",
+       "      <td>66.295455</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Ghost</th>\n",
+       "      <td>64.437500</td>\n",
+       "      <td>73.781250</td>\n",
+       "      <td>81.187500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Steel</th>\n",
+       "      <td>65.222222</td>\n",
+       "      <td>92.703704</td>\n",
+       "      <td>126.370370</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Rock</th>\n",
+       "      <td>65.363636</td>\n",
+       "      <td>92.863636</td>\n",
+       "      <td>100.795455</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Dark</th>\n",
+       "      <td>66.806452</td>\n",
+       "      <td>88.387097</td>\n",
+       "      <td>70.225806</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Poison</th>\n",
+       "      <td>67.250000</td>\n",
+       "      <td>74.678571</td>\n",
+       "      <td>68.821429</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Grass</th>\n",
+       "      <td>67.271429</td>\n",
+       "      <td>73.214286</td>\n",
+       "      <td>70.800000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fighting</th>\n",
+       "      <td>69.851852</td>\n",
+       "      <td>96.777778</td>\n",
+       "      <td>65.925926</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fire</th>\n",
+       "      <td>69.903846</td>\n",
+       "      <td>84.769231</td>\n",
+       "      <td>67.769231</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Psychic</th>\n",
+       "      <td>70.631579</td>\n",
+       "      <td>71.456140</td>\n",
+       "      <td>67.684211</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Flying</th>\n",
+       "      <td>70.750000</td>\n",
+       "      <td>78.750000</td>\n",
+       "      <td>66.250000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Ice</th>\n",
+       "      <td>72.000000</td>\n",
+       "      <td>72.750000</td>\n",
+       "      <td>71.416667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Water</th>\n",
+       "      <td>72.062500</td>\n",
+       "      <td>74.151786</td>\n",
+       "      <td>72.946429</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Ground</th>\n",
+       "      <td>73.781250</td>\n",
+       "      <td>95.750000</td>\n",
+       "      <td>84.843750</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fairy</th>\n",
+       "      <td>74.117647</td>\n",
+       "      <td>61.529412</td>\n",
+       "      <td>65.705882</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Normal</th>\n",
+       "      <td>77.275510</td>\n",
+       "      <td>73.469388</td>\n",
+       "      <td>59.846939</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Dragon</th>\n",
+       "      <td>83.312500</td>\n",
+       "      <td>112.125000</td>\n",
+       "      <td>86.375000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 hp      attack     defense\n",
+       "type                                       \n",
+       "Bug       56.884058   70.971014   70.724638\n",
+       "Electric  59.795455   69.090909   66.295455\n",
+       "Ghost     64.437500   73.781250   81.187500\n",
+       "Steel     65.222222   92.703704  126.370370\n",
+       "Rock      65.363636   92.863636  100.795455\n",
+       "Dark      66.806452   88.387097   70.225806\n",
+       "Poison    67.250000   74.678571   68.821429\n",
+       "Grass     67.271429   73.214286   70.800000\n",
+       "Fighting  69.851852   96.777778   65.925926\n",
+       "Fire      69.903846   84.769231   67.769231\n",
+       "Psychic   70.631579   71.456140   67.684211\n",
+       "Flying    70.750000   78.750000   66.250000\n",
+       "Ice       72.000000   72.750000   71.416667\n",
+       "Water     72.062500   74.151786   72.946429\n",
+       "Ground    73.781250   95.750000   84.843750\n",
+       "Fairy     74.117647   61.529412   65.705882\n",
+       "Normal    77.275510   73.469388   59.846939\n",
+       "Dragon    83.312500  112.125000   86.375000"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ptypes = pokemon.groupby(\"type\")\n",
+    "ptypes[[\"hp\", \"attack\", \"defense\"]].mean().sort_values(\"hp\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cc9a3d19-0ecd-487b-b34f-b748c44fc9c9",
+   "metadata": {},
+   "source": [
+    "Which type/subtype combinations are most likely to have legendary pokémon?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "444a580d-e70c-48a1-bf87-77f98b8c9f85",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "type      subtype \n",
+       "Electric  Flying      0.600000\n",
+       "Rock      Fairy       0.666667\n",
+       "Ghost     Dragon      1.000000\n",
+       "Ground    Fire        1.000000\n",
+       "Fire      Water       1.000000\n",
+       "          Steel       1.000000\n",
+       "Steel     Dragon      1.000000\n",
+       "Dragon    Electric    1.000000\n",
+       "Psychic   Ghost       1.000000\n",
+       "Dragon    Psychic     1.000000\n",
+       "          Ice         1.000000\n",
+       "Rock      Fighting    1.000000\n",
+       "Steel     Fighting    1.000000\n",
+       "Dragon    Fire        1.000000\n",
+       "Psychic   Dark        1.000000\n",
+       "          Fire        1.000000\n",
+       "Name: legendary, dtype: float64"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "legendary_percentages = pokemon.groupby([\"type\", \"subtype\"]).legendary.mean().sort_values() \n",
+    "legendary_percentages[legendary_percentages > 0.5]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "de23775b-8670-4371-913d-d8fa1d1f3a76",
+   "metadata": {},
+   "source": [
+    "#### Your turn\n",
+    "\n",
+    "**1.4.0.** `income` records peoples' annual income, in the following bands. `sleep` records the average hours of sleep someone gets per night. Is there a difference in the average hours of sleep by income level?\n",
+    "\n",
+    "| number | annual income, in $1000   | \n",
+    "| ------ | ----------- |\n",
+    "| 1      | Less than 10       |\n",
+    "| 2      | 10-15        |\n",
+    "| 3      | 15-20         |\n",
+    "| 4      | 20-25        |\n",
+    "| 5      | 25-35        |\n",
+    "| 6      | 35-50        |\n",
+    "| 7      | 50-75        |\n",
+    "| 8      | More than 75   |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "75c1ac4f-3914-4c0a-a156-2e084002df66",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'people' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[2], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mpeople\u001b[49m\u001b[38;5;241m.\u001b[39msleep\u001b[38;5;241m.\u001b[39mmean()\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'people' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "people.sleep.mean()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f6413f2b-26a0-4b70-976f-90e45558c4bb",
+   "metadata": {},
+   "source": [
+    "**1.4.0.** Is there a difference in peoples' income or general health, by sex and education level? "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "d46df8a1-bbc2-45a4-9be1-cee1858cbf21",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# YOUR CODE HERE"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "931d602b-ddf4-4c8b-80e0-f886267cce76",
+   "metadata": {},
+   "source": [
+    "### 1.5. Plotting \n",
+    "\n",
+    "Pandas has excellent built-in plotting capabilities, but \n",
+    "we are going to use the [seaborn](https://seaborn.pydata.org/) library because it's a bit \n",
+    "more intuitive and produces more beautiful plots. `set_theme`, called here without any arguments, assigns the default color palette. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "b1e06e4f-6b9e-42af-a27c-dbb525a259ce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import seaborn as sns\n",
+    "sns.set_theme()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a15ad672-13e8-4bdd-bc31-a489a1730daf",
+   "metadata": {},
+   "source": [
+    "#### Demo\n",
+    "\n",
+    "**When you want to visualize the distribution of a series**, a [histogram](https://seaborn.pydata.org/generated/seaborn.histplot.html) puts data into bins and plots the number of data points in each bin.\n",
+    "\n",
+    "Let's see the distribution of pokémon attack values. Note how assigning `x=\"attack\"` spreads attack values over the x-axis, while `y=\"attack\"` spreads attack values over the y-axis. The number of bins is selected automatically, but you can specify this with the optional `bins` argument. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "5ce066fe-f81d-4b78-a394-c5c2f4dc9f46",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'sns' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43msns\u001b[49m\u001b[38;5;241m.\u001b[39mhistplot(data\u001b[38;5;241m=\u001b[39mpokemon, x\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlegendary\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'sns' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "sns.histplot(data=pokemon, x=\"legendary\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "bceb253b-ef4f-4aa2-aef4-cab2b3ca6d59",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'sns' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[2], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43msns\u001b[49m\u001b[38;5;241m.\u001b[39mhistplot(data\u001b[38;5;241m=\u001b[39mpokemon, y\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mattack\u001b[39m\u001b[38;5;124m\"\u001b[39m, bins\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m)\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'sns' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "sns.histplot(data=pokemon, y=\"attack\", bins=5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2aac9186-86c0-41db-a1c4-8719bb78b46b",
+   "metadata": {},
+   "source": [
+    "**When you want to compare the distribution of a numeric variable across categories**, a [barplot](https://seaborn.pydata.org/generated/seaborn.barplot.html) is a good choice. Choose one numeric column and one categorical column. \n",
+    "\n",
+    "Let's see pokémon hit points by legendary/non-legendary. `errorbar=\"sd\"` shows the standard deviation for each category. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "92be1ad0-12bb-49f0-a3f6-85fcfd98e943",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='legendary', ylabel='hp'>"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.barplot(data=pokemon, x=\"legendary\", y=\"hp\", errorbar=\"sd\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4f75e1fa-a5d7-4d2c-a458-8190a7cd700e",
+   "metadata": {},
+   "source": [
+    "Here, we use a barplot to show average hit points by type. `errorbar=None` removes the standard deviation bars, because they clutter up the plot with too much detail. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "17f1c289-5990-4420-bfcb-e50eee0b8af6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='hp', ylabel='type'>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.barplot(data=pokemon, x=\"hp\", y=\"type\", hue=\"type\", errorbar=None, palette=\"muted\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "213d6139-203f-4d81-a4b1-6f98cb184662",
+   "metadata": {},
+   "source": [
+    "**When you want to show how many observations are the intersection of multiple categories,** a [countplot](https://seaborn.pydata.org/generated/seaborn.countplot.html) is a good choice. \n",
+    "\n",
+    "To demonstrate this, let's convert the numeric variable `speed` into a categorical variable, `speed_category`, using the built-in function [cut](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.cut.html). "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "3c8e9f47-9aea-4bf0-a628-7aa1a66a8eee",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'pd' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[8], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m bins \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m50\u001b[39m, \u001b[38;5;241m100\u001b[39m, \u001b[38;5;241m200\u001b[39m]\n\u001b[1;32m      2\u001b[0m labels \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mslow\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmedium\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfast\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m----> 3\u001b[0m pokemon[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mspeed_category\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241m.\u001b[39mcut(pokemon\u001b[38;5;241m.\u001b[39mspeed, bins\u001b[38;5;241m=\u001b[39mbins, labels\u001b[38;5;241m=\u001b[39mlabels)\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'pd' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "bins = [0, 50, 100, 200]\n",
+    "labels = [\"slow\", \"medium\", \"fast\"]\n",
+    "pokemon[\"speed_category\"] = pd.cut(pokemon.speed, bins=bins, labels=labels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "22f78bec-3d18-4133-ba9f-6595d7181ded",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='speed_category', ylabel='count'>"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(data=pokemon, x=\"speed_category\", hue=\"legendary\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fd508c13-9900-4be1-958f-4f9e9e9b633a",
+   "metadata": {},
+   "source": [
+    "**When you want to show the relationship between two numeric variables**, a [scatterplot](https://seaborn.pydata.org/generated/seaborn.scatterplot.html) is a good choice. \n",
+    "\n",
+    "Here, we plot pokémon hit points against their speed. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "444d9832-bd57-4238-9ea4-5ee898847170",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='hp', ylabel='speed'>"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.scatterplot(data=pokemon, x=\"hp\", y=\"speed\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03e81709-393d-4c41-bce5-2dffc9cf5553",
+   "metadata": {},
+   "source": [
+    "You can distinguish between categories within a scatter plot by assigning a categorical variable to `hue`. We set the marker size with `s` and their opacity with `alpha`. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "86f9747b-00a3-407f-9b73-0bce40bac50d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='hp', ylabel='speed'>"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.scatterplot(data=pokemon, x=\"hp\", y=\"speed\", hue=\"legendary\", alpha=0.5, s=60)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f3741251-2a2b-437e-b68f-084fb4399e9f",
+   "metadata": {},
+   "source": [
+    "Finally, if you want scatter plots across multiple categories, a [relplot](https://seaborn.pydata.org/generated/seaborn.relplot.html) lets you distribute categories across rows and colums in a grid. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "7385237c-6a5c-4041-af46-559d6d84d1fa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x131a01e80>"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1592.5x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "favorite_types = pokemon[pokemon.type.isin([\"Fire\", \"Water\", \"Grass\"])]\n",
+    "sns.relplot(data=favorite_types, x=\"hp\", y=\"speed\", hue=\"legendary\", col=\"type\", s=100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c6a20904-416d-44be-a4f3-2107200fb3c2",
+   "metadata": {},
+   "source": [
+    "#### Your turn\n",
+    "\n",
+    "**1.5.0.** Plot a histogram of peoples' heights."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "3b268a30-42ff-4ab8-b2cd-c58a76121f9c",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'histplot' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[35], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mhistplot\u001b[49m(data\u001b[38;5;241m=\u001b[39mpeople, x\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mheight\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'histplot' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "histplot(data=people, x=\"height\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "f145ae04-2796-4420-8d98-ce74d5bd4c83",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='attack', ylabel='Count'>"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.histplot(data=pokemon, x=\"attack\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9b0c9120-fff4-42b2-8ab6-3aa2eba47806",
+   "metadata": {},
+   "source": [
+    "**1.5.1.** Plot a bar chart showing peoples' average hours of sleep by age. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "ee30c851-14b1-4901-9182-4304d54d53a6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your code here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "15d94323-2d65-4100-9916-101516f6ccf1",
+   "metadata": {},
+   "source": [
+    "**1.5.2.** Plot a bar chart showing peoples' likelihood of getting exercise by income. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "13eeecd8-2518-4ed9-aac5-727a96b5bf80",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your code here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b2705fef-470d-494c-86c1-8b3bd34b3660",
+   "metadata": {},
+   "source": [
+    "**1.5.3.** Plot a bar chart showing average reported health by age. For each age, show average health for those who get exercise and those who don't."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "4ee2eb69-2f9a-42e7-b5d3-9499631bfd06",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your code here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "84b1e240-4f75-4c86-8c1f-1026aa223717",
+   "metadata": {},
+   "source": [
+    "**1.5.4.** Create a plot showing the number of people at each income level, for each education level. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "d7e02da8-beab-40e7-95d0-74a5c2bc838e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your code here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ac717580-4157-402c-9262-b2b50dfe606f",
+   "metadata": {},
+   "source": [
+    "**1.5.5.** Plot side-by-side scatter plots showing the relationship between height and weight for males and females. (There are so many overlapping dots that the plot will be more informative if you lower the opacity of each dot. Try using `alpha=0.1` and `edgecolor=None`.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "b00dd7d6-226b-469c-86d8-b71b328aa576",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your code here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e9ff7225-5d08-428b-90e8-ee60f4a4049a",
+   "metadata": {},
+   "source": [
+    "## 2. Crafting a data argument\n",
+    "\n",
+    "Everything up to here are just tools, worthless without a clear research question and a convincing argument. Choose a research question that interests you which might be answerable using the `people` dataset. Then do your best to find the answer in the space below. This answer should include data analysis (code cells) as well as written argument (text cells) explaining what the data means and why you believe it answers your question. \n",
+    "\n",
+    "Examples of research questions might include:\n",
+    "\n",
+    "- Do older people tend to have higher incomes?\n",
+    "- Do people who sleep at least 6 hours a night tend to report better health? \n",
+    "- Is it more common for males to be bisexual than females?\n",
+    "\n",
+    "**A note of caution:** this lab has given you tools for exploring associations--patterns that tend to co-occur. These tools *do not* equip you to argue that one variable causes another to change. For example: Plot 1.5.4 showed that people who are taller also tend to be heaver, with a lot of individual variation. But are people heavier *because* they are taller? Are they taller because they are heavier? Or maybe neither variable causes the other--perhaps they're both caused by something else. If you want to be able to answer questions like these, take a course on statistics."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "6f934273-b829-4bc2-a7f4-a27a3fc44a99",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your code here. Feel free to add new text cells and code cells as necessary."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8b4b852b-402c-45d4-b3bb-840e47b249ed",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/proposal.md b/proposal.md
index 5a8632d..10ee5c6 100644
--- a/proposal.md
+++ b/proposal.md
@@ -4,62 +4,74 @@ This planning document will also form the introduction of your
 argument.
 
 ## Overarching Question
+DO PEOPLE WHO EXIBIT RISKY BEHAVIORS (SMOKING, GAMBLING, DRINKING) ORDER THEIR STEAK IN THE SAME RISKY MANNER AS THEIR BEAHVIORS (RARE). IS THERE A RELATIONSHIP BETWEEN THE TWO.
 
 ### What central question are you interested in exploring? Why are you interested in exploring this question?
 
-**A sleep study using males and females using bedtimes/wakeup times and hours of sleep for the weekday, for kids ages 11-14, to see how much sleep students are getting during the week and if there is a difference between ages and males and females. Also does their attendance percentage in school correlate with more sleep.
-I am interested in this becasue I teach 11-14 year olds
+THE CENTRAL QUESTION WOULD ASK DO PEOPLE WHO HAVE AN AFFINITY FOR RISKY BEHAVIORS, SPECIFICALLY (SMOKING, DRINKING, GAMBLING) ALSO HAVE A RISKY BEHAVIOR WHEN IT COMES TO EATING THEIR FOOD. SPECIFICALLY IN THIS STUDY, IT IS ABOUT ORDERING STEAK.
+I AM INTERESTED IN THIS QUESTION BECASUE IT IS INTERESTING TO SEE IF FOOD RISK BEHAVIORS ARE ASSOCIATED WITH ACTIVITY RISK BEHAVIORS. I ALSO AM INTERESTED TO SEE IN THIS STUDY HOW INCOME LEVELS EFFECT THESE DECISIONS AS WELL AS EDUCATION LEVELS, WHICH IS ALSO IN THE STUDY.
 
 
-*This should be the big picture question that you ask; use at least 5
-sentences to describe why you are interested in it.*
-
-Does a high attendance percentage >95%, have a relationship with high sleep hours per night (>7hrs. per night) as well as age and gender. Does this relationship differ between males and females and ages. Overall does age, gender, and sleep hours have a relationship with high attendance, >95%?
 
 ### What specific research questions will you investigate?
 
 *List 2-4 specific research questions. Each should be answerable 
 using your data set.*
 
-Average sleep for females
-Average sleep for males
-Average wakeup time for females
-Average wakeup time for males
-Average attendance % for males
-Average attendance % for females
-Average sleep for 11,12,13,and 14 year olds
-Average sleep for 11.12.13.14 year old males/females
+1. DO MORE MALES THAN FEMALES ORDER RARE STEAK?
+2. DO PEOPLE WITH A BACHELORS DEGREE GAMBLE MORE THAN PEOPLE WITH A GRADUATE DEGREE?
+3. DO PEOPLE WHO ORDER RARE STEAK ALSO DRINK?
+4. DO ALL FEMALES WHO ORDER RARE STEAK ALSO SMOKE?
+5. DO ALL MALES WHO ORDER RARE STEAK ALSO DRINK
+6. Are males more likely to order their steak rare than females
+
+ 
+   
 
 
 ## Data source
-
+538 WEBSITE
 ### What data set will you use to answer your overarching question? 
 
 *Give the title of your data set and provide a link to your data.*
 
-Sleep, age and gender as it relates to attendance
-
-https://docs.google.com/spreadsheets/d/1hB269WnLnm71C6WXjyBM9blWSF8X1Zf-nNqvftU6E94/edit?usp=sharing
-
+TITLE OF STUDY IS: STEAK-RISK-SURVEY
 
+WEBSITES: 
+https://github.com/fivethirtyeight/data/tree/master/steak-survey
+https://fivethirtyeight.com/features/how-americans-like-their-steak/
+https://docs.google.com/spreadsheets/d/125buas5LwUrDXEgQh0DPniGkA6-fWeupBgaXgUqXoOI/edit?gid=1253281910#gid=1253281910
 
 ### Where is this data from?
 
 *Describe the source of the data set--not just where you downloaded it, but
 the person or organization who gathered the data. Explain why you trust them.*
 
-Changing school start times: impact on sleep in primary and secondary school students Open Access
-Lisa J Meltzer, Kyla L Wahlstrom, Amy E Plog, Matthew J Strand
+May 16, 2014, at 5:08 PM
+
+How Americans Like Their Steak
+By Walt Hickey
+Filed under FiveThirtyAte
+DATA LAB
+https://github.com/fivethirtyeight/data/tree/master/steak-survey
+https://fivethirtyeight.com/features/how-americans-like-their-steak/
+
 
 ### What is this data about?
 
 *Describe the nature of the data in the dataset, including the number of rows 
 and some of the columns which will be important to you.*
 
-number of rows=50
-gender
-attendance_percent
-hrs_sleep
+number of rows= 433
+COLUMNS= 8
+COLUMNS WHIC HARE IMPORTANT ARE:
+1.gender
+2.how do you like steak
+3.income level
+4. degree
+5. gamble
+6. drink
+7. smoke
 
 ## Methods 
 
@@ -67,3 +79,10 @@ hrs_sleep
 
 *For each research question, explain what you will do with the data set 
 to answer the question, and how you will present your answer (e.g. a chart or a table).*
+
+1. DO MORE MALES THAN FEMALES ORDER RARE STEAK? table
+2. DO PEOPLE WITH A BACHELORS DEGREE GAMBLE MORE THAN PEOPLE WITH A GRADUATE DEGREE? chart
+3. DO PEOPLE WHO ORDER RARE STEAK ALSO DRINK?
+4. DO ALL FEMALES WHO ORDER RARE STEAK ALSO SMOKE?
+5. DO ALL MALES WHO ORDER RARE STEAK ALSO DRINK
+6. Are males more likely to order their steak rare than females
\ No newline at end of file
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zH}P2Ve9}y`b2;Rp(Q*)$G12#6o9qz2a$%RJc#X>-O;=<ng1}}55ERr0Xz8#lrByDS
zJ6CKJAHT7Y!YY}BcN0DlSOL~Psm3fy&=kD5QLU2K+a!wnF8qRo6*t4@6A?bJ1h_-*
GC;t}>uhKdI

literal 0
HcmV?d00001

diff --git a/steak.csv b/steak.csv
new file mode 100644
index 0000000..6227563
--- /dev/null
+++ b/steak.csv
@@ -0,0 +1,433 @@
+smoke,drink,gamble,cooked,Gender,Age,Household Income,Education
+Yes,Yes,Yes,Medium,Male,> 60,"$50,000 - $99,999",Bachelor degree
+No,Yes,No,Medium,Male,> 60,"$50,000 - $99,999",Graduate degree
+Yes,Yes,Yes,Medium,Male,18-29,"$50,000 - $99,999",Bachelor degree
+No,Yes,Yes,Medium,Male,18-29,"$50,000 - $99,999",Bachelor degree
+No,Yes,Yes,Medium,Male,18-29,"$50,000 - $99,999",Bachelor degree
+No,Yes,No,Medium,Male,45-60,"$25,000 - $49,999",Bachelor degree
+No,Yes,No,Medium,Male,> 60,"$25,000 - $49,999",Graduate degree
+No,No,No,Medium,Male,18-29,"$0 - $24,999",Graduate degree
+Yes,Yes,No,Medium,Male,45-60,"$50,000 - $99,999",Graduate degree
+No,Yes,Yes,Medium,Male,30-44,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,No,Medium,Male,18-29,,Some college or Associate degree
+No,No,No,Medium,Male,30-44,"$50,000 - $99,999",Bachelor degree
+Yes,Yes,Yes,Medium,Male,30-44,,High school degree
+No,Yes,Yes,Medium,Male,30-44,"$100,000 - $149,999",Bachelor degree
+No,Yes,Yes,Medium,Male,45-60,"$50,000 - $99,999",Bachelor degree
+No,Yes,Yes,Medium,Male,30-44,"$50,000 - $99,999",Bachelor degree
+No,Yes,Yes,Medium,Male,> 60,"$100,000 - $149,999",Bachelor degree
+Yes,Yes,Yes,Medium,Male,30-44,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,Yes,Medium,Male,45-60,"$150,000+",Bachelor degree
+No,Yes,Yes,Medium,Male,> 60,"$50,000 - $99,999",Bachelor degree
+No,Yes,Yes,Medium,Male,> 60,"$100,000 - $149,999",Some college or Associate degree
+No,Yes,No,Medium,Male,> 60,,Graduate degree
+No,Yes,No,Medium,Male,18-29,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,Yes,Medium,Male,> 60,"$50,000 - $99,999",Bachelor degree
+No,Yes,No,Medium,Male,45-60,,Bachelor degree
+No,Yes,Yes,Medium,Male,45-60,,Some college or Associate degree
+No,Yes,No,Medium,Male,18-29,"$25,000 - $49,999",Graduate degree
+No,No,No,Medium,Male,18-29,"$100,000 - $149,999",Some college or Associate degree
+No,Yes,No,Medium,Female,> 60,"$50,000 - $99,999",Graduate degree
+No,Yes,Yes,Medium,Male,> 60,"$25,000 - $49,999",Some college or Associate degree
+No,Yes,Yes,Medium,Female,> 60,"$25,000 - $49,999",High school degree
+No,No,No,Medium,Male,30-44,"$25,000 - $49,999",Some college or Associate degree
+No,No,Yes,Medium,Male,30-44,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,Yes,Medium,Male,30-44,"$100,000 - $149,999",Bachelor degree
+No,Yes,No,Medium,,,,
+No,Yes,No,Medium,Male,30-44,"$100,000 - $149,999",Bachelor degree
+No,Yes,Yes,Medium,Male,30-44,"$50,000 - $99,999",Some college or Associate degree
+Yes,Yes,Yes,Medium,Male,30-44,"$150,000+",Graduate degree
+Yes,Yes,Yes,Medium,Male,18-29,"$0 - $24,999",High school degree
+Yes,Yes,Yes,Medium,Female,18-29,"$25,000 - $49,999",High school degree
+Yes,Yes,Yes,Medium,Male,30-44,"$25,000 - $49,999",Some college or Associate degree
+No,No,Yes,Medium,Male,45-60,"$150,000+",Some college or Associate degree
+No,No,No,Medium,Male,18-29,"$0 - $24,999",Bachelor degree
+No,Yes,Yes,Medium,Male,30-44,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,Yes,Medium,Female,> 60,"$0 - $24,999",Some college or Associate degree
+No,Yes,Yes,Medium,Male,45-60,"$150,000+",Graduate degree
+No,Yes,Yes,Medium,Female,> 60,,Graduate degree
+No,No,No,Medium,Female,> 60,"$25,000 - $49,999",Some college or Associate degree
+No,No,Yes,Medium,Male,> 60,"$0 - $24,999",Bachelor degree
+No,Yes,No,Medium,Male,30-44,"$100,000 - $149,999",Bachelor degree
+No,Yes,No,Medium,Female,18-29,"$50,000 - $99,999",Graduate degree
+No,Yes,Yes,Medium,Male,30-44,"$100,000 - $149,999",Bachelor degree
+No,No,No,Medium,Female,45-60,,Graduate degree
+No,Yes,No,Medium,Male,18-29,"$50,000 - $99,999",Bachelor degree
+No,Yes,No,Medium,Female,45-60,"$150,000+",Bachelor degree
+No,Yes,No,Medium,Male,> 60,"$50,000 - $99,999",Graduate degree
+No,No,No,Medium,Female,45-60,"$25,000 - $49,999",Some college or Associate degree
+No,Yes,Yes,Medium,Male,45-60,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,Yes,Medium,Female,30-44,"$150,000+",Bachelor degree
+No,Yes,No,Medium,Female,30-44,"$50,000 - $99,999",Bachelor degree
+No,No,No,Medium,Male,18-29,,High school degree
+No,Yes,No,Medium,Male,> 60,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,No,Medium,Female,> 60,"$150,000+",Bachelor degree
+No,Yes,Yes,Medium,Male,30-44,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,Yes,Medium,Female,30-44,"$100,000 - $149,999",Graduate degree
+No,Yes,No,Medium,Female,> 60,"$25,000 - $49,999",High school degree
+Yes,Yes,Yes,Medium,Male,18-29,"$100,000 - $149,999",Bachelor degree
+No,Yes,No,Medium,Female,45-60,"$25,000 - $49,999",Some college or Associate degree
+No,Yes,No,Medium,Female,18-29,"$50,000 - $99,999",Bachelor degree
+Yes,Yes,Yes,Medium,Male,18-29,"$0 - $24,999",Some college or Associate degree
+No,No,No,Medium,Male,18-29,"$50,000 - $99,999",Bachelor degree
+No,Yes,No,Medium,Male,18-29,"$25,000 - $49,999",Bachelor degree
+No,Yes,No,Medium,Female,30-44,"$150,000+",Bachelor degree
+No,Yes,Yes,Medium,Female,18-29,"$100,000 - $149,999",Bachelor degree
+No,Yes,No,Medium,Female,18-29,"$150,000+",Some college or Associate degree
+No,Yes,Yes,Medium,Female,30-44,"$150,000+",Graduate degree
+No,Yes,Yes,Medium,Male,45-60,"$150,000+",Graduate degree
+No,Yes,No,Medium,,,,
+No,Yes,No,Medium,,,,
+No,Yes,No,Medium,Female,45-60,"$50,000 - $99,999",Some college or Associate degree
+Yes,Yes,Yes,Medium,Male,30-44,"$50,000 - $99,999",Bachelor degree
+No,Yes,No,Medium,Male,30-44,"$50,000 - $99,999",Graduate degree
+No,Yes,No,Medium,Female,18-29,"$100,000 - $149,999",Some college or Associate degree
+No,No,Yes,Medium,Male,45-60,,Some college or Associate degree
+No,Yes,Yes,Medium,Male,45-60,"$0 - $24,999",High school degree
+Yes,Yes,Yes,Medium,Female,> 60,"$25,000 - $49,999",Graduate degree
+No,Yes,Yes,Medium,Female,45-60,"$150,000+",Graduate degree
+No,Yes,No,Medium,Female,18-29,"$0 - $24,999",Some college or Associate degree
+No,Yes,Yes,Medium,Female,45-60,"$150,000+",Bachelor degree
+No,Yes,Yes,Medium,,,,
+Yes,Yes,Yes,Medium,Male,45-60,"$100,000 - $149,999",Bachelor degree
+No,Yes,No,Medium,Female,45-60,"$100,000 - $149,999",Some college or Associate degree
+No,Yes,No,Medium,Male,30-44,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,Yes,Medium,Female,30-44,"$100,000 - $149,999",Graduate degree
+No,No,Yes,Medium,Male,> 60,"$50,000 - $99,999",Bachelor degree
+No,No,No,Medium,Female,30-44,"$50,000 - $99,999",Bachelor degree
+No,Yes,Yes,Medium,Female,30-44,"$25,000 - $49,999",Some college or Associate degree
+No,Yes,Yes,Medium,Male,> 60,"$150,000+",Some college or Associate degree
+No,Yes,Yes,Medium,Female,> 60,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,Yes,Medium,Female,> 60,"$50,000 - $99,999",Graduate degree
+Yes,Yes,Yes,Medium,Male,45-60,"$50,000 - $99,999",Bachelor degree
+No,Yes,No,Medium,Female,30-44,"$150,000+",Bachelor degree
+No,Yes,Yes,Medium,Female,45-60,"$50,000 - $99,999",Graduate degree
+No,Yes,Yes,Medium,Female,> 60,"$50,000 - $99,999",Graduate degree
+No,Yes,No,Medium,,,,
+No,No,No,Medium,Female,18-29,,High school degree
+No,Yes,Yes,Medium,Male,45-60,"$150,000+",Graduate degree
+No,Yes,Yes,Medium,Female,> 60,,Graduate degree
+No,No,Yes,Medium,Male,45-60,"$100,000 - $149,999",Bachelor degree
+No,No,Yes,Medium,Female,> 60,"$50,000 - $99,999",High school degree
+No,No,No,Medium,Female,45-60,"$50,000 - $99,999",Graduate degree
+No,Yes,No,Medium,Female,30-44,"$100,000 - $149,999",Graduate degree
+No,No,No,Medium,Female,45-60,,Graduate degree
+No,Yes,Yes,Medium,Female,30-44,"$50,000 - $99,999",Bachelor degree
+,No,Yes,Medium,Female,45-60,,Bachelor degree
+No,Yes,No,Medium,Female,18-29,"$0 - $24,999",Bachelor degree
+No,Yes,Yes,Medium,Male,30-44,"$100,000 - $149,999",Graduate degree
+No,Yes,No,Medium,Male,18-29,"$25,000 - $49,999",Graduate degree
+Yes,Yes,No,Medium,Male,18-29,"$0 - $24,999",Some college or Associate degree
+Yes,Yes,Yes,Medium,Female,45-60,"$100,000 - $149,999",Some college or Associate degree
+No,Yes,No,Medium,Female,18-29,,Some college or Associate degree
+No,Yes,No,Medium,Female,18-29,"$0 - $24,999",Some college or Associate degree
+No,Yes,Yes,Medium,Male,45-60,"$0 - $24,999",Bachelor degree
+Yes,Yes,No,Medium,Female,45-60,"$150,000+",Bachelor degree
+No,Yes,No,Medium,Male,> 60,"$50,000 - $99,999",Graduate degree
+No,No,No,Medium,Female,> 60,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,Yes,Medium,Female,45-60,"$150,000+",Graduate degree
+No,Yes,Yes,Medium,Female,30-44,"$50,000 - $99,999",Some college or Associate degree
+Yes,Yes,Yes,Medium,Female,30-44,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,Yes,Medium,Male,30-44,"$100,000 - $149,999",Bachelor degree
+No,Yes,Yes,Medium,Male,> 60,,Graduate degree
+Yes,Yes,Yes,Medium,Male,18-29,"$0 - $24,999",
+No,Yes,No,Medium rare,Male,> 60,"$50,000 - $99,999",Some college or Associate degree
+No,No,No,Medium rare,Male,18-29,"$0 - $24,999",Some college or Associate degree
+No,Yes,Yes,Medium rare,Male,18-29,"$25,000 - $49,999",Some college or Associate degree
+No,Yes,Yes,Medium rare,Male,> 60,"$25,000 - $49,999",Some college or Associate degree
+No,Yes,No,Medium rare,Male,> 60,"$50,000 - $99,999",High school degree
+No,No,Yes,Medium rare,Male,18-29,"$100,000 - $149,999",Graduate degree
+No,Yes,Yes,Medium rare,Male,18-29,"$50,000 - $99,999",Bachelor degree
+No,No,No,Medium rare,Male,30-44,"$50,000 - $99,999",Graduate degree
+No,Yes,Yes,Medium rare,Male,18-29,"$25,000 - $49,999",Bachelor degree
+No,No,No,Medium rare,Male,> 60,"$25,000 - $49,999",Some college or Associate degree
+No,No,No,Medium rare,Male,45-60,"$0 - $24,999",Some college or Associate degree
+Yes,Yes,Yes,Medium rare,Male,45-60,"$50,000 - $99,999",Graduate degree
+No,No,No,Medium rare,Male,> 60,,Graduate degree
+No,Yes,Yes,Medium rare,Male,> 60,"$0 - $24,999",High school degree
+No,No,Yes,Medium rare,Male,45-60,"$0 - $24,999",Some college or Associate degree
+No,Yes,Yes,Medium rare,Male,> 60,"$150,000+",Bachelor degree
+No,Yes,No,Medium rare,Male,45-60,,High school degree
+No,No,No,Medium rare,Male,18-29,"$100,000 - $149,999",Some college or Associate degree
+No,Yes,Yes,Medium rare,Male,> 60,"$50,000 - $99,999",Bachelor degree
+No,Yes,No,Medium rare,Male,45-60,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,Yes,Medium rare,Male,45-60,,Some college or Associate degree
+No,Yes,No,Medium rare,,,,
+Yes,Yes,No,Medium rare,Male,18-29,"$0 - $24,999",Some college or Associate degree
+Yes,Yes,Yes,Medium rare,Male,45-60,,Graduate degree
+No,Yes,No,Medium rare,Female,> 60,"$100,000 - $149,999",Bachelor degree
+No,Yes,No,Medium rare,Male,45-60,,Some college or Associate degree
+No,No,Yes,Medium rare,Female,> 60,,Some college or Associate degree
+No,Yes,Yes,Medium rare,Male,45-60,"$100,000 - $149,999",Bachelor degree
+Yes,Yes,No,Medium rare,Male,18-29,,High school degree
+No,Yes,No,Medium rare,Female,> 60,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,Yes,Medium rare,Female,> 60,"$25,000 - $49,999",Graduate degree
+No,Yes,No,Medium rare,Female,> 60,"$100,000 - $149,999",Graduate degree
+Yes,No,Yes,Medium rare,Female,> 60,"$0 - $24,999",Bachelor degree
+No,Yes,No,Medium rare,Male,30-44,,Some college or Associate degree
+No,Yes,No,Medium rare,Male,18-29,"$0 - $24,999",Bachelor degree
+No,Yes,Yes,Medium rare,Male,45-60,"$100,000 - $149,999",High school degree
+No,No,Yes,Medium rare,Male,18-29,"$50,000 - $99,999",High school degree
+No,Yes,No,Medium rare,Male,45-60,"$100,000 - $149,999",Bachelor degree
+No,Yes,Yes,Medium rare,Male,30-44,"$100,000 - $149,999",Graduate degree
+No,No,No,Medium rare,Female,> 60,"$25,000 - $49,999",Bachelor degree
+No,Yes,Yes,Medium rare,Female,> 60,"$150,000+",Graduate degree
+No,Yes,No,Medium rare,Male,45-60,"$50,000 - $99,999",Graduate degree
+No,No,No,Medium rare,Male,45-60,"$50,000 - $99,999",Bachelor degree
+No,No,No,Medium rare,,,,
+No,No,No,Medium rare,Male,30-44,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,Yes,Medium rare,Male,18-29,"$25,000 - $49,999",Some college or Associate degree
+Yes,Yes,Yes,Medium rare,Female,18-29,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,Yes,Medium rare,Male,30-44,"$50,000 - $99,999",Bachelor degree
+No,Yes,No,Medium rare,Female,18-29,,Graduate degree
+No,Yes,No,Medium rare,,,,
+No,Yes,No,Medium rare,Female,> 60,,Some college or Associate degree
+No,No,No,Medium rare,Male,18-29,"$50,000 - $99,999",Some college or Associate degree
+Yes,Yes,No,Medium rare,Female,45-60,"$100,000 - $149,999",Graduate degree
+No,Yes,Yes,Medium rare,,,,
+No,Yes,No,Medium rare,Male,30-44,"$50,000 - $99,999",Graduate degree
+Yes,Yes,Yes,Medium rare,Female,18-29,"$150,000+",Graduate degree
+No,Yes,Yes,Medium rare,Male,> 60,,Bachelor degree
+No,Yes,No,Medium rare,Male,30-44,"$25,000 - $49,999",Some college or Associate degree
+No,Yes,Yes,Medium rare,Female,18-29,"$150,000+",Bachelor degree
+No,Yes,No,Medium rare,Male,> 60,,Graduate degree
+No,Yes,Yes,Medium rare,Male,45-60,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,No,Medium rare,Female,30-44,"$100,000 - $149,999",Graduate degree
+No,Yes,Yes,Medium rare,Female,45-60,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,Yes,Medium rare,Male,30-44,"$25,000 - $49,999",Bachelor degree
+No,Yes,Yes,Medium rare,Female,30-44,"$50,000 - $99,999",Bachelor degree
+No,Yes,No,Medium rare,Female,> 60,"$150,000+",Bachelor degree
+No,Yes,Yes,Medium rare,Female,> 60,"$25,000 - $49,999",High school degree
+No,Yes,No,Medium rare,Female,30-44,"$50,000 - $99,999",Bachelor degree
+No,Yes,Yes,Medium rare,Female,45-60,"$50,000 - $99,999",Graduate degree
+No,Yes,Yes,Medium rare,Male,> 60,"$50,000 - $99,999",Graduate degree
+No,Yes,No,Medium rare,Female,> 60,,Bachelor degree
+No,Yes,No,Medium rare,Female,45-60,,Graduate degree
+No,No,No,Medium rare,Female,> 60,"$25,000 - $49,999",Some college or Associate degree
+No,No,No,Medium rare,Male,45-60,,Some college or Associate degree
+No,Yes,Yes,Medium rare,Female,30-44,"$25,000 - $49,999",Bachelor degree
+Yes,Yes,Yes,Medium rare,Male,30-44,"$25,000 - $49,999",Some college or Associate degree
+Yes,Yes,No,Medium rare,Female,18-29,"$0 - $24,999",Graduate degree
+No,Yes,No,Medium rare,Female,45-60,"$50,000 - $99,999",Bachelor degree
+Yes,Yes,Yes,Medium rare,Female,18-29,"$100,000 - $149,999",Bachelor degree
+No,Yes,Yes,Medium rare,Male,> 60,"$50,000 - $99,999",Bachelor degree
+No,Yes,No,Medium rare,Female,18-29,"$25,000 - $49,999",Bachelor degree
+No,Yes,Yes,Medium rare,Male,30-44,"$100,000 - $149,999",Bachelor degree
+No,Yes,No,Medium rare,Female,30-44,"$0 - $24,999",Some college or Associate degree
+Yes,Yes,Yes,Medium rare,Female,18-29,"$25,000 - $49,999",Some college or Associate degree
+No,Yes,No,Medium rare,Male,30-44,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,No,Medium rare,Female,45-60,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,Yes,Medium rare,Male,30-44,"$25,000 - $49,999",Graduate degree
+No,Yes,Yes,Medium rare,Male,30-44,"$25,000 - $49,999",Some college or Associate degree
+No,Yes,Yes,Medium rare,Female,> 60,"$25,000 - $49,999",Some college or Associate degree
+Yes,Yes,No,Medium rare,Female,30-44,,Some college or Associate degree
+No,Yes,Yes,Medium rare,Male,45-60,"$25,000 - $49,999",Graduate degree
+No,Yes,Yes,Medium rare,Female,30-44,"$100,000 - $149,999",Bachelor degree
+No,Yes,No,Medium rare,Female,30-44,"$25,000 - $49,999",Graduate degree
+Yes,No,No,Medium rare,Female,45-60,"$100,000 - $149,999",Bachelor degree
+No,Yes,Yes,Medium rare,Female,45-60,"$150,000+",Bachelor degree
+No,No,Yes,Medium rare,Male,> 60,,High school degree
+No,No,No,Medium rare,Male,30-44,"$50,000 - $99,999",Graduate degree
+No,Yes,No,Medium rare,Female,30-44,"$100,000 - $149,999",Graduate degree
+No,Yes,No,Medium rare,Male,> 60,"$150,000+",Graduate degree
+Yes,Yes,Yes,Medium rare,Female,45-60,"$50,000 - $99,999",Some college or Associate degree
+Yes,No,No,Medium rare,Male,30-44,"$100,000 - $149,999",Bachelor degree
+No,Yes,No,Medium rare,Female,30-44,"$150,000+",Bachelor degree
+No,No,No,Medium rare,Female,> 60,"$50,000 - $99,999",Graduate degree
+No,No,No,Medium rare,Female,> 60,"$50,000 - $99,999",Graduate degree
+Yes,Yes,Yes,Medium rare,,,,
+No,Yes,Yes,Medium rare,,,,
+No,Yes,No,Medium rare,Male,> 60,"$100,000 - $149,999",Graduate degree
+No,Yes,Yes,Medium rare,Female,> 60,,High school degree
+Yes,Yes,No,Medium rare,Male,> 60,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,Yes,Medium rare,Female,> 60,"$50,000 - $99,999",Graduate degree
+Yes,Yes,Yes,Medium rare,Female,45-60,,Bachelor degree
+No,No,No,Medium rare,Male,> 60,"$50,000 - $99,999",Bachelor degree
+No,No,No,Medium rare,Female,> 60,,Some college or Associate degree
+No,No,No,Medium rare,Male,30-44,"$100,000 - $149,999",Some college or Associate degree
+No,Yes,Yes,Medium rare,Female,> 60,"$50,000 - $99,999",Graduate degree
+No,Yes,Yes,Medium rare,,,,
+No,Yes,Yes,Medium rare,Female,45-60,"$100,000 - $149,999",Bachelor degree
+No,Yes,Yes,Medium rare,Male,30-44,"$100,000 - $149,999",Bachelor degree
+No,Yes,Yes,Medium rare,Female,45-60,,Graduate degree
+No,Yes,Yes,Medium rare,Female,45-60,"$100,000 - $149,999",Graduate degree
+No,Yes,Yes,Medium rare,Male,> 60,"$50,000 - $99,999",Bachelor degree
+No,Yes,Yes,Medium rare,Female,45-60,"$50,000 - $99,999",Bachelor degree
+No,Yes,Yes,Medium rare,Male,45-60,"$50,000 - $99,999",Graduate degree
+No,No,No,Medium rare,Female,> 60,,Some college or Associate degree
+No,No,No,Medium rare,Female,18-29,"$25,000 - $49,999",High school degree
+No,No,Yes,Medium rare,Female,> 60,"$50,000 - $99,999",Bachelor degree
+No,Yes,No,Medium rare,Male,> 60,"$50,000 - $99,999",Graduate degree
+Yes,Yes,Yes,Medium rare,Female,18-29,"$50,000 - $99,999",Some college or Associate degree
+Yes,Yes,Yes,Medium rare,Female,30-44,"$50,000 - $99,999",Graduate degree
+No,Yes,No,Medium rare,Male,45-60,"$100,000 - $149,999",Bachelor degree
+No,Yes,No,Medium rare,Male,18-29,"$100,000 - $149,999",Bachelor degree
+No,Yes,No,Medium rare,Female,30-44,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,No,Medium rare,Female,> 60,"$50,000 - $99,999",Graduate degree
+Yes,Yes,Yes,Medium rare,Male,45-60,"$100,000 - $149,999",Bachelor degree
+No,Yes,Yes,Medium rare,,,,
+No,Yes,No,Medium rare,Female,30-44,"$50,000 - $99,999",Bachelor degree
+No,Yes,Yes,Medium rare,Male,30-44,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,Yes,Medium rare,Female,45-60,"$100,000 - $149,999",Some college or Associate degree
+No,Yes,Yes,Medium rare,Male,45-60,"$100,000 - $149,999",Bachelor degree
+No,No,No,Medium rare,Female,18-29,"$25,000 - $49,999",Some college or Associate degree
+Yes,Yes,Yes,Medium rare,Male,30-44,"$100,000 - $149,999",Bachelor degree
+No,Yes,No,Medium rare,Female,> 60,"$50,000 - $99,999",Graduate degree
+Yes,No,No,Medium rare,Female,45-60,"$0 - $24,999",Bachelor degree
+No,Yes,Yes,Medium rare,Male,45-60,"$150,000+",Graduate degree
+No,Yes,Yes,Medium rare,Female,45-60,,Graduate degree
+No,No,No,Medium rare,Female,45-60,"$25,000 - $49,999",High school degree
+No,No,Yes,Medium rare,Female,18-29,"$25,000 - $49,999",Graduate degree
+Yes,Yes,Yes,Medium rare,Female,30-44,,Some college or Associate degree
+No,Yes,No,Medium rare,Female,30-44,,Graduate degree
+Yes,Yes,No,Medium rare,Male,30-44,"$50,000 - $99,999",Some college or Associate degree
+No,No,No,Medium rare,Female,45-60,"$0 - $24,999",Some college or Associate degree
+No,No,No,Medium rare,Female,> 60,"$50,000 - $99,999",Graduate degree
+Yes,Yes,No,Medium rare,Male,18-29,"$0 - $24,999",Some college or Associate degree
+No,Yes,No,Medium rare,Male,18-29,"$25,000 - $49,999",High school degree
+No,Yes,No,Medium rare,Female,> 60,,Graduate degree
+No,Yes,No,Medium rare,Female,30-44,"$50,000 - $99,999",Some college or Associate degree
+No,No,No,Medium rare,Male,30-44,"$50,000 - $99,999",Graduate degree
+Yes,Yes,Yes,Medium rare,Male,45-60,,High school degree
+No,Yes,Yes,Medium rare,Male,30-44,"$100,000 - $149,999",Bachelor degree
+No,Yes,Yes,Medium rare,Female,30-44,"$100,000 - $149,999",Graduate degree
+No,Yes,No,Medium rare,Male,> 60,"$150,000+",Bachelor degree
+No,Yes,No,Medium rare,Female,45-60,,Some college or Associate degree
+Yes,Yes,Yes,Medium rare,Female,18-29,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,Yes,Medium rare,Male,> 60,"$50,000 - $99,999",Bachelor degree
+No,No,No,Medium rare,Female,18-29,"$25,000 - $49,999",High school degree
+Yes,Yes,Yes,Medium rare,Male,30-44,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,No,Medium Well,Male,> 60,"$50,000 - $99,999",Bachelor degree
+No,Yes,No,Medium Well,Male,18-29,"$25,000 - $49,999",Some college or Associate degree
+Yes,Yes,Yes,Medium Well,,,,
+Yes,Yes,Yes,Medium Well,Male,18-29,"$50,000 - $99,999",High school degree
+No,Yes,Yes,Medium Well,Male,45-60,"$50,000 - $99,999",Some college or Associate degree
+No,No,No,Medium Well,Male,45-60,"$50,000 - $99,999",Bachelor degree
+No,Yes,Yes,Medium Well,Male,45-60,,Some college or Associate degree
+Yes,Yes,No,Medium Well,Male,> 60,"$50,000 - $99,999",Graduate degree
+No,No,,Medium Well,Male,> 60,"$50,000 - $99,999",Some college or Associate degree
+Yes,Yes,Yes,Medium Well,Male,30-44,"$50,000 - $99,999",Bachelor degree
+No,No,Yes,Medium Well,Male,> 60,"$25,000 - $49,999",Some college or Associate degree
+No,Yes,No,Medium Well,Male,> 60,"$150,000+",Bachelor degree
+No,Yes,Yes,Medium Well,Male,45-60,"$100,000 - $149,999",Bachelor degree
+No,Yes,Yes,Medium Well,Female,> 60,"$0 - $24,999",High school degree
+No,Yes,Yes,Medium Well,Male,> 60,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,Yes,Medium Well,Male,30-44,"$25,000 - $49,999",Some college or Associate degree
+No,Yes,No,Medium Well,Female,> 60,,Bachelor degree
+No,No,No,Medium Well,Female,> 60,,Graduate degree
+No,Yes,Yes,Medium Well,Male,> 60,"$25,000 - $49,999",Some college or Associate degree
+No,Yes,Yes,Medium Well,Male,> 60,"$150,000+",Graduate degree
+Yes,No,No,Medium Well,Female,18-29,"$25,000 - $49,999",High school degree
+No,Yes,No,Medium Well,Male,45-60,"$50,000 - $99,999",Some college or Associate degree
+No,No,No,Medium Well,Female,18-29,"$100,000 - $149,999",Some college or Associate degree
+No,No,No,Medium Well,Female,18-29,"$0 - $24,999",Some college or Associate degree
+No,Yes,Yes,Medium Well,Female,30-44,"$50,000 - $99,999",Graduate degree
+No,Yes,No,Medium Well,Female,45-60,,Graduate degree
+No,Yes,Yes,Medium Well,Male,45-60,"$100,000 - $149,999",Bachelor degree
+No,No,No,Medium Well,Female,> 60,,High school degree
+No,Yes,Yes,Medium Well,Female,30-44,"$100,000 - $149,999",Graduate degree
+No,Yes,No,Medium Well,Female,45-60,"$50,000 - $99,999",Graduate degree
+No,Yes,Yes,Medium Well,Female,30-44,"$25,000 - $49,999",Bachelor degree
+No,Yes,No,Medium Well,Female,18-29,"$50,000 - $99,999",Graduate degree
+No,Yes,No,Medium Well,Female,18-29,"$0 - $24,999",Some college or Associate degree
+No,Yes,Yes,Medium Well,Female,> 60,,Some college or Associate degree
+Yes,Yes,Yes,Medium Well,Female,30-44,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,No,Medium Well,Female,30-44,"$50,000 - $99,999",Bachelor degree
+No,Yes,No,Medium Well,Male,45-60,"$50,000 - $99,999",Bachelor degree
+No,No,No,Medium Well,Male,18-29,,Some college or Associate degree
+No,No,No,Medium Well,Female,30-44,"$25,000 - $49,999",Some college or Associate degree
+No,Yes,No,Medium Well,Male,18-29,"$25,000 - $49,999",Some college or Associate degree
+No,No,No,Medium Well,Female,45-60,"$150,000+",Bachelor degree
+No,Yes,No,Medium Well,Male,18-29,"$0 - $24,999",Bachelor degree
+No,Yes,Yes,Medium Well,Female,18-29,"$50,000 - $99,999",Graduate degree
+No,Yes,Yes,Medium Well,Female,45-60,"$50,000 - $99,999",Bachelor degree
+No,Yes,Yes,Medium Well,,,,
+No,Yes,No,Medium Well,Female,30-44,"$150,000+",Graduate degree
+No,Yes,No,Medium Well,,,,
+No,Yes,Yes,Medium Well,Male,45-60,"$100,000 - $149,999",Some college or Associate degree
+No,Yes,No,Medium Well,Female,45-60,"$150,000+",Some college or Associate degree
+No,Yes,No,Medium Well,Female,18-29,"$0 - $24,999",Bachelor degree
+No,No,,Medium Well,Female,> 60,"$50,000 - $99,999",Graduate degree
+No,Yes,No,Medium Well,Female,30-44,"$25,000 - $49,999",Bachelor degree
+No,Yes,No,Medium Well,Female,18-29,"$50,000 - $99,999",Bachelor degree
+No,Yes,No,Medium Well,,,,
+No,Yes,No,Medium Well,Female,30-44,"$25,000 - $49,999",Some college or Associate degree
+No,Yes,No,Medium Well,Female,30-44,"$50,000 - $99,999",Graduate degree
+No,Yes,Yes,Medium Well,Female,> 60,"$50,000 - $99,999",Some college or Associate degree
+Yes,Yes,No,Medium Well,Female,30-44,"$100,000 - $149,999",Graduate degree
+Yes,Yes,No,Medium Well,Male,18-29,"$0 - $24,999",Bachelor degree
+No,Yes,Yes,Medium Well,Female,45-60,"$25,000 - $49,999",Some college or Associate degree
+No,Yes,No,Medium Well,Female,18-29,,Bachelor degree
+Yes,Yes,Yes,Medium Well,Male,> 60,"$0 - $24,999",Bachelor degree
+No,No,No,Medium Well,Male,> 60,"$100,000 - $149,999",Graduate degree
+No,Yes,No,Medium Well,Male,45-60,"$150,000+",Bachelor degree
+No,Yes,Yes,Medium Well,Male,18-29,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,Yes,Medium Well,Male,45-60,,Graduate degree
+Yes,No,No,Medium Well,Female,45-60,"$25,000 - $49,999",Some college or Associate degree
+No,Yes,Yes,Medium Well,,,,
+No,Yes,Yes,Medium Well,Male,30-44,"$50,000 - $99,999",Graduate degree
+No,No,Yes,Medium Well,Female,30-44,"$0 - $24,999",Some college or Associate degree
+No,Yes,No,Medium Well,Male,> 60,"$150,000+",Graduate degree
+Yes,Yes,Yes,Medium Well,Male,45-60,"$100,000 - $149,999",Some college or Associate degree
+No,Yes,No,Medium Well,Female,> 60,"$100,000 - $149,999",Bachelor degree
+No,No,Yes,Medium Well,Male,30-44,"$50,000 - $99,999",Graduate degree
+,No,,Medium Well,Female,30-44,"$0 - $24,999",Bachelor degree
+No,Yes,Yes,Rare,Male,> 60,"$150,000+",Graduate degree
+Yes,Yes,Yes,Rare,Male,18-29,"$50,000 - $99,999",Graduate degree
+No,Yes,No,Rare,Male,> 60,,Graduate degree
+No,Yes,No,Rare,Male,30-44,"$25,000 - $49,999",Bachelor degree
+Yes,Yes,Yes,Rare,Male,> 60,,Bachelor degree
+No,Yes,Yes,Rare,Male,45-60,"$100,000 - $149,999",Bachelor degree
+Yes,No,No,Rare,Female,> 60,"$0 - $24,999",Some college or Associate degree
+,Yes,No,Rare,Male,18-29,"$50,000 - $99,999",High school degree
+No,Yes,Yes,Rare,Female,30-44,"$150,000+",Some college or Associate degree
+No,Yes,No,Rare,Female,> 60,"$25,000 - $49,999",Graduate degree
+No,Yes,Yes,Rare,Male,45-60,"$100,000 - $149,999",Graduate degree
+Yes,Yes,Yes,Rare,Female,30-44,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,Yes,Rare,Female,> 60,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,Yes,Rare,Female,45-60,"$100,000 - $149,999",Graduate degree
+No,Yes,No,Rare,Female,> 60,"$100,000 - $149,999",Bachelor degree
+No,Yes,No,Rare,Female,45-60,,Bachelor degree
+No,No,Yes,Rare,Female,30-44,"$0 - $24,999",Some college or Associate degree
+No,No,No,Rare,Female,45-60,"$50,000 - $99,999",Bachelor degree
+No,Yes,No,Rare,Female,45-60,"$50,000 - $99,999",Graduate degree
+Yes,Yes,Yes,Rare,,,,
+Yes,Yes,Yes,Rare,Male,18-29,"$25,000 - $49,999",Some college or Associate degree
+No,Yes,No,Rare,Female,18-29,"$50,000 - $99,999",Bachelor degree
+No,Yes,No,Rare,Male,> 60,"$150,000+",Graduate degree
+No,Yes,No,Well,Male,18-29,"$25,000 - $49,999",High school degree
+Yes,No,No,Well,Male,18-29,"$150,000+",
+No,No,Yes,Well,Male,45-60,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,No,Well,Male,18-29,,Some college or Associate degree
+No,No,Yes,Well,Female,18-29,"$25,000 - $49,999",Some college or Associate degree
+Yes,Yes,Yes,Well,Male,45-60,"$50,000 - $99,999",Bachelor degree
+No,Yes,No,Well,,,,
+No,No,Yes,Well,Female,45-60,,Some college or Associate degree
+No,Yes,Yes,Well,Male,45-60,"$100,000 - $149,999",Bachelor degree
+No,No,No,Well,Male,30-44,,Graduate degree
+No,Yes,No,Well,Female,> 60,"$50,000 - $99,999",Bachelor degree
+No,Yes,Yes,Well,Male,> 60,"$0 - $24,999",Bachelor degree
+Yes,Yes,No,Well,Female,18-29,"$100,000 - $149,999",Less than high school degree
+No,Yes,Yes,Well,Female,30-44,"$50,000 - $99,999",Graduate degree
+No,No,No,Well,Female,30-44,"$50,000 - $99,999",Bachelor degree
+No,Yes,Yes,Well,Female,45-60,"$100,000 - $149,999",Bachelor degree
+No,Yes,No,Well,Female,45-60,"$50,000 - $99,999",Bachelor degree
+No,No,No,Well,Female,45-60,"$150,000+",Bachelor degree
+No,Yes,Yes,Well,Female,45-60,"$100,000 - $149,999",Some college or Associate degree
+No,No,No,Well,Male,45-60,"$50,000 - $99,999",Bachelor degree
+No,No,No,Well,Female,30-44,,High school degree
+Yes,Yes,Yes,Well,Female,18-29,,Some college or Associate degree
+Yes,Yes,Yes,Well,Male,30-44,"$0 - $24,999",High school degree
+No,Yes,No,Well,Female,45-60,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,No,Well,Female,30-44,,Some college or Associate degree
+No,Yes,Yes,Well,Female,> 60,,Graduate degree
+No,No,No,Well,Female,30-44,,Graduate degree
+No,Yes,Yes,Well,Male,30-44,"$50,000 - $99,999",Some college or Associate degree
+No,Yes,No,Well,Female,> 60,"$50,000 - $99,999",Bachelor degree
+No,Yes,Yes,Well,Male,30-44,,Bachelor degree
+No,Yes,Yes,Well,Female,45-60,,Some college or Associate degree
+No,Yes,Yes,Well,Female,45-60,"$150,000+",High school degree
+,No,No,Well,Male,45-60,"$50,000 - $99,999",Bachelor degree
+No,Yes,No,Well,Male,30-44,"$0 - $24,999",Some college or Associate degree
+No,No,Yes,Well,Female,30-44,,Some college or Associate degree
+No,Yes,Yes,Well,Female,> 60,"$100,000 - $149,999",Bachelor degree
\ No newline at end of file
diff --git a/untitled.txt b/untitled.txt
new file mode 100644
index 0000000..e69de29