{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "worldwide-blood",
   "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: What effect does technology usage have on teens?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "appreciated-testimony",
   "metadata": {},
   "source": [
    "I am interested in this topic after recently reading “The Anxious Generation.” by Johnathan Haidt In this book, Haidt, explores how the wide prevalence of the smartphone and social media has rewired childhood for kids during middle school and early high school. His main premise is that increased social media and smartphone usage has led to the massive increases in depression and anxiety we have seen since 2012, which is about the same time the smartphone became readily available. While I think this project will be similar to his work, I will not set out to look at his main premise, but rather explore some other ideas related to teen tech habits. This question also has relevance to my work as a teacher where social media is a daily part of my students’ lives. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "permanent-pollution",
   "metadata": {},
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "technical-evans",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Include any import statements you will need\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "overhead-sigma",
   "metadata": {},
   "outputs": [],
   "source": [
    "### 💻 FILL IN YOUR DATASET FILE NAME BELOW 💻 ###\n",
    "\n",
    "file_name = \"March 7-April 10, 2018 - Teens and Tech Survey - CSV.csv\"\n",
    "dataset_path = \"data/\" + file_name\n",
    "\n",
    "df = pd.read_csv(dataset_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "heated-blade",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .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>CASEID</th>\n",
       "      <th>SURV_LANG</th>\n",
       "      <th>FITIN</th>\n",
       "      <th>FRIEND1</th>\n",
       "      <th>FRIEND2</th>\n",
       "      <th>FRIEND3</th>\n",
       "      <th>FRIEND4_1</th>\n",
       "      <th>FRIEND4_2</th>\n",
       "      <th>FRIEND4_3</th>\n",
       "      <th>FRIEND4_4</th>\n",
       "      <th>...</th>\n",
       "      <th>HH25</th>\n",
       "      <th>HH612</th>\n",
       "      <th>HH1317</th>\n",
       "      <th>HH18OV</th>\n",
       "      <th>CO_DATE</th>\n",
       "      <th>DURATION</th>\n",
       "      <th>SURV_MODE</th>\n",
       "      <th>MODE_END</th>\n",
       "      <th>DEVICE</th>\n",
       "      <th>WEIGHT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>22.866667</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>0.794092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>EN</td>\n",
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       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>20180324</td>\n",
       "      <td>17.850000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Smartphone</td>\n",
       "      <td>0.313173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>9</td>\n",
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       "      <td>2</td>\n",
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       "      <td>...</td>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>20180323</td>\n",
       "      <td>12.316667</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Smartphone</td>\n",
       "      <td>0.357175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>EN</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>20180402</td>\n",
       "      <td>9.733333</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Smartphone</td>\n",
       "      <td>0.762108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13</td>\n",
       "      <td>EN</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>20180308</td>\n",
       "      <td>14.633333</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Smartphone</td>\n",
       "      <td>0.842675</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 176 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   CASEID SURV_LANG  FITIN  FRIEND1  FRIEND2  FRIEND3  FRIEND4_1  FRIEND4_2  \\\n",
       "0       2        EN     77        3        1        2          0          1   \n",
       "1       7        EN      2        2        1        3          1          1   \n",
       "2       9        EN      2        2        1        3          0          0   \n",
       "3      10        EN      1        4        3        2          0          1   \n",
       "4      13        EN      2        4        4        2          0          1   \n",
       "\n",
       "   FRIEND4_3  FRIEND4_4  ...  HH25  HH612  HH1317 HH18OV   CO_DATE   DURATION  \\\n",
       "0          0          1  ...     1      3       2      4  20180322  22.866667   \n",
       "1          1          0  ...     0      2       2      4  20180324  17.850000   \n",
       "2          0          1  ...     1      2       1      2  20180323  12.316667   \n",
       "3          1          0  ...     0      0       1      2  20180402   9.733333   \n",
       "4          0          0  ...     0      1       2      3  20180308  14.633333   \n",
       "\n",
       "  SURV_MODE MODE_END      DEVICE    WEIGHT  \n",
       "0         1        1     Desktop  0.794092  \n",
       "1         2        2  Smartphone  0.313173  \n",
       "2         2        2  Smartphone  0.357175  \n",
       "3         2        2  Smartphone  0.762108  \n",
       "4         2        2  Smartphone  0.842675  \n",
       "\n",
       "[5 rows x 176 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "continental-franklin",
   "metadata": {},
   "source": [
    "**Data Overview**\n",
    "\n",
    "This data set shows the results of a survey by the Pew Research Center given to teens in 2018. Teens were asked various questions about technology and most were answered on a scale of 0-4. The dataset also includes some general demographic information. A key supplements the data which includes the full question and responses for each question. I will refer to those questions and responses in the methods and results below."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "infinite-instrument",
   "metadata": {},
   "source": [
    "# Methods and Results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26501d6d-81d8-499c-b226-618e43fee349",
   "metadata": {},
   "source": [
    "First, the data relavant to this study will be extracted into a new data set.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "basic-canadian",
   "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>INTREQ</th>\n",
       "      <th>FITIN</th>\n",
       "      <th>SOC1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>77</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AGE  INTREQ  FITIN  SOC1\n",
       "0   14       5     77     3\n",
       "1   15       1      2     2\n",
       "2   13       2      2     3\n",
       "3   17       1      1     2\n",
       "4   15       2      2     2"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df=df[['AGE','INTREQ','FITIN','SOC1']]\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "raw",
   "id": "b7ff5919-a432-41dd-9213-10861771dcfe",
   "metadata": {},
   "source": [
    "AGE indicates participant age\n",
    "INTREQ indicates frequency of which a teen uses technology (further defined below)\n",
    "FITIN indicates how well a teen thinks he or she fits in\n",
    "SOCI indicates what effect teens think social media has on teens their age."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "recognized-positive",
   "metadata": {},
   "source": [
    "## First Research Question: How does the amount of time teens spend on technology vary by age?\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",
    "✏️ *Write your answer below:*\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "portuguese-japan",
   "metadata": {},
   "source": [
    "### Results "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "39209c3f-7c3f-4a3e-abbd-7904e485e9e3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13    0.220727\n",
       "15    0.211306\n",
       "16    0.192463\n",
       "17    0.191117\n",
       "14    0.184388\n",
       "Name: AGE, dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.AGE.value_counts(normalize=True)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "negative-highlight",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "sns.set_theme()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "victorian-burning",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='AGE', ylabel='count'>"
      ]
     },
     "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.countplot(data=new_df, x=\"AGE\", hue=\"INTREQ\")\n",
    "\n",
    "\n",
    "# Is there a way to put these labels on the key/legend?\n",
    "#new_labels = [\"Almost Constantly\", \"Several Times a Day\", \"About Once a Day\", \"Several times a week\", \"Less often\"]\n",
    "#handles, labels = get_legend_handles_labels()\n",
    "#legend(handles, new_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "2eff3a2b-30ca-4cf3-b365-d41acc121ece",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/c3/lngy07lx6hx3_kdztwrlr7j80000gn/T/ipykernel_6406/2489872726.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_df[\"Tech Frequency\"] = pd.cut(new_df.INTREQ, bins=bins, labels=labels)\n"
     ]
    }
   ],
   "source": [
    "bins = [0, 1, 2]\n",
    "labels = [\"Almost Constantly\", \"Several Times Per Day\"]\n",
    "new_df[\"Tech Frequency\"] = pd.cut(new_df.INTREQ, bins=bins, labels=labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "fa39e859-5e46-4592-a0ac-d4ffd00a5038",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Tech Frequency', ylabel='count'>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Z6Z32lZGRgc8//xze3t7w8fHB4cOHce/ePezatQv29vZQq9VISUnBkiVLMH78+DK/miGRlZklFEolrqxbj/R7ZXsX3hep7NYEDQb4SF0GEZGkjGqOzVdffYXMzEzMmDEDABAZGQlXV1fY29vrlmndujXS09MRHR0Nd3d3qUqlV1z6vQSkxcVJXYYe62ryO7uBiOhlGU2wefjwIUJDQ/HRRx+hQoUKAIDExEQ4OTnpLVelShUAQEJCQrGCjYlJ6c5HUCgUkk0EexGtVkAI4zyWy3ki8lfeP+PyUH95qPFVpNUa5+8EY6NSKYr1O9pogs327dtha2uLQYMG6dqysrJgZ2ent5y5uTmAp/eiMJRSqYCDg7XBzy8KrVYYdbAx1tpI/jg/qfTxPTZOWVkqJCcri/2LW660WgWUSiXs7a1gYWFh8HqMJtiEh4ejb9++ei/GwsICOTk5esvlBxorKyuDx9JqBdLSMgx+/ouoVErY2VlizY5zuJv0uNTGMUSNKvaYMLgd0tIyodFopS6ngPz3juTLWH/2iqo8/IyW9/dYrnJysqHVaqHRCOTl8fP5L41GQKvV4vHjDGRmagr029lZFmlvpFEEm2vXriE+Ph5vv/22XruTkxNiYmL02pKSkgAAVatWLdaYZfFDdTfpMW7dfVTq4xhCo9FywyJJ8Gev9PE9Nk4ajXFOATA2xQ1+RhFsIiMj4ejoiIYNG+q1t2zZEuHh4UhPT4eNjQ0A4MKFC7C2ti6wLBERUXmlVEo3L1Nu19AximATFRWFBg0aFGjv2rUrVq5ciSlTpmDatGm4c+cOli9fjlGjRvFUbyIikgWlUoEKDpZQKaW58KxGq0Hqo8xihZstW0IQEXEeq1dv0LUtXjwfhw6F6y3n5FQNe/YcMnicojCKYPPgwQPdmVD/Zm5ujuDgYMyZMwcDBw6Evb093n33Xfj5+ZV9kURERKVAqVRApVRh/enNuPf4fpmOXd2+Knw7DYNSqTA42OzbtxtBQevg5uah1379+l8YOnQk+vf/56QgZRmEN6MINkFBQc/se+2117Bx48YyrIaIiKjs3Xt8H3Epd6Quo8iSkx9gyZKFuHw5ErVq1dbrE0Lg1q2bGDJkBBwdK5VpXTzfjIiIiF7atWvRMDU1QWjoDri4NNbru3v3DjIzM1GnTt0yr8so9tgQERFR+dK+fUe0b9+x0L4bN64DAHbvDkNExE9QKBRo3botxo2boDsZqLQw2BAREVGJio29AaVSiUqVKmHx4uW4e/cO1qxZhZs3b+DLL7+CUll6B4wYbIiIiKhEDRs2Ct7e/WFvXwEAUK9efVSsWAm+viMQHR0FV9fGz19BMXCODREREZWop7dGqKDXVq+eMwDgwYPSPfOLwYaIiIhK1Lx5/8MHH+hfmiU6+g8AQN26zqU6Ng9FERERGYHq9sW7VZAxjfnGG13g7/8RQkKC0L37m7h9Ow7Lly9Bt2498dprdUplzHwMNkRERBLSagU0Wg18Ow2TZHyNVlPit1Ro374T5s5dhK1bQ7B1ayhsbGzRrVtPjB37fomOUxgGGyIiIglptQKpjzLL9b2iAgJmF2jz8uoKL6+uxVqvIRhsiIiIJCa3G1FKiZOHiYiISDYYbIiIiEg2GGyIiIhINhhsiIiISDYYbIiIiEg2GGyIiIhINhhsiIiISDZ4HZtXlEplnJnWWOsiIipNSqWiXF+gz5gw2Lxi7G0tILRa2NlZSl0KERHhaahxqGAJpUolyfhajQaPUjOLFW62bAlBRMR5rF69QdeWnJyMwMDliIj4CUqlCq1atcEHH0xDhQoVSqDqZ2OwecVYW5hBoVQi9nAQMlMSpC6nAPu6jVGjYz+pyyAiKjNKpQJKlQpX1q1H+r2y/V62qV4NHu/7QqlUGBxs9u3bjaCgdXBz89C15eTk4MMP/WBjY4Mvv/wKubl5+PzzuZg//39YtuzLEqq+cAw2r6jMlARk3r8tdRkFWFR0kroEIiJJpN9LQFpcnNRlFFly8gMsWbIQly9Holat2np9J058i8TEBOzcGY6KFR0BAJMmfYgvvliEjIwnsLKyLrW6OKGBiIiIXtq1a9EwNTVBaOgOuLg01uuLiDiP5s1b6kINALRq1Qa7dh0o1VADcI8NERERGaB9+45o375joX3x8XFwd2+G0NBgHDt2GHl5efD0bAM/v8mwtbUt1bq4x4aIiIhK1JMnT3Ds2GFcvx6DWbPmY/r0APz22xXMnPkRhCjdM7C4x4aIiIhKlImJCaysrDB79kKYmDyNGnZ2szF27HBcuxaFRo1cS2/sUlszERFROVOa15PRaqW5To0UKleuAiGgCzUAULeuMwDg3r17DDZERESlrbSvJ5OVpUJyshIqlQImJv/MBJHjhUk9PJphz54wZGdnwdzcAgBw48Z1AEDNmrVKdWwGGyIiIpT+9WQUNjawbN8WGRolcpT/hCdTC3PY2dV+zjPLn759+2Pfvt2YPftTjB37PtLT/8ayZZ+jWbMWaNCgYamOzWBDRET0L6V1PRmVQwWY5+VBm5sLjULzT/v/32NjU71aiY/5IqU1ZoUKFbBmTRACA5dj3LjhMDU1Q8eOnTFx4oelMt6/MdgQERFJSJOngTYvDx7v+0oyvlajKfa9ogICZhdoq1WrNpYsWVms9RrCKIJNeHg4NmzYgPj4eNSuXRsTJ07Em2++CQC4c+cO5s2bh4sXL8LKygr9+/fHpEmToJLonhpERGQ4KW/2+CJSzXXR5ObibvRNqEye/XtNaWoKqyqVodFoS3x83gSzhB04cAABAQH45JNP0KFDBxw5cgRTp06Fk5MTGjdujNGjR6NOnToICwvD7du3ERAQAKVSicmTJ0tdOhERvQSlUoEKDpZQKfmH6X9pcnOhyc19Zr9Ko4VGo0VeXskHG7mRNNgIIbBq1SoMGzYM7733HgDg/fffR2RkJH7++WfcvXsX9+7dw65du2Bvbw+1Wo2UlBQsWbIE48ePh5mZmZTlExHRS1AqFVApVVh/ejPuPb4vdTkFuNVoBJ/mb0ldBhWTpMEmNjYWd+/exdtvv63X/vXXXwMAZs+eDVdXV9jb2+v6WrdujfT0dERHR8Pd3b1M6yUiouK79/g+4lLuSF1GAdXsq0hdApUAyYMNAGRkZGD06NGIiopCzZo18f7778PLywuJiYlwctK/23OVKk9/8BISEooVbP59DYGSJsdrEhCVlPK+fZSH+o21RmOtqzxRKIBSviOB5P57nZ+XJWmwSU9PBwDMmDEDEydOxLRp0/Dtt9/Cz88PISEhyMrKgp2dnd5zzM3NAQDZ2dkGj6tUKuDgULp3FyWiwtnZWUpdguzxPZYvOYdDrVYBpVIJe3srWFhYGLweSYONqakpAGD06NHw9vYGADRq1AhRUVEICQmBhYUFcnJy9J6TH2isrKwMHlerFUhLyzD4+S+iUin5xUL0DGlpmaVyZkdZKQ/bt7G+x+XhvTN2Go1WtntsNBoBrVaLx48zkJmpKdBvZ2dZpGAnabCpWrUqAECtVuu1169fHz/88AM8PT0RExOj15eUlKT3XENxZjmRNHhmR+njeyxfcg01/6bRiGL9/Eq6T8vV1RXW1ta4evWqXntMTAxq166Nli1bIioqSnfICgAuXLgAa2trNGxYupdkJiIiovJH0j02FhYWGDNmDNasWYOqVavCzc0NR44cwblz5xAaGgoPDw+sXLkSU6ZMwbRp03Dnzh0sX74co0aN4qneREQkGypT0xdeoK+05tfwAn0lzM/PD5aWllixYgXu378PZ2dnBAYGolWrVgCA4OBgzJkzBwMHDoS9vT3effdd+Pn5SVw1ERFRyVCZmqJ6I+fnBpvSpNFokZqaUaxws2VLCCIizmP16g0AgIkTx+HKlV8KXTYgYDbefLP0rhckebABgJEjR2LkyJGF9r322mvYuHFjGVdERERUNlQmKqhMVDiwKwLJD/4u07ErVbZFn4GtoFQqDA42+/btRlDQOri5eejaFi5citx/XUlZCGDWrJlIS3uMTp28ilv2cxlFsCEiopJjrKcEG2tdxiL5wd+4fy9V6jKKLDn5AZYsWYjLlyNRq1ZtvT47O3u9x3v37kR09B8IDd1erLOai4LBhohIJuxtLSC0Wp5STWXi2rVomJqaIDR0B0JDg5GQcK/Q5R49eoSgoHUYNmwUateuU+p1MdgQEcmEtYUZFEolYg8HITMlQepyCrCv2xg1OvaTugwqIe3bd0T79h1fuNy2bZtgbm6BwYOHlkFVDDZERLKTmZKAzPu3pS6jAIuKTi9eiGQlI+MJDh3aj5Ejx+ruHFDaeMCTiIiISsWZMz8gNzcXvXv3KbMxGWyIiIioVJw58wNat24HW1vbMhuTwYaIiIhKxa+/XkaLFp5lOiaDDREREZW4+/cTkZqaivr1Xy/TcTl5mIiIyAhUqlx2h2vKYsyUlGQAgL19hVIbozAMNkRERBLS5GmgydOgz8BW0oyv0Rb7XlEBAbMLtLm4NMbZs5HFWq8hGGyIiIgkpMnNxb3oGy+8CaZVlcrQaLQlPj5vgklEREQlSpObC82/7q30XyqNFhqNFnl5JR9s5IaTh4mIiEg2GGyIiIhINhhsiIiISDYYbIiIiMqC0PsP/YcQJfPOMNgQERGVAW3GEwiNBhpGm0Ll5GQDAFSq4p3XxLOiiIiIyoDIyUXGH1FQNvUALG2hggKKoj5Xq0FOTjY0GvmFIiEEcnKykZ7+CJaWNlAqi7fPhcGGiIiojDy5+PSCdVpXFyhUz75uzX8pTUyQodJCq5Xv6d6Wljaws6tY7PUw2BAREZWhJxcjkXHlKpTW1ijqLhub6jXg8sFEPH6cIcu9NiqVSbH31ORjsCEiIipjIjcXmtTUoi9vZw8LCwtkZmp4kb4X4ORhIiIikg0GGyIiIpINBhsiIiKSDQYbIiIikg0GGyIiIpINBhsiIiKSDQYbIiIikg0GGyIiIpINBhsiIiKSDcmDzf3799GgQYMC//bt2wcAiI6OxpAhQ+Dh4QEvLy9s3rxZ4oqJiIjIWEl+S4Vr167B3NwcJ06cgELxz00zbG1t8ejRI4wcORJeXl6YM2cOrly5gjlz5sDa2ho+Pj4SVk1ERETGSPJgExMTgzp16qBKlSoF+jZt2gRTU1PMnTsXJiYmcHZ2RlxcHDZs2MBgQ0RERAVIfijqzz//hLOzc6F9kZGR8PT0hInJP/mrdevWuHXrFpKTk8uqRCIiIionjGKPjYODA9577z3Exsbitddew/vvv4+OHTsiMTERarVab/n8PTsJCQmoVKmSweOamJReplOpJM+LREarvG8f5b1+Kt/48/dikgabvLw83Lx5E/Xr14e/vz9sbGxw5MgRjBs3DiEhIcjKyoKZmZnec8zNzQEA2dnZBo+rVCrg4GBdrNqJyDB2dpZSl0BUbnH7eTFJg42JiQkiIiKgUqlgYWEBAGjcuDH++usvfP3117CwsEBOTo7ec/IDjZWVlcHjarUCaWkZhhf+AiqVkj98RM+QlpYJjUYrdRkG4/ZNUirv209x2NlZFmmPleSHoqytC+45ef3113H27Fk4OTkhKSlJry//cdWqVYs1bl7eq/mDQSQ1jUbL7Y/IQNx+XkzSg3V//fUXmjVrhoiICL3233//HfXr10fLli1x6dIlaDQaXd+FCxdQt25dODo6lnW5REREZOQkDTbOzs6oV68e5s6di8jISNy4cQOff/45rly5gvfffx8+Pj5IT09HQEAArl+/jn379iE0NBS+vr5Slk1ERERGStJDUUqlEl999RW++OILTJkyBWlpaXBxcUFISIjubKjg4GAsWLAA3t7eqFy5MqZPnw5vb28pyyYiIiIjZVCwuXjxIlxcXAqdH5OWloYff/wRvXv3LtK6KlWqhM8///yZ/W5ubti5c6chZRIREdErxqBDUcOGDcONGzcK7YuKisLMmTOLVRQRERGRIYq8x2bGjBlISEgAAAghMHv2bNjY2BRY7tatW8W6cB4RERGRoYq8x6ZHjx4QQkAIoWvLf5z/T6lUwsPD47mHloiIiIhKS5H32Hh5ecHLywsAMHToUMyePfuZ93giIiIikoJBk4e3bNlS0nUQERERFZtBwSYrKwvr1q3DqVOnkJmZCa1W/yqICoUCJ06cKJECiYiIiIrKoGCzYMEC7NmzB56enmjUqBGUSt5tlIiIiKRnULD57rvv8OGHH2LcuHElXQ8RERGRwQza1ZKbmws3N7eSroWIiIioWAwKNu3bt8eZM2dKuhYiIiKiYjHoUFSvXr0wa9YsPHz4EO7u7rC0tCywTN++fYtbGxEREdFLMSjYTJkyBQAQHh6O8PDwAv0KhYLBhoiIiMqcQcHm5MmTJV0HERERUbEZFGxq1KhR0nUQERHRC6hUxnt5Fa1WQKsVL16wlBkUbFavXv3CZSZOnGjIqomIiOg/zO3todUK2NkVnNNqLDQaLVJTMyQPNyUebGxsbFClShUGGyIiohJiYmUFpVKBA7sikPzgb6nLKaBSZVv0GdgKSqWifAaba9euFWjLyMhAZGQkZs+ejc8++6zYhREREZG+5Ad/4/69VKnLMGoldrDOysoKHTt2xIQJE7BkyZKSWi0RERFRkZX4LKTq1avjxo0bJb1aIiIiohcy6FBUYYQQSExMRHBwMM+aIiIiIkkYFGwaNmwIhUJRaJ8QgoeiiIiISBIGBZsJEyYUGmxsbGzQuXNn1KlTp7h1EREREb00g4LNpEmTSroOIiIiomIzeI7Nw4cPsXHjRvz8889IS0uDg4MDWrRogREjRsDR0bEkayQiIiIqEoPOikpMTIS3tzc2bdoEc3NzuLi4wMTEBCEhIejbty/u379f0nUSERERvZBBe2yWLl0KExMTHD16FLVq1dK1x8fHY9SoUVixYgUWLVpUYkUSERERFYVBe2zOnj2LyZMn64UaAKhVqxYmTJiAM2fOlEhxRERERC/DoGCj0Wjg4OBQaF/FihWRnp5erKKIiIiIDGFQsGnQoAEOHTpUaN+BAwegVquLVRQRERGRIQwKNn5+fjh06BDGjRuH8PBwnDt3DuHh4Rg7diyOHj0KX19fg4qJjY1F06ZNsW/fPl1bdHQ0hgwZAg8PD3h5eWHz5s0GrZuIiIjkz6DJw+3atcOiRYuwbNkyvfk0lStXxueff45u3bq99Dpzc3Mxbdo0ZGRk6NoePXqEkSNHwsvLC3PmzMGVK1cwZ84cWFtbw8fHx5DSiYiISMYMvo5NUlISXFxcMGPGDDx+/BjXrl1DYGCgwfNrAgMDYWNjo9e2a9cumJqaYu7cuTAxMYGzszPi4uKwYcMGBhsiIiIqwKBDURs3bsTKlStRp04dODs7o1mzZnjjjTfQu3dvLFq0CLt3736p9V28eBE7d+4scIp4ZGQkPD09YWLyT/5q3bo1bt26heTkZENKJyIiIhkzKNiEhYVhypQp+OSTT3Rt1apVw6effoqJEyciNDS0yOtKS0vD9OnT8emnn6JatWp6fYmJiXByctJrq1KlCgAgISHBkNKJiIhIxgw6FHX//n00adKk0D53d3esW7euyOuaPXs2mjZtirfffrtAX1ZWFszMzPTazM3NAQDZ2dkvUXFBJiYGZboiUalKb91E5V153z7Ke/1EpckYtg+Dgk2NGjVw/vx5tGnTpkDfxYsXC+xleZbw8HBERkY+89RxCwsL5OTk6LXlBxorK6uXrPofSqUCDg7WBj+fiAxnZ2cpdQlEVEqMYfs2KNgMHDgQS5cuRW5uLrp27QpHR0c8fPgQp06dQkhICD766KMirWfv3r1ISUlB586d9dpnzZqFo0ePwsnJCUlJSXp9+Y+rVq1qSOkAAK1WIC0t48ULGkilUhrFh0tkjNLSMqHRaKUuw2DcvomerTS3bzs7yyLtETIo2IwYMQL379/Hli1b9ObTqFQqDB8+HCNHjizSepYtW4asrCy9tu7du2Py5Mn4v//7Pxw4cABhYWHQaDRQqVQAgAsXLqBu3brFvoN4Xl75/WIlKs80Gi23PyKZMobt2+DTvWfMmAE/Pz9cuXIFqampsLOzg5ub2zNvtVCYZ+11cXR0RNWqVeHj44Pg4GAEBARgzJgx+PXXXxEaGoo5c+YYWjYRERHJmMHBBgBsbW3RoUOHkqqlAEdHRwQHB2PBggXw9vZG5cqVMX36dHh7e5famERERFR+FSvYlIY///xT77Gbmxt27twpUTVERERUnkh/XhYRERFRCWGwISIiItlgsCEiIiLZYLAhIiIi2WCwISIiItlgsCEiIiLZYLAhIiIi2WCwISIiItlgsCEiIiLZYLAhIiIi2WCwISIiItlgsCEiIiLZYLAhIiIi2WCwISIiItlgsCEiIiLZYLAhIiIi2WCwISIiItlgsCEiIiLZYLAhIiIi2WCwISIiItlgsCEiIiLZYLAhIiIi2WCwISIiItlgsCEiIiLZYLAhIiIi2WCwISIiItlgsCEiIiLZYLAhIiIi2WCwISIiItlgsCEiIiLZkDzYpKSk4OOPP0br1q3RtGlTjBs3Djdu3ND1R0dHY8iQIfDw8ICXlxc2b94sYbVERERkzCQPNhMmTEBcXBw2bNiAPXv2wMLCAiNGjEBmZiYePXqEkSNHonbt2ti7dy8mTJiAZcuWYe/evVKXTUREREbIRMrBHz9+jBo1asDX1xdqtRoA4Ofnhz59+uCvv/7C+fPnYWpqirlz58LExATOzs66EOTj4yNl6URERGSEJN1jY29vjy+++EIXah4+fIjQ0FA4OTmhfv36iIyMhKenJ0xM/slfrVu3xq1bt5CcnCxV2URERGSkJN1j82+fffYZdu3aBTMzM6xbtw5WVlZITEzUhZ58VapUAQAkJCSgUqVKUpRKRERERspogs3w4cMxaNAgbNu2DRMmTMD27duRlZUFMzMzveXMzc0BANnZ2cUaz8Sk9HZWqVSST10iMlrlffso7/UTlSZj2D6MJtjUr18fALBgwQJcvXoVW7duhYWFBXJycvSWyw80VlZWBo+lVCrg4GBteLFEZDA7O0upSyCiUmIM27ekwebhw4c4f/48evTooZtHo1QqUb9+fSQlJcHJyQlJSUl6z8l/XLVqVYPH1WoF0tIyDC/8BVQqpVF8uETGKC0tExqNVuoyDMbtm+jZSnP7trOzLNIeIUmDTXJyMqZOnYrg4GB06NABAJCbm4uoqCh4eXmhUqVKCAsLg0ajgUqlAgBcuHABdevWhaOjY7HGzssrv1+sROWZRqPl9kckU8awfUt6MEytVqNjx46YP38+Ll68iJiYGPj7+yMtLQ0jRoyAj48P0tPTERAQgOvXr2Pfvn0IDQ2Fr6+vlGUTERGRkZJ8ls/y5cvRpk0bfPjhhxgwYABSU1Oxbds2VK9eHY6OjggODkZsbCy8vb2xevVqTJ8+Hd7e3lKXTUREREZI8snDtra2mD17NmbPnl1ov5ubG3bu3Fm2RREREVG5JPkeGyIiIqKSwmBDREREssFgQ0RERLLBYENERESywWBDREREssFgQ0RERLLBYENERESywWBDREREssFgQ0RERLLBYENERESywWBDREREssFgQ0RERLLBYENERESywWBDREREssFgQ0RERLLBYENERESywWBDREREssFgQ0RERLLBYENERESywWBDREREssFgQ0RERLLBYENERESywWBDREREssFgQ0RERLLBYENERESywWBDREREssFgQ0RERLLBYENERESywWBDREREssFgQ0RERLIhebBJTU3F//73P3Ts2BHNmjXD4MGDERkZqes/f/48+vXrB3d3d/Ts2RNHjhyRsFoiIiIyZpIHm6lTp+Ly5ctYvnw59u7di0aNGmH06NG4efMmbty4AV9fX3To0AH79u3DgAEDMH36dJw/f17qsomIiMgImUg5eFxcHM6dO4ft27ejefPmAIDPPvsMP/74Iw4dOoSUlBQ0aNAAH374IQDA2dkZUVFRCA4ORps2baQsnYiIiIyQpHtsHBwcsGHDBjRp0kTXplAooFAokJaWhsjIyAIBpnXr1rh06RKEEGVdLhERERk5SffY2NnZoVOnTnpt3377LeLi4vDJJ59g//79cHJy0uuvUqUKMjMz8ejRI1SsWNHgsU1MSi/TqVSSH+EjMlrlffso7/UTlSZj2D4kDTb/9csvv2DmzJno3r07OnfujKysLJiZmektk/84JyfH4HGUSgUcHKyLVSsRGcbOzlLqEoiolBjD9m00webEiROYNm0amjVrhmXLlgEAzM3NCwSY/MeWloa/eVqtQFpahuHFvoBKpTSKD5fIGKWlZUKj0UpdhsG4fRM9W2lu33Z2lkXaI2QUwWbr1q1YsGABevbsicWLF+v2ylSrVg1JSUl6yyYlJcHKygq2trbFGjMvr/x+sRKVZxqNltsfkUwZw/Yt+cGw7du3Y968eXjvvfewfPlyvUNPLVq0wM8//6y3/IULF9CsWTMolZKXTkREREZG0j02sbGxWLhwIbp16wZfX18kJyfr+iwsLDB06FB4e3tj2bJl8Pb2xunTp/HNN98gODhYwqqJiIjIWEkabL799lvk5ubi+PHjOH78uF6ft7c3Fi1ahLVr12Lp0qXYtGkTatasiaVLl/IaNkRERFQoSYPN+PHjMX78+Ocu07FjR3Ts2LGMKiIiIqLyjBNViIiISDYYbIiIiEg2GGyIiIhINhhsiIiISDYYbIiIiEg2GGyIiIhINhhsiIiISDYYbIiIiEg2GGyIiIhINhhsiIiISDYYbIiIiEg2GGyIiIhINhhsiIiISDYYbIiIiEg2GGyIiIhINhhsiIiISDYYbIiIiEg2GGyIiIhINhhsiIiISDYYbIiIiEg2GGyIiIhINhhsiIiISDYYbIiIiEg2GGyIiIhINhhsiIiISDYYbIiIiEg2GGyIiIhINhhsiIiISDYYbIiIiEg2GGyIiIhINowq2Kxfvx5Dhw7Va4uOjsaQIUPg4eEBLy8vbN68WaLqiIiIyNgZTbDZtm0bVq5cqdf26NEjjBw5ErVr18bevXsxYcIELFu2DHv37pWmSCIiIjJqJlIXcP/+fcyaNQsRERGoU6eOXt+uXbtgamqKuXPnwsTEBM7OzoiLi8OGDRvg4+MjTcFERERktCTfY/PHH3/A1NQUBw8ehLu7u15fZGQkPD09YWLyT/5q3bo1bt26heTk5LIulYiIiIyc5HtsvLy84OXlVWhfYmIi1Gq1XluVKlUAAAkJCahUqZLB45qYlF6mU6kkz4tERqu8bx/lvX6i0mQM24fkweZ5srKyYGZmptdmbm4OAMjOzjZ4vUqlAg4O1sWqjYgMY2dnKXUJRFRKjGH7NupgY2FhgZycHL22/EBjZWVl8Hq1WoG0tIxi1fY8KpXSKD5cImOUlpYJjUYrdRkG4/ZN9GyluX3b2VkWaY+QUQcbJycnJCUl6bXlP65atWqx1p2XV36/WInKM41Gy+2PSKaMYfuW/mDYc7Rs2RKXLl2CRqPRtV24cAF169aFo6OjhJURERGRMTLqYOPj44P09HQEBATg+vXr2LdvH0JDQ+Hr6yt1aURERGSEjDrYODo6Ijg4GLGxsfD29sbq1asxffp0eHt7S10aERERGSGjmmOzaNGiAm1ubm7YuXOnBNUQERFReWPUe2yIiIiIXgaDDREREckGgw0RERHJBoMNERERyQaDDREREckGgw0RERHJBoMNERERyQaDDREREckGgw0RERHJBoMNERERyQaDDREREckGgw0RERHJBoMNERERyQaDDREREckGgw0RERHJBoMNERERyQaDDREREckGgw0RERHJBoMNERERyQaDDREREckGgw0RERHJBoMNERERyQaDDREREckGgw0RERHJBoMNERERyQaDDREREckGgw0RERHJBoMNERERyQaDDREREckGgw0RERHJRrkINlqtFl9++SU6dOgADw8PjB07FvHx8VKXRUREREamXASbtWvXYvv27Zg3bx7CwsKg1WoxZswY5OTkSF0aERERGRGjDzY5OTnYuHEjJk+ejM6dO6Nhw4ZYsWIFEhMT8d1330ldHhERERkRow82165dw5MnT9CmTRtdm52dHVxcXHDx4kUJKyMiIiJjoxBCCKmLeJ7vvvsOkyZNwtWrV2FhYaFr/+CDD5CVlYX169e/9DqFENBqS+9lKxSAUqnE4/QsaDTaUhvHEGamKthYmSP3SRqEViN1OQUoTcxgYmmNtMy/kWeE9ZmbmMLa3BrZaWkQecZVn9LMDGY21nhihD93AKBSKWFtYwGtVgvj/tZ5Pm7fhuP2bThu34BSqYBCoXjhcialM3zJyczMBACYmZnptZubm+Px48cGrVOhUEClevGbU1z2NhYvXkgiptZ2UpfwXHaWtlKX8Fzmdsb7/lkb8c8d8DQUyAG3b8Nx+zYct+8i1CB1AS+Sv5fmvxOFs7OzYWlpKUVJREREZKSMPthUq1YNAJCUlKTXnpSUhKpVq0pREhERERkpow82DRs2hI2NDSIiInRtaWlpiIqKQsuWLSWsjIiIiIyN0c+xMTMzw5AhQ7Bs2TJUrFgRNWrUwNKlS+Hk5ITu3btLXR4REREZEaMPNgAwefJk5OXl4dNPP0VWVhZatmyJr7/+GqamplKXRkREREbE6E/3JiIiIioqo59jQ0RERFRUDDZEREQkGww2REREJBsMNkRERCQbDDZEREQkGww2REREJBsMNkRERCQb5eICfVQ60tPT0a5dO1hbW+P06dN6Fzz08vKCt7c3Jk2aJEltQgiEh4ejY8eOcHR0fOZyGRkZCAkJwbFjx3Dnzh3Y2NjA3d0dfn5+cHV1LcOKgVOnTqFWrVqoX79+ia3z0qVLEEKgRYsWuHPnDrp06YLNmzejVatWJTYGGZeDBw9i69atiImJgUKhQL169TBgwAC88847Upf2Qv7+/rh79y62bNlSoG/o0KH4+eefn/ncGjVqwNvbG/v378f3339fmmW+FC8vL9y9e1f3WKFQwMrKCi4uLvjggw9K/NY+/x3P1NQUlSpVQqdOnfDBBx+gYsWKJTqeHDHYvMKOHDkCR0dHPHjwAMePH0evXr2kLknn4sWL8Pf3x8mTJ5+5zMOHD/Hee+/BxMQEkyZNQqNGjfD48WOEhobi3XffxYYNG8osANy9exfjx4/H5s2bSzTYvPvuu/j888/RokWLElsnGa89e/ZgwYIFCAgIQPPmzSGEwLlz5zB//nwkJydj4sSJUpdosMDAQOTm5gIAEhISMGDAAAQGBqJp06YAAJVKBXNzc7z33ntSllmoUaNGYdSoUQCe/tGVmpqK5cuXY8yYMTh27BiqV69eauNlZWUhJiYGS5cuxZAhQ7Bz507Y2tqW6Hhyw2DzCtu7dy86dOiAe/fuISwszKiCTVEuiD1nzhxkZ2dj586dsLOz07UvW7YMI0aMwOzZs3HkyBEolaV/xJUX8KaSsH37dvj4+KB///66tnr16uH+/fvYvHlzuQ42FSpU0P1/dnY2AMDe3h6VK1fWW87a2rosyyoSKysrvTqrVKmCOXPmoGPHjjh+/DiGDx9equPVqlULjRo1Qu/evREcHIwPP/ywRMeTG86xeUXduHEDV69eRbt27dC9e3dEREQgNja20GX37duHbt26ISwsDJ07d4a7uzsmT56M+/fvY9q0aWjatCk6duyIPXv26J6j0WgQGhqKHj16oEmTJujRowd27Niht96vv/4aXbt2RePGjeHl5YU1a9ZACIGIiAgMGzYMANClSxfs27evQE3Jyck4fvw4hg0bphdqgKe7iufOnYuVK1dCoVAAePoX4rRp09CuXTt4eHhg9OjRuHbtmu45/v7+8Pf3x+LFi9GmTRu4u7vD19cX9+/f1y0THh6O3r17o0mTJujQoQMWLFiAnJwc3SEiABg2bBgCAwMBACdOnMCAAQPg4eGBJk2aoF+/fvjxxx916xs6dCiWLVuGTz75BC1atECzZs3w0UcfIT09HQDQoEEDAMDMmTPh7++v9xqvXbuGBg0a4OLFi3rtU6dOxeTJkwv9HMn4KZVKXL58GY8fP9ZrHzduHHbu3Kl7nJOTg6VLl6JDhw5o2rQpBg4ciLNnzwIAnjx5gqZNm2L79u1661i9ejU6d+4MrVYLIQSCgoLQpUsXuLu7o0+fPjh48KBu2YiICLi4uOj2evbr1w9arRaRkZEYNmwYmjVrhsaNG+PNN9/EgQMHSuz1BwYGwsvLCwBw584dNGjQAEeOHEHfvn1129CNGzewZs0atG3bFp6enpgzZ47eHxanTp1Cv3794Obmhm7dumHlypXIycnR9Z8+fRr9+vWDu7s72rRpA39//wLvd1GYmDzdL2BmZgbg+Z8J8M/36Pz589G8eXP4+fm91HjVq1dHt27dcOTIEV1bTEwMfH190bJlSzRu3BhdunTBxo0bATzdo924cWOEh4frreeLL76Aj4/PS7/eckXQK2nRokXCw8NDZGZmikePHglXV1excOFCXf8bb7whvvzySyGEEHv37hUuLi5i+PDh4s8//xQ//PCDcHFxES1bthQbN24UN27cEAEBAcLV1VU8fPhQCCHE/PnzRcuWLcXBgwdFbGys2LRpk3B1dRUhISFCCCFOnjwpWrZsKc6ePSvu3r0rjhw5IlxdXUV4eLjIzs4W3377rVCr1eLq1asiMzOzQP1nzpzR9b/I33//LTp16iSGDBkirl69KqKjo4Wfn59o3ry5uHPnjhBCiBkzZghXV1fh7+8vrl+/LiIiIkS7du2Ev7+/EEKI6Oho4erqKo4dOybu3r0rzpw5I1q2bCnWrFkj8vLyxNWrV4VarRbffvutSE9PF7/99pto2LChCAkJEbdv3xZRUVFi9OjRonXr1iI7O1sIIcSQIUOEq6ur+OKLL0RsbKw4ceKEcHd3F4GBgUIIIZKSkoRarRahoaEiLS1NxMfHC7VaLS5cuCCEEKJv377ik08+0b3OtLQ00aRJE/HDDz+81M8CGY9jx46Jhg0bCjc3NzF27Fixfv16cfXqVaHVavWWmzp1qujTp4+4cOGCiI2NFRs3bhSurq7i1KlTQggh/P39xeDBg/We0717d7F8+XIhhBBffPGFeOONN8SpU6dEXFyc2LNnj2jatKnYunWrEEKICxcuCLVaLd59910RGxsroqKiRGJionBzcxNLliwRt27dEn/99ZeYPn26cHV1FQ8ePBBCPN2OhgwZ8sLX+d+f5XxffvmleOONN/SW6dKli4iIiBDR0dGiS5cuomXLlmLatGni+vXrYvv27UKtVouTJ08KIYQ4ffq0cHNzEzt27BBxcXHixx9/FN27dxeTJ08WQgiRkpIiGjduLLZu3Sru3LkjIiMjhZeXl9529F///i7Ml5iYKCZPniw8PDzE3bt3i/SZ7N27V6jVajFp0iRx+/ZtERMTU+Tx8gUFBQm1Wi3S09NFRkaGaNeunZg+fbq4fv26iI2NFUuWLBFqtVpERUUJIYSYMGGCGD58uO75Go1GdOzYUfc5yxWDzSsoNzdXtG3bVkydOlXX5uvrKzw9PUVWVpYQomCwUavV4vr167rl+/XrJwYNGqR7fP36daFWq8Uvv/wi/v77b+Hq6iq2bNmiN+6CBQtEmzZthFarFSEhIaJdu3YiNjZW13/x4kXdl0T+F2t8fHyhr+HQoUNCrVaLW7duvfD1btu2Tbi5uYmUlBRdW2ZmpmjXrp1YvHixEOLpF3Lr1q1FTk6OXr3du3cXQghx/Phx0bhxY/Hrr7/q+n/99Vdx8+ZNIUTBL+qoqCixbds2vTpOnz4t1Gq1uHfvnhDiabDp06eP3jJ+fn5i1KhRusdqtVrs3bu30DG2bNkimjdvrvvMdu7cKdq1ayfy8vJe+J6Q8bp8+bL48MMPhaenp1Cr1UKtVovu3buLyMhIIYQQt27d0vvllW/69Om6UBERESEaNGigC+75wfvWrVviyZMnokmTJuL48eN6z1+1apUuVORvfydOnND1x8XFiaCgIL2Qlb/dX7x4UQhROsHm39vRokWLhKurq8jIyNC1tWnTRnz11VdCCCEGDx4s5s+fr7fO8+fP675LoqKihFqtFt9//72uPyYmRkRHRz+z1jfeeEO4uroKDw8P4eHhIRo3bizUarV48803dX9EFOUzyf8efd5Y+eM9K9js3LlTqNVqkZiYKFJSUsT69etFenq6rj8rK0uo1Wqxf/9+IYQQ33//vWjYsKFITEwUQghx9uxZ0bhxY5GamvrcGso7zrF5BZ0+fRrJycno3bu3rq137944deoUjh07hr59+xb6vNq1a+v+38rKCtWqVdM9Njc3B/B0d+zNmzeRm5uL5s2b6z3f09MTmzZtQkpKCv7v//4Pe/fuRY8ePVC/fn20bdsWPXr0KPIkvPwzA1JTU/Haa689d9mYmBjUqVNH72wCCwsLuLm5ISYmRu/1/fvMMFtbW91kx/zdy/3790fNmjXRrl07dOnSBY0bNy50zEaNGsHe3h4bNmzAzZs3ERcXpzv0pdFodMvVq1dP73m2trZIS0sryluAt99+G4sXL8bJkyfRq1cv7N+/H3369IFKpSrS88k4eXh4wMPDA1qtFteuXcPp06exdetWjB07FsePH0dUVBSApxPL/y03N1d3WLZly5aoWbMmDh8+DF9fXxw8eBDNmjXDa6+9hl9//RXZ2dn46KOP9Oaf5eXlIScnB1lZWbq2OnXq6P6/du3a6NevHzZv3oyYmBjcvn270J/pkvbv7dvKygqVKlWCpaWlrs3CwkJ3qCkqKgq//vqr3mFx8f8PU924cQOdOnXCW2+9hfHjx6Ny5cpo164dOnfujG7duj23hnfeeQdDhw4F8PRwYYUKFfQm8BblM8n37/f0Zf39998AABsbG1hbW+Pdd9/F4cOHERUVpfd5aLVaANCdVXrgwAGMGzcO+/fvR5cuXWBvb29wDeUBg80rKH/OSmETEcPCwp4ZbP79Sx/AMyflimdMpM3f2ExMTFChQgUcOHAAly9fxrlz53D27Fls3rwZkyZNKtIEycaNG8PU1BS//PIL3N3dC/SfP38emzZtwrx5855bT/5xcuCfY+WFMTc3x+bNmxEVFYWzZ8/i7NmzGD9+PPr27YvPP/+8wPI///wzRo8ejc6dO6N58+Z4++23kZmZiQkTJugt97wxX8Te3h5du3bFwYMH0aRJE1y+fBnz5883eH0krcTERKxfvx6+vr5wcnKCUqmEi4sLXFxc0LVrV7z11lu4ePGi7ud527ZtBSba5m+TCoUCffv2xaFDh3Rn7kyZMgXAP9vnypUrCwRrQP9nMv8PFgC4fv063n33Xbi6uqJt27bo3r07HBwcMGDAgBJ9H/7r39so8OzvHeDpNj1mzBh4e3sX6MufjPvFF19gwoQJOHPmDH766Sd8/PHHaN68OTZt2vTM9drb2z/3D6iifCb5LCwsnrmeF/njjz9Qp04dWFtb48GDBxg0aBAqVqwILy8vtG/fHk2aNEGnTp10y6tUKt3PwZAhQ3DixAmsWrXK4PHLC04efsWkpKToJs+Fh4fr/fPx8cHly5f19mIYwtnZGaamprh06ZJee2RkJCpXrgx7e3scPHgQO3bsQPPmzTF58mTs2rULAwYMwNGjRwFAN+n3Wezs7NCjRw9s3rxZN9k2n1arxVdffYXY2FhUqlQJDRo0wK1bt5CSkqJbJjs7G7///nuRT80+ffo0Vq9eDRcXF4wbNw6bN2/G5MmTn1nvxo0b0apVKwQGBmLEiBFo164dEhISAJTsGVQ+Pj44d+4cwsPD4ebmBmdn5xJbN5UtMzMz7N69W28Sb778v/orVaqE119/HQDw4MEDvPbaa7p/+/bt05to7+3tjevXryMsLAxPnjzBm2++CeDpXkITExPcu3dP7/mnT5/G119//czgEBYWBkdHR4SEhGDs2LHo1KkTkpOTARjPWYGvv/46YmNj9V5XYmIilixZgidPnuDq1atYuHAh6tWrhxEjRmDDhg1YuHAhLly4oPf9YMi4wIs/k+JITEzEyZMn8fbbbwMADh8+jNTUVOzYsQN+fn7o1q2bbhL0vz8PHx8fxMTEYMuWLbC1tUX79u1LpB5jxmDzijl48CDy8vIwduxYqNVqvX/jx4+HUqlEWFhYscawsbHBoEGD8OWXX+Lw4cOIi4vDtm3bsH37dowaNQoKhQLZ2dlYvHgxwsPDcefOHURGRuLixYu6a1pYWVkBeHr2z5MnTwodZ8aMGVCpVBg8eDCOHz+O+Ph4REZGws/PD1euXMHChQuhUCjw9ttvo0KFCpgyZQp+/fVXXLt2DdOmTUNGRgYGDRpUpNdkamqKNWvWIDQ0FPHx8fj999/xww8/FKg3JiYGf//9N6pVq4Y///wTkZGRuHPnDvbu3av7S+nfZ2i8iJWVFW7cuIFHjx4V2t+2bVtUqlQJwcHBhf6VSuVHxYoVMWbMGKxatQorVqxAdHQ04uPjcerUKUycOBGtWrVCixYt8Prrr+ONN97ArFmz8P333yM+Ph5BQUFYv3693uHiGjVqoFWrVvjiiy/QtWtX2NjYAHh6uPOdd97BqlWrcODAAcTHx2PPnj1YunQpqlSp8sz6nJyckJiYiNOnT+Pu3bv47rvvMHv2bAAv9zNdmsaOHYtvv/0Wq1evRmxsLM6fP4+ZM2fi77//RuXKlWFjY4Pt27dj6dKliIuLQ0xMDI4ePYo6derAwcHB4HGL+pkUVUZGBh48eIAHDx4gPj4eJ06cwJgxY1CzZk2MHDkSwNPPIzMzE9988w3u3buHs2fPYurUqQD0P4+6deuiWbNmWLt27StzqJqHol4x+/btQ9u2bQvdBV27dm3doY3/7v59WTNnzoSDgwOWLVuG5ORk1KlTB//73/8wcOBAAMCAAQOQmpqKtWvXIiEhAfb29ujRowemTZsGAFCr1ejUqROmTJmCqVOn6i5W9W9VqlTBrl27sGHDBixduhSJiYmws7NDs2bNsHPnTjRs2BDA0y/yrVu3YtGiRRgxYgQAoHnz5tixYwdq1apVpNfTtm1bLFiwABs3bsSKFStgYWGBTp066U7DdnBwgI+PD5YsWYK4uDhMnjwZycnJGD9+PACgfv36WLhwIT7++GP89ttvRd6zMmrUKAQHB+PGjRv49NNPC/QrlUr83//9H0JCQvTmTFH5NGXKFNSpUwe7du3Ctm3bkJWVherVq+PNN9+Er6+vbrkVK1ZgxYoV+N///ofHjx+jdu3aWLBgQYFw269fP1y4cAH9+vXTa8/fPletWoWkpCRUq1YNkydPxpgxY55Z27Bhw3Dz5k1Mnz4dOTk5qFOnDqZOnYovv/wSv/32Gzp27Fiyb4YBevbsiRUrVmD9+vX46quvUKFCBXh5eem+V5ydnREYGIjVq1dj+/btUCqVaN26NYKCgop9vauifiZFsXHjRt1p26ampqhWrRp69eqFUaNG6Q519ezZE3/88QcWLVqE9PR01KhRAwMGDMDJkyfx22+/YfDgwbr19evXD7/88ssr88ePQhjLPkQiMoi/vz/y8vKwbNkyqUshIiMUGBiIn376qcC1xOSKe2yIyqlz587h+vXrOHLkCLZt2yZ1OURkZC5duoTY2Fhs3rwZc+fOlbqcMsNgQ1RO7d27Fz/88AMmTZoENzc3qcshIiNz6tQpbN26FT4+PrrJ468CHooiIiIi2eBZUURERCQbDDZEREQkGww2REREJBsMNkRERCQbPCuK6BXg7++P/fv3P3cZT09PbNmypVjj3LlzB126dMHnn39e4KJwL9KgQYNn9llZWeHy5cvFqo2IXg0MNkSvAD8/P7zzzju6x2vXrkVUVBRWr16ta8u/5L6U+vfvX+hNFYt7VVgienUw2BC9AmrXrq13z5qKFSvCzMwMHh4e0hVVCCcnJ6OriYjKF/4ZREQ6kZGRGDJkCNzd3eHp6YkZM2bg4cOHesvcvHkTEydOhKenJ1q2bAlfX1/cuHFDb5kHDx5g8uTJaNq0KTw9PfHZZ58982amL+POnTto0KABQkJC0LNnT7i7u2Pv3r0Ant6A1NfXF82aNUOzZs0wYcIExMfHF3i+n58fmjVrhnbt2mHdunUICAjA0KFDdcs0aNAAgYGBes8LDAwscKjsRe/Vvn374OLigqtXr2LQoEFo0qQJ3njjDXz99dd660lPT8e8efPQoUMHeHh4wMfHBz/88AMAYPHixXBzc8Pff/+t95y1a9eiefPmyMzMNOyNJJIxBhsiAgBcvHgRI0aMgIWFBVauXIlPPvkEP//8M4YNG4asrCwAwP379zFo0CDcunULs2fPxtKlS5GcnIzhw4cjNTVVt65Vq1ahWrVqWLt2LYYPH45du3bpHfZ6Fq1Wi7y8vAL//iswMBBjx47FkiVL0K5dO8TGxuKdd95BSkoKFi9ejAULFiA+Ph6DBw9GSkoKAODJkycYOnQoYmJiMH/+fAQEBCA8PBzHjx8vlfcq//VMmTIFvXr1woYNG9CsWTMsWbIEP/74IwBAo9Fg1KhROHToEHx9fbF27VrUq1cPEyZMQGRkJPr374/s7Gx88803euMfOHAAvXr1gqWl5UvXTiR3PBRFRACAL774AnXr1sX69euhUqkAAO7u7ujduzf27t2L9957D6GhocjJyUFISAgqV64MAGjYsCEGDx6Mq1ev6u5a3qNHD8ycORMA0KZNG5w7dw4XLlx4YQ1r167F2rVrC7SfPXtWNx4AvPnmm/Dx8dE9/uijj2BpaYnQ0FDdXKE2bdqga9euCA4OxowZM7B//34kJCTgwIEDur0vbm5u6NmzZ6m8VwAghICfn59u3lDz5s1x/Phx/PDDD+jQoQPOnDmDq1evYs2aNejatSsAoHXr1oiPj8eFCxcwceJENG3aFAcOHNCt45dffsGtW7ewaNGil66b6FXAYENEyMzMxNWrVzF69GgIIXR7SWrVqgVnZ2ecO3cO7733Hi5dugQPDw+9kOHk5IRTp04BeHqoBwBatGiht/6aNWvi0qVLL6xj4MCBGDhwYIF2BwcHvceNGjXSe3zhwgV4enrCwsJCV7uNjQ1atGiBn376CcDTQ0e1atXSO6RUs2ZNNG3a9IV1/VtR36t8/16/mZkZKlasiIyMDABPb1JoamoKLy8v3TJKpRJhYWG6xz4+Pvjss89w9+5d1KhRA/v370fdunVfum6iVwWDDREhLS0NWq0WQUFBCAoKKtBvbm4OAEhNTUXNmjVfuL7/HiJRKpUoym3pqlSpgiZNmrxwOSsrK73HqampOHr0KI4ePVpg2YoVKwIAHj9+rPv/f6tatSru37//wjHzFfW9ymdhYaH3+N/vRWpqKipUqPDcs7569eqFhQsX4sCBAxg9ejSOHTuGcePGFbleolcNgw0RwdraGgqFAiNGjEDv3r0L9OcHFVtb2wKTiQHg/PnzqFmzJhQKRanXWhhbW1u0bdsWI0eOLNBnYvL0a87BwQG3b98u0P/vuUH5NBqN3uP8PSxA0d+rotadmpoKIYTeexcVFQUhBFxdXWFtbY2ePXvi2LFjUKvVyMjIQJ8+fYo8BtGrhpOHiQg2NjZwcXHBzZs30aRJE92/119/HYGBgYiIiADw9BDT1atX9cJNSkoKxowZg9OnT0tVPjw9PXH9+nU0atRIV3vjxo0RGhqqmxzcpk0b3LlzB7/99pvueampqQUu/GdjY1NgD84vv/yi11+U96ooWrRogdzcXJw5c0bXJoTAzJkzsX79el1b//79ERMTg02bNqFt27aoWrVqkccgetUw2BARAGDq1Kk4e/YsPvroI5w+fRrff/89xowZg/Pnz8PV1RUAMGLECJiZmWHMmDH49ttv8f3332P8+PFwcnLC22+/LVntfn5+uH37Nnx9fXHixAn8+OOPmDRpEo4cOYKGDRsCAPr06QMXFxdMmDAB4eHhOHnyJMaOHVvgNPTOnTvjyJEjCAsLw/nz5/Hxxx8jLi5Ob5mivFdF0blzZzRt2hT+/v7YuXMnfvrpJ/j7++PGjRsYM2aMbrnmzZujbt26+Pnnn1/6is5ErxoeiiIiAED79u3x9ddfY/Xq1Zg8eTJMTU3h6uqKkJAQ3UXzqlWrhu3bt2Pp0qXw9/eHmZkZWrVqhRUrVsDe3r7A9VbKSsOGDbFt2zasWLEC06dPhxACarUaa9asQZcuXQA8nbi7ceNGLF68GPPmzYOJiQkGDBgAMzMzvXXNnDkTeXl5WLx4MUxMTNCrVy989NFH+PTTT3XLFOW9KgqVSoWgoCAsW7YMq1atQmZmJho0aICNGzfCzc1Nb9nOnTvj4cOHurOniKhwClGUGX1ERDKVf3G+4t4nqzQJIdC7d2+0b98en3zyidTlEBk17rEhIjJS6enpCA0NxW+//Yb4+Hi9KyQTUeEYbIiIjJSFhQXCwsKg1WqxcOFC1KpVS+qSiIweD0URERGRbPCsKCIiIpINBhsiIiKSDQYbIiIikg0GGyIiIpINBhsiIiKSDQYbIiIikg0GGyIiIpINBhsiIiKSDQYbIiIiko3/B6jvlCIBhpg9AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=new_df, x=\"Tech Frequency\", hue=\"AGE\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "collectible-puppy",
   "metadata": {},
   "source": [
    "## Second Research Question: [✏️ PUT YOUR QUESTION HERE ✏️]\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"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "juvenile-creation",
   "metadata": {},
   "source": [
    "### Results "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "pursuant-surrey",
   "metadata": {},
   "outputs": [],
   "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",
    "#######################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "located-night",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 💻 YOU CAN ADD NEW CELLS WITH THE \"+\" BUTTON "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "infectious-symbol",
   "metadata": {},
   "source": [
    "# Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "furnished-camping",
   "metadata": {
    "code_folding": []
   },
   "source": [
    "## Considerations"
   ]
  },
  {
   "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",
    "- *What were limitations of your datset?*\n",
    "- *Are there any known biases in the data?*\n",
    "\n",
    "✏️ *Write your answer below:*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "beneficial-invasion",
   "metadata": {},
   "source": [
    "## Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "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:*"
   ]
  }
 ],
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