generated from mwc/project_argument
So far I'm doing great however, I am worried about not doing the chart correctly. I already had a gard time creating the first one from the data section.
501 lines
13 KiB
Plaintext
501 lines
13 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "worldwide-blood",
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"metadata": {},
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"source": [
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"# Impact of Technological apps on Education?"
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]
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},
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{
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"cell_type": "markdown",
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"id": "understanding-numbers",
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"metadata": {},
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"source": [
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"*✏️ This research looks at whether using educational apps like Pear Deck helps students learn better and stay more engaged. It compares test scores and survey results from students who used Pear Deck with those who had regular lessons. The goal is to see if learning with technology improves student performance and interest.*"
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]
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},
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{
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"cell_type": "markdown",
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"id": "greater-circular",
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"metadata": {},
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"source": [
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"## Overarching Question: Do Educational apps like pear deck improve student learning outcomes?"
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]
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},
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{
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"cell_type": "markdown",
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"id": "appreciated-testimony",
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"metadata": {},
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"source": [
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"*✏️I am interested in exploring the question: “Do educational apps like Pear Deck improve student learning outcomes?” I chose this question because educational technology is becoming increasingly common in classrooms, and I have observed its use firsthand during my placement in a 3rd-grade classroom. My students regularly use ELA apps such as Pear Assessment and math apps like Pear Deck to complete their classwork. I am curious about whether regular use of educational apps impacts student performance. Investigating this question can reveal patterns in how technology influences learning and provide evidence-based recommendations for teachers.*"
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]
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},
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{
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"cell_type": "markdown",
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"id": "permanent-pollution",
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"metadata": {},
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"source": [
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"# Data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "c4330642-cdf6-447a-af17-94afb4261a30",
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"metadata": {},
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"outputs": [],
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"source": [
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"#Include any import statements you will need\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "overhead-sigma",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Student</th>\n",
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" <th>Traditional_Studying</th>\n",
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" <th>Pear_Deck_Studying</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>1</td>\n",
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" <td>10</td>\n",
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" <td>14.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>2</td>\n",
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" <td>10</td>\n",
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" <td>8.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>3</td>\n",
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" <td>10</td>\n",
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" <td>10.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>4</td>\n",
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" <td>10</td>\n",
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" <td>15.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>5</td>\n",
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" <td>10</td>\n",
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" <td>14.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>5</th>\n",
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" <td>6</td>\n",
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" <td>10</td>\n",
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" <td>9.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>6</th>\n",
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" <td>7</td>\n",
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" <td>10</td>\n",
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" <td>13.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>7</th>\n",
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" <td>8</td>\n",
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" <td>10</td>\n",
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" <td>15.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>8</th>\n",
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" <td>9</td>\n",
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" <td>10</td>\n",
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" <td>9.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>9</th>\n",
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" <td>10</td>\n",
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" <td>10</td>\n",
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" <td>15.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>10</th>\n",
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" <td>11</td>\n",
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" <td>10</td>\n",
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" <td>15.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>11</th>\n",
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" <td>12</td>\n",
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" <td>10</td>\n",
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" <td>15.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>12</th>\n",
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" <td>13</td>\n",
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" <td>10</td>\n",
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" <td>14.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>13</th>\n",
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" <td>14</td>\n",
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" <td>10</td>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>14</th>\n",
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" <td>15</td>\n",
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" <td>10</td>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" Student Traditional_Studying Pear_Deck_Studying\n",
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"0 1 10 14.0\n",
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"1 2 10 8.0\n",
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"2 3 10 10.0\n",
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"3 4 10 15.0\n",
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"4 5 10 14.0\n",
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"5 6 10 9.0\n",
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"6 7 10 13.0\n",
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"7 8 10 15.0\n",
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"8 9 10 9.0\n",
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"9 10 10 15.0\n",
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"10 11 10 15.0\n",
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"11 12 10 15.0\n",
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"12 13 10 14.0\n",
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"13 14 10 NaN\n",
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"14 15 10 NaN"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"### 💻 FILL IN YOUR DATASET FILE NAME BELOW 💻 ###\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"data = {\n",
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" \"Student\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15],\n",
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" \"Traditional_Studying\": [10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10],\n",
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" \"Pear_Deck_Studying\": [14, 8, 10, 15, 14, 9, 13, 15, 9, 15, 15, 15, 14, None, None]\n",
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"}\n",
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"\n",
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"df = pd.DataFrame(data)\n",
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"df"
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]
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},
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{
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"cell_type": "markdown",
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"id": "continental-franklin",
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"metadata": {},
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"source": [
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"**Data Overview**\n",
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"\n",
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"*✏️ Write 2-3 sentences describing this dataset. Be sure to include where the data comes from and what it contains.*"
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]
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},
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{
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"cell_type": "markdown",
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"id": "infinite-instrument",
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"metadata": {},
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"source": [
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"# Methods and Results"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "basic-canadian",
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"metadata": {},
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"outputs": [],
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"source": [
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"#Import any helper files you need here"
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]
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},
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{
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"cell_type": "markdown",
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"id": "recognized-positive",
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"metadata": {},
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"source": [
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"## First Research Question: [✏️ PUT YOUR QUESTION HERE ✏️]\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "graduate-palmer",
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"metadata": {},
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"source": [
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"### Methods"
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]
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},
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{
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"cell_type": "markdown",
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"id": "endless-variation",
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"metadata": {},
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"source": [
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"*Explain how you will approach this research question below. Consider the following:* \n",
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" - *Which aspects of the dataset will you use?* \n",
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" - *How will you reorganize/store the data?* \n",
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" - *What data science tools/functions will you use and why?* \n",
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" \n",
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"✏️ *Write your answer below:*\n",
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"\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "portuguese-japan",
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"metadata": {},
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"source": [
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"### Results "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"id": "negative-highlight",
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"metadata": {},
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"outputs": [],
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"source": [
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"#######################################################################\n",
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"### 💻 YOUR WORK GOES HERE TO ANSWER THE FIRST RESEARCH QUESTION 💻 \n",
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"### \n",
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"### Your data analysis may include a statistic and/or a data visualization\n",
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"#######################################################################"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"id": "victorian-burning",
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"metadata": {},
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"outputs": [],
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"source": [
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"# 💻 YOU CAN ADD NEW CELLS WITH THE \"+\" BUTTON "
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]
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},
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{
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"cell_type": "markdown",
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"id": "collectible-puppy",
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"metadata": {},
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"source": [
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"## Second Research Question: [✏️ PUT YOUR QUESTION HERE ✏️]\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "demographic-future",
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"metadata": {},
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"source": [
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"### Methods"
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]
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},
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{
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"cell_type": "markdown",
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"id": "incorporate-roller",
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||
"metadata": {},
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"source": [
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"*Explain how you will approach this research question below. Consider the following:* \n",
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" - *Which aspects of the dataset will you use?* \n",
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" - *How will you reorganize/store the data?* \n",
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" - *What data science tools/functions will you use and why?* \n",
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"\n",
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"✏️ *Write your answer below:*\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "juvenile-creation",
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"metadata": {},
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"source": [
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"### Results "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"id": "pursuant-surrey",
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"metadata": {},
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"outputs": [],
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"source": [
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"#######################################################################\n",
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"### 💻 YOUR WORK GOES HERE TO ANSWER THE SECOND RESEARCH QUESTION 💻 \n",
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"###\n",
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"### Your data analysis may include a statistic and/or a data visualization\n",
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"#######################################################################"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"id": "located-night",
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"metadata": {},
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"outputs": [],
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"source": [
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"# 💻 YOU CAN ADD NEW CELLS WITH THE \"+\" BUTTON "
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]
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},
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{
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"cell_type": "markdown",
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"id": "infectious-symbol",
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"metadata": {},
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"source": [
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"# Discussion"
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]
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},
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{
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"cell_type": "markdown",
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"id": "furnished-camping",
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"metadata": {
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"code_folding": []
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},
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"source": [
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"## Considerations"
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]
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},
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{
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"cell_type": "markdown",
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"id": "bearing-stadium",
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"metadata": {},
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"source": [
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"*It's important to recognize the limitations of our research.\n",
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"Consider the following:*\n",
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"\n",
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"- *Do the results give an accurate depiction of your research question? Why or why not?*\n",
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"- *What were limitations of your datset?*\n",
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"- *Are there any known biases in the data?*\n",
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"\n",
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"✏️ *Write your answer below:*"
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]
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},
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{
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"cell_type": "markdown",
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"id": "beneficial-invasion",
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"metadata": {},
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"source": [
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"## Summary"
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]
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},
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{
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"cell_type": "markdown",
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"id": "about-raise",
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"metadata": {},
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"source": [
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"*Summarize what you discovered through the research. Consider the following:*\n",
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"\n",
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"- *What did you learn about your media consumption/digital habits?*\n",
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"- *Did the results make sense?*\n",
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"- *What was most surprising?*\n",
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"- *How will this project impact you going forward?*\n",
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"\n",
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"✏️ *Write your answer below:*"
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]
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||
}
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||
],
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||
"metadata": {
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||
"jupytext": {
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"cell_metadata_json": true,
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"text_representation": {
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"extension": ".Rmd",
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"format_name": "rmarkdown",
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"format_version": "1.2",
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"jupytext_version": "1.9.1"
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}
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},
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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||
"language": "python",
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||
"name": "python3"
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},
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||
"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.3"
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},
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"toc": {
|
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"base_numbering": 1,
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"nav_menu": {},
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"number_sections": false,
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"sideBar": true,
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"skip_h1_title": false,
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"title_cell": "Table of Contents",
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"title_sidebar": "Contents",
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"toc_cell": false,
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"toc_position": {},
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"toc_section_display": true,
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"toc_window_display": false
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},
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"varInspector": {
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"cols": {
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"lenName": 16,
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"lenType": 16,
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"lenVar": 40
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},
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"kernels_config": {
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"python": {
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"delete_cmd_postfix": "",
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"delete_cmd_prefix": "del ",
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"library": "var_list.py",
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"varRefreshCmd": "print(var_dic_list())"
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},
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"r": {
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"delete_cmd_postfix": ") ",
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"delete_cmd_prefix": "rm(",
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"library": "var_list.r",
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"varRefreshCmd": "cat(var_dic_list()) "
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}
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},
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"types_to_exclude": [
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"module",
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"function",
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"builtin_function_or_method",
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"instance",
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"_Feature"
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"window_display": false
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"nbformat": 4,
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"nbformat_minor": 5
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