path: root/CS105MiniProject.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "21abd26c73fd0070",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "<div>\n",
    "    <h1><center>CS105 Mini-Project</center></h1>\n",
    "    <h2><center>Does who a student is living with effect if and how they work jobs?</center></h2>\n",
    "    <p>By: <b>NAMES HERE</b></p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69d8e8ad7c61ba61",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# Data Loading & Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b68b27041fdab1a5",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-24T05:38:10.428394Z",
     "start_time": "2024-02-24T05:38:10.392848Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "    What is your current class standing? What is your age?  \\\n0                                 Senior               23+   \n1                                 Junior                20   \n2                                 Junior               23+   \n3                                 Senior               23+   \n4                               Graduate                22   \n..                                   ...               ...   \n255                               Junior                21   \n256                                  NaN                21   \n257                               Senior                21   \n258                            Sophomore                21   \n259                            Sophomore                18   \n\n    Who do you live with?  \\\n0                 Neither   \n1                    Both   \n2                 Friends   \n3                 Neither   \n4                 Neither   \n..                    ...   \n255               Friends   \n256                Family   \n257                Family   \n258                Family   \n259               Friends   \n\n    Do you currently live in a house, apartment, or dorm?  \\\n0                                                House      \n1                                            Apartment      \n2                                                House      \n3                                            Apartment      \n4                                            Apartment      \n..                                                 ...      \n255                                              House      \n256                                          Apartment      \n257                                              House      \n258                                          Apartment      \n259                                               Dorm      \n\n    How many people live in your household?  \\\n0                                         6   \n1                                         4   \n2                                         4   \n3                                         1   \n4                                         1   \n..                                      ...   \n255                                       5   \n256             North District 4 bed 2 bath   \n257                                       9   \n258                                       4   \n259      3 (room), 8 (hall), ~70 (building)   \n\n    What was your GPA your very first quarter at UCR? Do you currently work?  \\\n0                                                2.73                    Yes   \n1                                                 3.7                     No   \n2                                                3.75                     No   \n3                                                3.81                     No   \n4                                                3.23                    Yes   \n..                                                ...                    ...   \n255                                                 4                    Yes   \n256                                               3.5                     No   \n257                                               3.7                     No   \n258                                                 3                    Yes   \n259                                                 4                     No   \n\n    How many hours do you work per week on average?  \\\n0                                            5 - 10   \n1                                               NaN   \n2                                               NaN   \n3                                               NaN   \n4                                           10 - 20   \n..                                              ...   \n255                                         10 - 20   \n256                                             NaN   \n257                                           1 - 5   \n258                                          5 - 10   \n259                                             NaN   \n\n    Do you work on or off campus?  \\\n0                      Off-campus   \n1                             NaN   \n2                             NaN   \n3                             NaN   \n4                      Off-campus   \n..                            ...   \n255                     On-campus   \n256                           NaN   \n257                    Off-campus   \n258                     On-campus   \n259                           NaN   \n\n    Do you work in a department related to your major?  \\\n0                                                   No   \n1                                                  NaN   \n2                                                  NaN   \n3                                                   No   \n4                                                  Yes   \n..                                                 ...   \n255                                                 No   \n256                                                NaN   \n257                                                 No   \n258                                                 No   \n259                                                NaN   \n\n    Do you have roommates that are part of your major?  \n0                                                   No  \n1                                                  Yes  \n2                                                   No  \n3                                                   No  \n4                                                   No  \n..                                                 ...  \n255                                                 No  \n256                                                 No  \n257                                                 No  \n258                                                 No  \n259                                                Yes  \n\n[260 rows x 11 columns]",
      "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>What is your current class standing?</th>\n      <th>What is your age?</th>\n      <th>Who do you live with?</th>\n      <th>Do you currently live in a house, apartment, or dorm?</th>\n      <th>How many people live in your household?</th>\n      <th>What was your GPA your very first quarter at UCR?</th>\n      <th>Do you currently work?</th>\n      <th>How many hours do you work per week on average?</th>\n      <th>Do you work on or off campus?</th>\n      <th>Do you work in a department related to your major?</th>\n      <th>Do you have roommates that are part of your major?</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Senior</td>\n      <td>23+</td>\n      <td>Neither</td>\n      <td>House</td>\n      <td>6</td>\n      <td>2.73</td>\n      <td>Yes</td>\n      <td>5 - 10</td>\n      <td>Off-campus</td>\n      <td>No</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Junior</td>\n      <td>20</td>\n      <td>Both</td>\n      <td>Apartment</td>\n      <td>4</td>\n      <td>3.7</td>\n      <td>No</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Yes</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Junior</td>\n      <td>23+</td>\n      <td>Friends</td>\n      <td>House</td>\n      <td>4</td>\n      <td>3.75</td>\n      <td>No</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Senior</td>\n      <td>23+</td>\n      <td>Neither</td>\n      <td>Apartment</td>\n      <td>1</td>\n      <td>3.81</td>\n      <td>No</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>No</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Graduate</td>\n      <td>22</td>\n      <td>Neither</td>\n      <td>Apartment</td>\n      <td>1</td>\n      <td>3.23</td>\n      <td>Yes</td>\n      <td>10 - 20</td>\n      <td>Off-campus</td>\n      <td>Yes</td>\n      <td>No</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>255</th>\n      <td>Junior</td>\n      <td>21</td>\n      <td>Friends</td>\n      <td>House</td>\n      <td>5</td>\n      <td>4</td>\n      <td>Yes</td>\n      <td>10 - 20</td>\n      <td>On-campus</td>\n      <td>No</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>256</th>\n      <td>NaN</td>\n      <td>21</td>\n      <td>Family</td>\n      <td>Apartment</td>\n      <td>North District 4 bed 2 bath</td>\n      <td>3.5</td>\n      <td>No</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>257</th>\n      <td>Senior</td>\n      <td>21</td>\n      <td>Family</td>\n      <td>House</td>\n      <td>9</td>\n      <td>3.7</td>\n      <td>No</td>\n      <td>1 - 5</td>\n      <td>Off-campus</td>\n      <td>No</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>258</th>\n      <td>Sophomore</td>\n      <td>21</td>\n      <td>Family</td>\n      <td>Apartment</td>\n      <td>4</td>\n      <td>3</td>\n      <td>Yes</td>\n      <td>5 - 10</td>\n      <td>On-campus</td>\n      <td>No</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>259</th>\n      <td>Sophomore</td>\n      <td>18</td>\n      <td>Friends</td>\n      <td>Dorm</td>\n      <td>3 (room), 8 (hall), ~70 (building)</td>\n      <td>4</td>\n      <td>No</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Yes</td>\n    </tr>\n  </tbody>\n</table>\n<p>260 rows × 11 columns</p>\n</div>"
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load dataframe from data.csv\n",
    "df = pd.read_csv(\"data.csv\")\n",
    "\n",
    "# Select relevant columns\n",
    "df = df.iloc[:, [2, 3, 7, 8, 9, 34, 58, 59, 60, 61, 26]]\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7ee1fc9a8abba2b",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3f72adcb3bc0285e",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-24T05:38:10.445114Z",
     "start_time": "2024-02-24T05:38:10.429806Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "    What is your current class standing? What is your age?  \\\n0                                 Senior               23+   \n1                                 Junior                20   \n2                                 Junior               23+   \n3                                 Senior               23+   \n4                               Graduate                22   \n..                                   ...               ...   \n255                               Junior                21   \n256                                  NaN                21   \n257                               Senior                21   \n258                            Sophomore                21   \n259                            Sophomore                18   \n\n    Who do you live with?  \\\n0                 Neither   \n1                    Both   \n2                 Friends   \n3                 Neither   \n4                 Neither   \n..                    ...   \n255               Friends   \n256                Family   \n257                Family   \n258                Family   \n259               Friends   \n\n    Do you currently live in a house, apartment, or dorm?  \\\n0                                                House      \n1                                            Apartment      \n2                                                House      \n3                                            Apartment      \n4                                            Apartment      \n..                                                 ...      \n255                                              House      \n256                                          Apartment      \n257                                              House      \n258                                          Apartment      \n259                                               Dorm      \n\n     How many people live in your household?  \\\n0                                          6   \n1                                          4   \n2                                          4   \n3                                          1   \n4                                          1   \n..                                       ...   \n255                                        5   \n256                                        4   \n257                                        9   \n258                                        4   \n259                                        3   \n\n     What was your GPA your very first quarter at UCR? Do you currently work?  \\\n0                                                 2.73                    Yes   \n1                                                 3.70                     No   \n2                                                 3.75                     No   \n3                                                 3.81                     No   \n4                                                 3.23                    Yes   \n..                                                 ...                    ...   \n255                                               4.00                    Yes   \n256                                               3.50                     No   \n257                                               3.70                     No   \n258                                               3.00                    Yes   \n259                                               4.00                     No   \n\n    How many hours do you work per week on average?  \\\n0                                            5 - 10   \n1                                                 0   \n2                                                 0   \n3                                                 0   \n4                                           10 - 20   \n..                                              ...   \n255                                         10 - 20   \n256                                               0   \n257                                               0   \n258                                          5 - 10   \n259                                               0   \n\n    Do you work on or off campus?  \\\n0                      Off-campus   \n1                             NaN   \n2                             NaN   \n3                             NaN   \n4                      Off-campus   \n..                            ...   \n255                     On-campus   \n256                           NaN   \n257                    Off-campus   \n258                     On-campus   \n259                           NaN   \n\n    Do you work in a department related to your major?  \\\n0                                                   No   \n1                                                  NaN   \n2                                                  NaN   \n3                                                  NaN   \n4                                                  Yes   \n..                                                 ...   \n255                                                 No   \n256                                                NaN   \n257                                                NaN   \n258                                                 No   \n259                                                NaN   \n\n    Do you have roommates that are part of your major?  \n0                                                   No  \n1                                                  Yes  \n2                                                   No  \n3                                                   No  \n4                                                   No  \n..                                                 ...  \n255                                                 No  \n256                                                 No  \n257                                                 No  \n258                                                 No  \n259                                                Yes  \n\n[260 rows x 11 columns]",
      "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>What is your current class standing?</th>\n      <th>What is your age?</th>\n      <th>Who do you live with?</th>\n      <th>Do you currently live in a house, apartment, or dorm?</th>\n      <th>How many people live in your household?</th>\n      <th>What was your GPA your very first quarter at UCR?</th>\n      <th>Do you currently work?</th>\n      <th>How many hours do you work per week on average?</th>\n      <th>Do you work on or off campus?</th>\n      <th>Do you work in a department related to your major?</th>\n      <th>Do you have roommates that are part of your major?</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Senior</td>\n      <td>23+</td>\n      <td>Neither</td>\n      <td>House</td>\n      <td>6</td>\n      <td>2.73</td>\n      <td>Yes</td>\n      <td>5 - 10</td>\n      <td>Off-campus</td>\n      <td>No</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Junior</td>\n      <td>20</td>\n      <td>Both</td>\n      <td>Apartment</td>\n      <td>4</td>\n      <td>3.70</td>\n      <td>No</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Yes</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Junior</td>\n      <td>23+</td>\n      <td>Friends</td>\n      <td>House</td>\n      <td>4</td>\n      <td>3.75</td>\n      <td>No</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Senior</td>\n      <td>23+</td>\n      <td>Neither</td>\n      <td>Apartment</td>\n      <td>1</td>\n      <td>3.81</td>\n      <td>No</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Graduate</td>\n      <td>22</td>\n      <td>Neither</td>\n      <td>Apartment</td>\n      <td>1</td>\n      <td>3.23</td>\n      <td>Yes</td>\n      <td>10 - 20</td>\n      <td>Off-campus</td>\n      <td>Yes</td>\n      <td>No</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>255</th>\n      <td>Junior</td>\n      <td>21</td>\n      <td>Friends</td>\n      <td>House</td>\n      <td>5</td>\n      <td>4.00</td>\n      <td>Yes</td>\n      <td>10 - 20</td>\n      <td>On-campus</td>\n      <td>No</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>256</th>\n      <td>NaN</td>\n      <td>21</td>\n      <td>Family</td>\n      <td>Apartment</td>\n      <td>4</td>\n      <td>3.50</td>\n      <td>No</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>257</th>\n      <td>Senior</td>\n      <td>21</td>\n      <td>Family</td>\n      <td>House</td>\n      <td>9</td>\n      <td>3.70</td>\n      <td>No</td>\n      <td>0</td>\n      <td>Off-campus</td>\n      <td>NaN</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>258</th>\n      <td>Sophomore</td>\n      <td>21</td>\n      <td>Family</td>\n      <td>Apartment</td>\n      <td>4</td>\n      <td>3.00</td>\n      <td>Yes</td>\n      <td>5 - 10</td>\n      <td>On-campus</td>\n      <td>No</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>259</th>\n      <td>Sophomore</td>\n      <td>18</td>\n      <td>Friends</td>\n      <td>Dorm</td>\n      <td>3</td>\n      <td>4.00</td>\n      <td>No</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Yes</td>\n    </tr>\n  </tbody>\n</table>\n<p>260 rows × 11 columns</p>\n</div>"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fixes empty values\n",
    "df['Do you currently work?'] = df['Do you currently work?'].fillna('No')\n",
    "\n",
    "# Replaces custom text answers with appropriate values\n",
    "df['How many people live in your household?'] = (df['How many people live in your household?']\n",
    "                                                 .fillna(0)\n",
    "                                                 .replace('4 in total', '4')\n",
    "                                                 .replace('4 (Including me)', '4')\n",
    "                                                 .replace('at school 4 including me ', '4')\n",
    "                                                 .replace('3 excluding me', '4')\n",
    "                                                 .replace('5 including me', '5')\n",
    "                                                 .replace('North District 4 bed 2 bath', '4')\n",
    "                                                 .replace('3 (room), 8 (hall), ~70 (building)', '3')\n",
    "                                                 .astype(int))\n",
    "df['Who do you live with?'] = df['Who do you live with?'].replace('Family, Friends', 'Both').replace(\n",
    "    'Family, Friends, Both', 'Both')\n",
    "df['Do you currently live in a house, apartment, or dorm?'] = (\n",
    "    df['Do you currently live in a house, apartment, or dorm?']\n",
    "    .replace('house (renting)', 'House'))\n",
    "\n",
    "df.loc[df['What was your GPA your very first quarter at UCR?'].str.contains(\n",
    "    \"I am not sure|idk|I don't know|This is my first quarter|i don't rem|not sure|I never checked. |I dont know\") == True, 'What was your GPA your very first quarter at UCR?'] = np.nan\n",
    "df['What was your GPA your very first quarter at UCR?'] = (\n",
    "    df['What was your GPA your very first quarter at UCR?']\n",
    "    .replace('Idk, I think 3.2 or something along those lines', '3.2')\n",
    "    .replace('2.8?', '2.8')\n",
    "    .replace('3 point something', '3.0')\n",
    "    .replace('3.67 I think', '3.67')\n",
    "    .replace('3.0?', '3.0')\n",
    "    .replace('about 3.0', '3.0')\n",
    "    .astype(np.float64))\n",
    "# Normalizes non-applicable answers\n",
    "df.loc[df['Do you currently work?'] == 'No', 'How many hours do you work per week on average?'] = 0\n",
    "df.loc[df['Do you currently work?'] == 'No', 'Do you work in a department related to your major?'] = np.nan\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "285236650ff590d8",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-24T05:38:10.456195Z",
     "start_time": "2024-02-24T05:38:10.447401Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "    What is your current class standing? What is your age?  \\\n0                                 Senior               23+   \n4                               Graduate                22   \n8                                 Junior                20   \n9                                 Senior                22   \n13                                Junior                21   \n..                                   ...               ...   \n246                             Graduate               23+   \n247                               Senior                21   \n252                               Junior                20   \n255                               Junior                21   \n258                            Sophomore                21   \n\n    Who do you live with?  \\\n0                 Neither   \n4                 Neither   \n8                 Friends   \n9                  Family   \n13                 Family   \n..                    ...   \n246                Family   \n247               Friends   \n252                Family   \n255               Friends   \n258                Family   \n\n    Do you currently live in a house, apartment, or dorm?  \\\n0                                                House      \n4                                            Apartment      \n8                                                House      \n9                                                House      \n13                                           Apartment      \n..                                                 ...      \n246                                              House      \n247                                          Apartment      \n252                                              House      \n255                                              House      \n258                                          Apartment      \n\n     How many people live in your household?  \\\n0                                          6   \n4                                          1   \n8                                          6   \n9                                          5   \n13                                         4   \n..                                       ...   \n246                                        2   \n247                                        3   \n252                                        5   \n255                                        5   \n258                                        4   \n\n     What was your GPA your very first quarter at UCR? Do you currently work?  \\\n0                                                 2.73                    Yes   \n4                                                 3.23                    Yes   \n8                                                 3.40                    Yes   \n9                                                  NaN                    Yes   \n13                                                3.50                    Yes   \n..                                                 ...                    ...   \n246                                               4.00                    Yes   \n247                                               3.60                    Yes   \n252                                               3.50                    Yes   \n255                                               4.00                    Yes   \n258                                               3.00                    Yes   \n\n    How many hours do you work per week on average?  \\\n0                                            5 - 10   \n4                                           10 - 20   \n8                                           10 - 20   \n9                                             1 - 5   \n13                                          10 - 20   \n..                                              ...   \n246                                         10 - 20   \n247                                         20 - 40   \n252                                         20 - 40   \n255                                         10 - 20   \n258                                          5 - 10   \n\n    Do you work on or off campus?  \\\n0                      Off-campus   \n4                      Off-campus   \n8                       On-campus   \n9                       On-campus   \n13                     Off-campus   \n..                            ...   \n246                     On-campus   \n247                    Off-campus   \n252                    Off-campus   \n255                     On-campus   \n258                     On-campus   \n\n    Do you work in a department related to your major?  \\\n0                                                   No   \n4                                                  Yes   \n8                                                   No   \n9                                                   No   \n13                                                  No   \n..                                                 ...   \n246                                                Yes   \n247                                                 No   \n252                                                 No   \n255                                                 No   \n258                                                 No   \n\n    Do you have roommates that are part of your major?  \n0                                                   No  \n4                                                   No  \n8                                                   No  \n9                                                   No  \n13                                                  No  \n..                                                 ...  \n246                                                 No  \n247                                                Yes  \n252                                                 No  \n255                                                 No  \n258                                                 No  \n\n[77 rows x 11 columns]",
      "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>What is your current class standing?</th>\n      <th>What is your age?</th>\n      <th>Who do you live with?</th>\n      <th>Do you currently live in a house, apartment, or dorm?</th>\n      <th>How many people live in your household?</th>\n      <th>What was your GPA your very first quarter at UCR?</th>\n      <th>Do you currently work?</th>\n      <th>How many hours do you work per week on average?</th>\n      <th>Do you work on or off campus?</th>\n      <th>Do you work in a department related to your major?</th>\n      <th>Do you have roommates that are part of your major?</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Senior</td>\n      <td>23+</td>\n      <td>Neither</td>\n      <td>House</td>\n      <td>6</td>\n      <td>2.73</td>\n      <td>Yes</td>\n      <td>5 - 10</td>\n      <td>Off-campus</td>\n      <td>No</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Graduate</td>\n      <td>22</td>\n      <td>Neither</td>\n      <td>Apartment</td>\n      <td>1</td>\n      <td>3.23</td>\n      <td>Yes</td>\n      <td>10 - 20</td>\n      <td>Off-campus</td>\n      <td>Yes</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Junior</td>\n      <td>20</td>\n      <td>Friends</td>\n      <td>House</td>\n      <td>6</td>\n      <td>3.40</td>\n      <td>Yes</td>\n      <td>10 - 20</td>\n      <td>On-campus</td>\n      <td>No</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Senior</td>\n      <td>22</td>\n      <td>Family</td>\n      <td>House</td>\n      <td>5</td>\n      <td>NaN</td>\n      <td>Yes</td>\n      <td>1 - 5</td>\n      <td>On-campus</td>\n      <td>No</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>Junior</td>\n      <td>21</td>\n      <td>Family</td>\n      <td>Apartment</td>\n      <td>4</td>\n      <td>3.50</td>\n      <td>Yes</td>\n      <td>10 - 20</td>\n      <td>Off-campus</td>\n      <td>No</td>\n      <td>No</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>246</th>\n      <td>Graduate</td>\n      <td>23+</td>\n      <td>Family</td>\n      <td>House</td>\n      <td>2</td>\n      <td>4.00</td>\n      <td>Yes</td>\n      <td>10 - 20</td>\n      <td>On-campus</td>\n      <td>Yes</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>247</th>\n      <td>Senior</td>\n      <td>21</td>\n      <td>Friends</td>\n      <td>Apartment</td>\n      <td>3</td>\n      <td>3.60</td>\n      <td>Yes</td>\n      <td>20 - 40</td>\n      <td>Off-campus</td>\n      <td>No</td>\n      <td>Yes</td>\n    </tr>\n    <tr>\n      <th>252</th>\n      <td>Junior</td>\n      <td>20</td>\n      <td>Family</td>\n      <td>House</td>\n      <td>5</td>\n      <td>3.50</td>\n      <td>Yes</td>\n      <td>20 - 40</td>\n      <td>Off-campus</td>\n      <td>No</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>255</th>\n      <td>Junior</td>\n      <td>21</td>\n      <td>Friends</td>\n      <td>House</td>\n      <td>5</td>\n      <td>4.00</td>\n      <td>Yes</td>\n      <td>10 - 20</td>\n      <td>On-campus</td>\n      <td>No</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>258</th>\n      <td>Sophomore</td>\n      <td>21</td>\n      <td>Family</td>\n      <td>Apartment</td>\n      <td>4</td>\n      <td>3.00</td>\n      <td>Yes</td>\n      <td>5 - 10</td>\n      <td>On-campus</td>\n      <td>No</td>\n      <td>No</td>\n    </tr>\n  </tbody>\n</table>\n<p>77 rows × 11 columns</p>\n</div>"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Working DataFrame\n",
    "w_df = df[df['Do you currently work?'] == 'Yes']\n",
    "# Not working DataFrame\n",
    "nw_df = df[df['Do you currently work?'] == 'No']\n",
    "w_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6516c926e6efd1c3",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-24T05:38:10.466309Z",
     "start_time": "2024-02-24T05:38:10.456960Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "    What is your current class standing? What is your age?  \\\n1                                 Junior                20   \n2                                 Junior               23+   \n3                                 Senior               23+   \n5                                 Junior                21   \n6                              Sophomore                19   \n..                                   ...               ...   \n253                               Senior                21   \n254                               Junior                19   \n256                                  NaN                21   \n257                               Senior                21   \n259                            Sophomore                18   \n\n    Who do you live with?  \\\n1                    Both   \n2                 Friends   \n3                 Neither   \n5                    Both   \n6                 Friends   \n..                    ...   \n253                Family   \n254                Family   \n256                Family   \n257                Family   \n259               Friends   \n\n    Do you currently live in a house, apartment, or dorm?  \\\n1                                            Apartment      \n2                                                House      \n3                                            Apartment      \n5                                            Apartment      \n6                                            Apartment      \n..                                                 ...      \n253                                              House      \n254                                              House      \n256                                          Apartment      \n257                                              House      \n259                                               Dorm      \n\n     How many people live in your household?  \\\n1                                          4   \n2                                          4   \n3                                          1   \n5                                          4   \n6                                          4   \n..                                       ...   \n253                                        6   \n254                                        5   \n256                                        4   \n257                                        9   \n259                                        3   \n\n     What was your GPA your very first quarter at UCR? Do you currently work?  \\\n1                                                 3.70                     No   \n2                                                 3.75                     No   \n3                                                 3.81                     No   \n5                                                 4.00                     No   \n6                                                 4.00                     No   \n..                                                 ...                    ...   \n253                                               4.00                     No   \n254                                               3.80                     No   \n256                                               3.50                     No   \n257                                               3.70                     No   \n259                                               4.00                     No   \n\n    How many hours do you work per week on average?  \\\n1                                                 0   \n2                                                 0   \n3                                                 0   \n5                                                 0   \n6                                                 0   \n..                                              ...   \n253                                               0   \n254                                               0   \n256                                               0   \n257                                               0   \n259                                               0   \n\n    Do you work on or off campus?  \\\n1                             NaN   \n2                             NaN   \n3                             NaN   \n5                             NaN   \n6                             NaN   \n..                            ...   \n253                           NaN   \n254                           NaN   \n256                           NaN   \n257                    Off-campus   \n259                           NaN   \n\n    Do you work in a department related to your major?  \\\n1                                                  NaN   \n2                                                  NaN   \n3                                                  NaN   \n5                                                  NaN   \n6                                                  NaN   \n..                                                 ...   \n253                                                NaN   \n254                                                NaN   \n256                                                NaN   \n257                                                NaN   \n259                                                NaN   \n\n    Do you have roommates that are part of your major?  \n1                                                  Yes  \n2                                                   No  \n3                                                   No  \n5                                                   No  \n6                                                   No  \n..                                                 ...  \n253                                                 No  \n254                                                Yes  \n256                                                 No  \n257                                                 No  \n259                                                Yes  \n\n[183 rows x 11 columns]",
      "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>What is your current class standing?</th>\n      <th>What is your age?</th>\n      <th>Who do you live with?</th>\n      <th>Do you currently live in a house, apartment, or dorm?</th>\n      <th>How many people live in your household?</th>\n      <th>What was your GPA your very first quarter at UCR?</th>\n      <th>Do you currently work?</th>\n      <th>How many hours do you work per week on average?</th>\n      <th>Do you work on or off campus?</th>\n      <th>Do you work in a department related to your major?</th>\n      <th>Do you have roommates that are part of your major?</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>Junior</td>\n      <td>20</td>\n      <td>Both</td>\n      <td>Apartment</td>\n      <td>4</td>\n      <td>3.70</td>\n      <td>No</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Yes</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Junior</td>\n      <td>23+</td>\n      <td>Friends</td>\n      <td>House</td>\n      <td>4</td>\n      <td>3.75</td>\n      <td>No</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Senior</td>\n      <td>23+</td>\n      <td>Neither</td>\n      <td>Apartment</td>\n      <td>1</td>\n      <td>3.81</td>\n      <td>No</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Junior</td>\n      <td>21</td>\n      <td>Both</td>\n      <td>Apartment</td>\n      <td>4</td>\n      <td>4.00</td>\n      <td>No</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Sophomore</td>\n      <td>19</td>\n      <td>Friends</td>\n      <td>Apartment</td>\n      <td>4</td>\n      <td>4.00</td>\n      <td>No</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>No</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>253</th>\n      <td>Senior</td>\n      <td>21</td>\n      <td>Family</td>\n      <td>House</td>\n      <td>6</td>\n      <td>4.00</td>\n      <td>No</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>254</th>\n      <td>Junior</td>\n      <td>19</td>\n      <td>Family</td>\n      <td>House</td>\n      <td>5</td>\n      <td>3.80</td>\n      <td>No</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Yes</td>\n    </tr>\n    <tr>\n      <th>256</th>\n      <td>NaN</td>\n      <td>21</td>\n      <td>Family</td>\n      <td>Apartment</td>\n      <td>4</td>\n      <td>3.50</td>\n      <td>No</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>257</th>\n      <td>Senior</td>\n      <td>21</td>\n      <td>Family</td>\n      <td>House</td>\n      <td>9</td>\n      <td>3.70</td>\n      <td>No</td>\n      <td>0</td>\n      <td>Off-campus</td>\n      <td>NaN</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>259</th>\n      <td>Sophomore</td>\n      <td>18</td>\n      <td>Friends</td>\n      <td>Dorm</td>\n      <td>3</td>\n      <td>4.00</td>\n      <td>No</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Yes</td>\n    </tr>\n  </tbody>\n</table>\n<p>183 rows × 11 columns</p>\n</div>"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nw_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7efd20d58edbb05d",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6deea60d8966fa15",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-24T05:38:10.699027Z",
     "start_time": "2024-02-24T05:38:10.468100Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 800x800 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Count the number of people who work and don't work\n",
    "work_counts = df['Do you currently work?'].value_counts()\n",
    "\n",
    "# Plotting a pie chart\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.pie(work_counts, labels=work_counts.index, autopct='%1.1f%%', startangle=90, colors=['lightblue', 'lightcoral'])\n",
    "plt.title('Distribution of People Who Work and Don\\'t Work')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "The majority of student respondents (70.4%) do **not** work while attending school."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "5dbc734405a3858d"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "Do you currently live in a house, apartment, or dorm?  Apartment      Dorm  \\\nDo you currently work?                                                       \nNo                                                      0.500000  0.131868   \nYes                                                     0.493506  0.064935   \n\nDo you currently live in a house, apartment, or dorm?     House      Room  \nDo you currently work?                                                     \nNo                                                     0.362637  0.005495  \nYes                                                    0.441558  0.000000  ",
      "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>Do you currently live in a house, apartment, or dorm?</th>\n      <th>Apartment</th>\n      <th>Dorm</th>\n      <th>House</th>\n      <th>Room</th>\n    </tr>\n    <tr>\n      <th>Do you currently work?</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>No</th>\n      <td>0.500000</td>\n      <td>0.131868</td>\n      <td>0.362637</td>\n      <td>0.005495</td>\n    </tr>\n    <tr>\n      <th>Yes</th>\n      <td>0.493506</td>\n      <td>0.064935</td>\n      <td>0.441558</td>\n      <td>0.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 800x800 with 2 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_2dhist = pd.crosstab(df.loc[:, 'Do you currently work?'],\n",
    "                        df.loc[:, 'Do you currently live in a house, apartment, or dorm?'],\n",
    "                        normalize='index')\n",
    "\n",
    "# Plot heatmap\n",
    "plt.subplots(figsize=(8, 8))\n",
    "sns.heatmap(df_2dhist, cmap=\"Blues\")\n",
    "plt.xlabel('Do you currently live in a house, apartment, or dorm?')\n",
    "_ = plt.ylabel('Do you currently work?')\n",
    "df_2dhist"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-24T05:38:11.115799Z",
     "start_time": "2024-02-24T05:38:10.717858Z"
    }
   },
   "id": "c533e52f7d64a4df",
   "execution_count": 6
  },
  {
   "cell_type": "markdown",
   "source": [
    "For both working & non-working participants, the proportion who live in an apartment are equivalent (50%).\n",
    "\n",
    "However, 13% of non-working participants live in a dorm while only 6% of working participants live in a dorm.\n",
    "This 7% drop is matched in participants who live a house, with 44% of working participants living in a house compared to 36% of non-working participants.\n",
    "\n",
    "This indicates that working participants tend to live off-campus and in living situations that have a higher cost of living."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "6294dcc03fa9f516"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('Do you currently live in a house, apartment, or dorm?').size().plot(kind='barh',\n",
    "                                                                                color=sns.palettes.mpl_palette(\n",
    "                                                                                    'Dark2'))\n",
    "plt.gca().spines[['top', 'right', ]].set_visible(False)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-24T05:38:11.239245Z",
     "start_time": "2024-02-24T05:38:11.116471Z"
    }
   },
   "id": "450665f2272bb3a2",
   "execution_count": 7
  },
  {
   "cell_type": "markdown",
   "source": [
    "Most participants live in either an Apartment or a House. This would indicate that most students either live off-campus or on-campus apartments."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "4d64d891924f201e"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Average GPA:  3.6520247933884296\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataTable1 = pd.pivot_table(data=df, values='What was your GPA your very first quarter at UCR?',\n",
    "                            index='How many hours do you work per week on average?', aggfunc='mean')\n",
    "_ = dataTable1.plot(kind='bar')\n",
    "print(\"Total Average GPA: \", df['What was your GPA your very first quarter at UCR?'].mean())"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-24T05:38:11.368629Z",
     "start_time": "2024-02-24T05:38:11.241156Z"
    }
   },
   "id": "1a704a4702ea3f9c",
   "execution_count": 8
  },
  {
   "cell_type": "markdown",
   "source": [
    "The average GPA seems to be independent in respect to working hours per week.\n",
    "Most students who work less than 20 hours have an equivalent average GPA to the total average GPA of all participants (3.65).\n",
    "\n",
    "There is a small drop in GPA associated with students who work more than 20 hours (3.5 GPA), which may mean some of those students may struggle maintaining balance between work and school.  \n",
    "\n",
    "This would indicate that most students seem to be able to balance work with school. However, it would also indicate that\n",
    "students who work full-time jobs may struggle slightly in school."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "cb1fd8e56d403466"
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Hypotheses"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "f573ee142b931496"
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Hypothesis 2: Students who live on-campus are more likely to have roommates of the same major.\n",
    "\n",
    "Null Hypothesis: There is no relationship between students who live on-campus and students who have roommates of the same major.\n",
    "\n",
    "Significance value: 0.1\n",
    "Degrees of Freedom: 2"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "dcc6d91b3e660c2e"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "Do you work in a department related to your major?  No  Yes  Total\nDo you currently live in a house, apartment, or...                \nApartment                                           22   16     38\nDorm                                                 4    1      5\nHouse                                               27    7     34\nTotal                                               53   24     77",
      "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>Do you work in a department related to your major?</th>\n      <th>No</th>\n      <th>Yes</th>\n      <th>Total</th>\n    </tr>\n    <tr>\n      <th>Do you currently live in a house, apartment, or dorm?</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Apartment</th>\n      <td>22</td>\n      <td>16</td>\n      <td>38</td>\n    </tr>\n    <tr>\n      <th>Dorm</th>\n      <td>4</td>\n      <td>1</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>House</th>\n      <td>27</td>\n      <td>7</td>\n      <td>34</td>\n    </tr>\n    <tr>\n      <th>Total</th>\n      <td>53</td>\n      <td>24</td>\n      <td>77</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roommates_major_table = pd.crosstab(df.iloc[:, 3], df.iloc[:, 9], margins=True, margins_name='Total')\n",
    "roommates_major_table"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-24T05:38:11.454613Z",
     "start_time": "2024-02-24T05:38:11.414412Z"
    }
   },
   "id": "24d1f01fdd4ca1d6",
   "execution_count": 9
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi-squared value: 4.183390044200403\n"
     ]
    }
   ],
   "source": [
    "num_rows, num_cols = roommates_major_table.shape\n",
    "# Initialize expected frequencies\n",
    "expected_frequencies = []\n",
    "chi_squared = 0\n",
    "for i in range(num_rows - 1):\n",
    "    row_totals = roommates_major_table.iloc[i, -1]\n",
    "    for j in range(num_cols - 1):\n",
    "        col_totals = roommates_major_table.iloc[-1, j]\n",
    "        expected_frequency = (row_totals * col_totals) / roommates_major_table.iloc[-1, -1]\n",
    "        expected_frequencies.append(expected_frequency)\n",
    "        chi_squared += ((roommates_major_table.iloc[i, j] - expected_frequency) ** 2) / expected_frequency\n",
    "\n",
    "print(\"Chi-squared value:\", chi_squared)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-24T05:38:11.493183Z",
     "start_time": "2024-02-24T05:38:11.468104Z"
    }
   },
   "id": "fd3e73d9f461afd1",
   "execution_count": 10
  },
  {
   "cell_type": "markdown",
   "source": [
    "With a significance value of 0.1 and 2 degrees of freedom, chi-squared must be greater than 4.61.\n",
    "Since chi-squared of `4.18 < 4.61`, we accept the null hypothesis:\n",
    "\n",
    "There is no relationship between students who live on-campus and students who have roommates of the same major."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "32eff09a99bfb3c7"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-24T05:38:11.502610Z",
     "start_time": "2024-02-24T05:38:11.498403Z"
    }
   },
   "id": "b513f8e8241e86e5",
   "execution_count": 10
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}