mh_benefits_analysis.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "mh_benefits_analysis.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "xUcD679tAkRb"
      ],
      "authorship_tag": "ABX9TyNOjplpr++vKJOfky70T+Xd",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/alexisbianco0919/compsci216-project/blob/main/mh_benefits_analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r_1ynyO503xG"
      },
      "source": [
        "# Analysis of mh_benefits "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nf1YO-3y1CBx"
      },
      "source": [
        "# import needed libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import re\n",
        "import seaborn as sns"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 830
        },
        "id": "_Zn_55Cm37aZ",
        "outputId": "9566fe5d-c715-4438-b5fe-ad83a07863ad"
      },
      "source": [
        "# download data from merged_data.csv\n",
        "cd = pd.read_csv('https://raw.githubusercontent.com/alexisbianco0919/compsci216-project/main/clean_data_2.csv')\n",
        "\n",
        "# print column values\n",
        "print(cd['mh_benefits'].value_counts())\n",
        "print('\\n')\n",
        "\n",
        "# print year values\n",
        "print(cd['year'].value_counts())\n",
        "print('\\n')\n",
        "\n",
        "# visualize data\n",
        "cd"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Yes                               1267\n",
            "I don't know                       658\n",
            "No                                 381\n",
            "Not eligible for coverage / NA     148\n",
            "Name: mh_benefits, dtype: int64\n",
            "\n",
            "\n",
            "2016    1433\n",
            "2017     756\n",
            "2018     417\n",
            "2019     352\n",
            "Name: year, dtype: int64\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>self_employeed</th>\n",
              "      <th>company_size</th>\n",
              "      <th>tech_company</th>\n",
              "      <th>tech_prole</th>\n",
              "      <th>mh_benefits</th>\n",
              "      <th>anonymity</th>\n",
              "      <th>leaving_difficulty</th>\n",
              "      <th>disc_mh_coworker</th>\n",
              "      <th>disc_mh_supervisor</th>\n",
              "      <th>reveal_mh_business</th>\n",
              "      <th>reveal_mh_coworkers</th>\n",
              "      <th>affects_productivity</th>\n",
              "      <th>productivity_percent</th>\n",
              "      <th>mention_ph_interview</th>\n",
              "      <th>mention_mh_interview</th>\n",
              "      <th>share_mh_ff</th>\n",
              "      <th>exp_unsupport</th>\n",
              "      <th>reveal_less</th>\n",
              "      <th>family_history</th>\n",
              "      <th>mh_in_past</th>\n",
              "      <th>mh_currently</th>\n",
              "      <th>mh_diagnosed</th>\n",
              "      <th>sought_treatment</th>\n",
              "      <th>age</th>\n",
              "      <th>gender</th>\n",
              "      <th>country_live</th>\n",
              "      <th>state_live</th>\n",
              "      <th>country_work</th>\n",
              "      <th>state_work</th>\n",
              "      <th>year</th>\n",
              "      <th>gender_cleaned</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>False</td>\n",
              "      <td>26-100</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Not eligible for coverage / NA</td>\n",
              "      <td>I don't know</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Maybe</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Somewhat open</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>39.0</td>\n",
              "      <td>Male</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>NaN</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2016</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>False</td>\n",
              "      <td>6-25</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Maybe</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Somewhat open</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>29.0</td>\n",
              "      <td>male</td>\n",
              "      <td>United States of America</td>\n",
              "      <td>Illinois</td>\n",
              "      <td>United States of America</td>\n",
              "      <td>Illinois</td>\n",
              "      <td>2016</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>False</td>\n",
              "      <td>6-25</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>I don't know</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Somewhat open</td>\n",
              "      <td>Maybe/Not sure</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>Maybe</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>38.0</td>\n",
              "      <td>Male</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>NaN</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2016</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Sometimes, if it comes up</td>\n",
              "      <td>Sometimes, if it comes up</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1-25%</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Neutral</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>43.0</td>\n",
              "      <td>male</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>NaN</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2016</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>False</td>\n",
              "      <td>6-25</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Maybe</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Somewhat open</td>\n",
              "      <td>Yes, I experienced</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>43.0</td>\n",
              "      <td>Female</td>\n",
              "      <td>United States of America</td>\n",
              "      <td>Illinois</td>\n",
              "      <td>United States of America</td>\n",
              "      <td>Illinois</td>\n",
              "      <td>2016</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <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>2953</th>\n",
              "      <td>False</td>\n",
              "      <td>More than 1000</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>I don't know</td>\n",
              "      <td>I don't know</td>\n",
              "      <td>Somewhat difficult</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Maybe</td>\n",
              "      <td>No</td>\n",
              "      <td>Very open</td>\n",
              "      <td>Yes, I observed</td>\n",
              "      <td>Maybe</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Maybe</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "      <td>27.0</td>\n",
              "      <td>male</td>\n",
              "      <td>India</td>\n",
              "      <td>NaN</td>\n",
              "      <td>India</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2019</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2954</th>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Not applicable to me</td>\n",
              "      <td>Not applicable to me</td>\n",
              "      <td>Yes</td>\n",
              "      <td>26-50%</td>\n",
              "      <td>Maybe</td>\n",
              "      <td>Maybe</td>\n",
              "      <td>Somewhat not open</td>\n",
              "      <td>Yes, I observed</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "      <td>48.0</td>\n",
              "      <td>m</td>\n",
              "      <td>United States of America</td>\n",
              "      <td>Louisiana</td>\n",
              "      <td>United States of America</td>\n",
              "      <td>Louisiana</td>\n",
              "      <td>2019</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2955</th>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Not applicable to me</td>\n",
              "      <td>Not applicable to me</td>\n",
              "      <td>Unsure</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Very open</td>\n",
              "      <td>I've always been self-employed</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "      <td>50.0</td>\n",
              "      <td>M</td>\n",
              "      <td>India</td>\n",
              "      <td>NaN</td>\n",
              "      <td>India</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2019</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2956</th>\n",
              "      <td>False</td>\n",
              "      <td>More than 1000</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Difficult</td>\n",
              "      <td>Maybe</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Somewhat not open</td>\n",
              "      <td>Maybe/Not sure</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Maybe</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "      <td>30.0</td>\n",
              "      <td>female</td>\n",
              "      <td>India</td>\n",
              "      <td>NaN</td>\n",
              "      <td>India</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2019</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2957</th>\n",
              "      <td>False</td>\n",
              "      <td>More than 1000</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Somewhat difficult</td>\n",
              "      <td>No</td>\n",
              "      <td>Maybe</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Maybe</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Very open</td>\n",
              "      <td>Maybe/Not sure</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>True</td>\n",
              "      <td>24.0</td>\n",
              "      <td>Female (cis)</td>\n",
              "      <td>United States of America</td>\n",
              "      <td>Oregon</td>\n",
              "      <td>United States of America</td>\n",
              "      <td>Oregon</td>\n",
              "      <td>2019</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2958 rows × 31 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      self_employeed    company_size  ...  year gender_cleaned\n",
              "0              False          26-100  ...  2016           male\n",
              "1              False            6-25  ...  2016           male\n",
              "2              False            6-25  ...  2016           male\n",
              "3               True             NaN  ...  2016           male\n",
              "4              False            6-25  ...  2016         female\n",
              "...              ...             ...  ...   ...            ...\n",
              "2953           False  More than 1000  ...  2019           male\n",
              "2954            True             NaN  ...  2019           male\n",
              "2955            True             NaN  ...  2019           male\n",
              "2956           False  More than 1000  ...  2019         female\n",
              "2957           False  More than 1000  ...  2019         female\n",
              "\n",
              "[2958 rows x 31 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iLjIvZzo1je3"
      },
      "source": [
        "## Basic Demographics"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7F_rn65OBpZY"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qk5TT5L31cjf"
      },
      "source": [
        "## Year-to-Year"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xUcD679tAkRb"
      },
      "source": [
        "### Survey Response"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "_nN3Vhky1gL4",
        "outputId": "758a2b4e-5380-474f-ae7a-2a7faf30e6af"
      },
      "source": [
        "# 2016\n",
        "data_2016 = cd[cd['year'] == 2016]\n",
        "data = data_2016.groupby('mh_benefits').count()['age']\n",
        "data.plot.pie(autopct=\"%.1f%%\")"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AxesSubplot(0.260833,0.125;0.503333x0.755)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "qvSlB7VA_QFI",
        "outputId": "d222c4f9-434d-4a3d-c763-153946e62cfb"
      },
      "source": [
        "# 2017\n",
        "data_2017 = cd[cd['year'] == 2017]\n",
        "data = data_2017.groupby('mh_benefits').count()['age']\n",
        "data.plot.pie(autopct=\"%.1f%%\")"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f91c523a290>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "2FDl82Wj_YT6",
        "outputId": "479eda03-74e5-4605-d62a-4039581b4e69"
      },
      "source": [
        "# 2018\n",
        "data_2018 = cd[cd['year'] == 2018]\n",
        "data = data_2018.groupby('mh_benefits').count()['age']\n",
        "data.plot.pie(autopct=\"%.1f%%\")"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f91c51a2550>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "wbea_GgC_eG1",
        "outputId": "590b8151-6ab8-4b10-b3a4-0edec529513b"
      },
      "source": [
        "# 2019\n",
        "data_2019 = cd[cd['year'] == 2019]\n",
        "data = data_2019.groupby('mh_benefits').count()['age']\n",
        "data.plot.pie(autopct=\"%.1f%%\")"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f91c517bd50>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_gJjtme91mrB"
      },
      "source": [
        "#a"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VspnrSV41odJ"
      },
      "source": [
        "## Hypothesis Testing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3NwYmji-1vJ2"
      },
      "source": [
        "#a"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}