datasci_6_py.ipynb

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      "provenance": [],
      "authorship_tag": "ABX9TyPjf4cJyz2tvrrVO+l1VeWI",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/jesschannn/datasci_6_anova/blob/main/datasci_6_py.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Setting Up"
      ],
      "metadata": {
        "id": "HAH7yiZtmeV-"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RY_3bOogksfO"
      },
      "outputs": [],
      "source": [
        "!pip install ucimlrepo"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from ucimlrepo import fetch_ucirepo\n",
        "import pandas as pd\n",
        "import scipy.stats as stats\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import statsmodels.api as sm\n",
        "from statsmodels.formula.api import ols\n",
        "from statsmodels.stats.multicomp import pairwise_tukeyhsd"
      ],
      "metadata": {
        "id": "oB2rnrzZlCZ8"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Loading in Dataset and Variables"
      ],
      "metadata": {
        "id": "04GyRWjRmg6r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# fetch dataset\n",
        "diabetes_130_us_hospitals_for_years_1999_2008 = fetch_ucirepo(id=296)"
      ],
      "metadata": {
        "id": "TEMnzc_jlKPW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# data (as pandas dataframes)\n",
        "X = diabetes_130_us_hospitals_for_years_1999_2008.data.features\n",
        "y = diabetes_130_us_hospitals_for_years_1999_2008.data.targets"
      ],
      "metadata": {
        "id": "0_oYMRuYlQDO"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2 = pd.DataFrame(X)\n",
        "df2"
      ],
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          "height": 635
        },
        "id": "wOOxQP1llTBK",
        "outputId": "dde0dffd-6956-422a-b1a4-a88aa48015a7"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   race  gender      age weight  admission_type_id  \\\n",
              "0             Caucasian  Female   [0-10)    NaN                  6   \n",
              "1             Caucasian  Female  [10-20)    NaN                  1   \n",
              "2       AfricanAmerican  Female  [20-30)    NaN                  1   \n",
              "3             Caucasian    Male  [30-40)    NaN                  1   \n",
              "4             Caucasian    Male  [40-50)    NaN                  1   \n",
              "...                 ...     ...      ...    ...                ...   \n",
              "101761  AfricanAmerican    Male  [70-80)    NaN                  1   \n",
              "101762  AfricanAmerican  Female  [80-90)    NaN                  1   \n",
              "101763        Caucasian    Male  [70-80)    NaN                  1   \n",
              "101764        Caucasian  Female  [80-90)    NaN                  2   \n",
              "101765        Caucasian    Male  [70-80)    NaN                  1   \n",
              "\n",
              "        discharge_disposition_id  admission_source_id  time_in_hospital  \\\n",
              "0                             25                    1                 1   \n",
              "1                              1                    7                 3   \n",
              "2                              1                    7                 2   \n",
              "3                              1                    7                 2   \n",
              "4                              1                    7                 1   \n",
              "...                          ...                  ...               ...   \n",
              "101761                         3                    7                 3   \n",
              "101762                         4                    5                 5   \n",
              "101763                         1                    7                 1   \n",
              "101764                         3                    7                10   \n",
              "101765                         1                    7                 6   \n",
              "\n",
              "       payer_code         medical_specialty  ...  examide  citoglipton  \\\n",
              "0             NaN  Pediatrics-Endocrinology  ...       No           No   \n",
              "1             NaN                       NaN  ...       No           No   \n",
              "2             NaN                       NaN  ...       No           No   \n",
              "3             NaN                       NaN  ...       No           No   \n",
              "4             NaN                       NaN  ...       No           No   \n",
              "...           ...                       ...  ...      ...          ...   \n",
              "101761         MC                       NaN  ...       No           No   \n",
              "101762         MC                       NaN  ...       No           No   \n",
              "101763         MC                       NaN  ...       No           No   \n",
              "101764         MC           Surgery-General  ...       No           No   \n",
              "101765        NaN                       NaN  ...       No           No   \n",
              "\n",
              "        insulin  glyburide-metformin  glipizide-metformin  \\\n",
              "0            No                   No                   No   \n",
              "1            Up                   No                   No   \n",
              "2            No                   No                   No   \n",
              "3            Up                   No                   No   \n",
              "4        Steady                   No                   No   \n",
              "...         ...                  ...                  ...   \n",
              "101761     Down                   No                   No   \n",
              "101762   Steady                   No                   No   \n",
              "101763     Down                   No                   No   \n",
              "101764       Up                   No                   No   \n",
              "101765       No                   No                   No   \n",
              "\n",
              "        glimepiride-pioglitazone metformin-rosiglitazone  \\\n",
              "0                             No                      No   \n",
              "1                             No                      No   \n",
              "2                             No                      No   \n",
              "3                             No                      No   \n",
              "4                             No                      No   \n",
              "...                          ...                     ...   \n",
              "101761                        No                      No   \n",
              "101762                        No                      No   \n",
              "101763                        No                      No   \n",
              "101764                        No                      No   \n",
              "101765                        No                      No   \n",
              "\n",
              "       metformin-pioglitazone change  diabetesMed  \n",
              "0                          No     No           No  \n",
              "1                          No     Ch          Yes  \n",
              "2                          No     No          Yes  \n",
              "3                          No     Ch          Yes  \n",
              "4                          No     Ch          Yes  \n",
              "...                       ...    ...          ...  \n",
              "101761                     No     Ch          Yes  \n",
              "101762                     No     No          Yes  \n",
              "101763                     No     Ch          Yes  \n",
              "101764                     No     Ch          Yes  \n",
              "101765                     No     No           No  \n",
              "\n",
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              "      <td>No</td>\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-39b59c0a-7e29-40f3-976c-3ff4d849a348\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-39b59c0a-7e29-40f3-976c-3ff4d849a348')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-39b59c0a-7e29-40f3-976c-3ff4d849a348 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2.to_csv('diabetes.csv')"
      ],
      "metadata": {
        "id": "PllN7C84lYDF"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Modified Dataset"
      ],
      "metadata": {
        "id": "fuxSz1-_mu-F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df1 = df2[['gender', 'max_glu_serum', 'time_in_hospital']]\n",
        "df1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "KXUVt6AulfH1",
        "outputId": "983d64b5-48f9-423d-a39b-840669b69c12"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        gender max_glu_serum  time_in_hospital\n",
              "0       Female          None                 1\n",
              "1       Female          None                 3\n",
              "2       Female          None                 2\n",
              "3         Male          None                 2\n",
              "4         Male          None                 1\n",
              "...        ...           ...               ...\n",
              "101761    Male          None                 3\n",
              "101762  Female          None                 5\n",
              "101763    Male          None                 1\n",
              "101764  Female          None                10\n",
              "101765    Male          None                 6\n",
              "\n",
              "[101766 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0c59f3ff-168d-4ffa-9d47-bd8a7c70e900\" class=\"colab-df-container\">\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>gender</th>\n",
              "      <th>max_glu_serum</th>\n",
              "      <th>time_in_hospital</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Female</td>\n",
              "      <td>None</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Female</td>\n",
              "      <td>None</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Female</td>\n",
              "      <td>None</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Male</td>\n",
              "      <td>None</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Male</td>\n",
              "      <td>None</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101761</th>\n",
              "      <td>Male</td>\n",
              "      <td>None</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101762</th>\n",
              "      <td>Female</td>\n",
              "      <td>None</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101763</th>\n",
              "      <td>Male</td>\n",
              "      <td>None</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101764</th>\n",
              "      <td>Female</td>\n",
              "      <td>None</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101765</th>\n",
              "      <td>Male</td>\n",
              "      <td>None</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>101766 rows × 3 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0c59f3ff-168d-4ffa-9d47-bd8a7c70e900')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-0c59f3ff-168d-4ffa-9d47-bd8a7c70e900 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-0c59f3ff-168d-4ffa-9d47-bd8a7c70e900');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-979a5848-5c01-4027-b4ee-e40c3e927126\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-979a5848-5c01-4027-b4ee-e40c3e927126')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-979a5848-5c01-4027-b4ee-e40c3e927126 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df1.dropna()\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "IBWSYAPInoJh",
        "outputId": "8363862a-c63d-48d1-d798-cf5498005573"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        gender max_glu_serum  time_in_hospital\n",
              "0       Female          None                 1\n",
              "1       Female          None                 3\n",
              "2       Female          None                 2\n",
              "3         Male          None                 2\n",
              "4         Male          None                 1\n",
              "...        ...           ...               ...\n",
              "101761    Male          None                 3\n",
              "101762  Female          None                 5\n",
              "101763    Male          None                 1\n",
              "101764  Female          None                10\n",
              "101765    Male          None                 6\n",
              "\n",
              "[101766 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-22966b2d-2235-447d-9634-3ba527bd79b5\" class=\"colab-df-container\">\n",
              "    <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>gender</th>\n",
              "      <th>max_glu_serum</th>\n",
              "      <th>time_in_hospital</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Female</td>\n",
              "      <td>None</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Female</td>\n",
              "      <td>None</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Female</td>\n",
              "      <td>None</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Male</td>\n",
              "      <td>None</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Male</td>\n",
              "      <td>None</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101761</th>\n",
              "      <td>Male</td>\n",
              "      <td>None</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101762</th>\n",
              "      <td>Female</td>\n",
              "      <td>None</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101763</th>\n",
              "      <td>Male</td>\n",
              "      <td>None</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101764</th>\n",
              "      <td>Female</td>\n",
              "      <td>None</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101765</th>\n",
              "      <td>Male</td>\n",
              "      <td>None</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>101766 rows × 3 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-22966b2d-2235-447d-9634-3ba527bd79b5')\"\n",
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              "      background-color: #E8F0FE;\n",
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              "      height: 32px;\n",
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              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "    .colab-df-buttons div {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-22966b2d-2235-447d-9634-3ba527bd79b5 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-22966b2d-2235-447d-9634-3ba527bd79b5');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "  </div>\n",
              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b2d9adf2-2a55-420b-85e2-208199c1144b')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
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              "    60% {\n",
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              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b2d9adf2-2a55-420b-85e2-208199c1144b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.to_csv('clean_diabetes.csv')"
      ],
      "metadata": {
        "id": "A73mzd_wmFZE"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Checking Data Type"
      ],
      "metadata": {
        "id": "GZ03_XBLmzGi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_type = df.dtypes\n",
        "data_type"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "neNJWBtxmKsa",
        "outputId": "0d4bceed-6f9a-4b17-e043-01be2f9c88a2"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "gender              object\n",
              "max_glu_serum       object\n",
              "time_in_hospital     int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Markdown Insights\n",
        "\n",
        "IV 1: gender\n",
        "- Gender was a variable I picked because certain diseases may affect men and women differently, which can influence how they are diagnosed and treated. Specifically for diabetes, there are differences in the prevalence and incidence of diabetes between men and women, certain hormonal and genetic factors for each gender may contribute to disparities in diabetes risk, and symptoms and signs of diabetes can differ between men and women.\n",
        "\n",
        "IV 2: max_glu_serum\n",
        "- Maximum glucose serum was another variable I picked because maximum glucose serum levels are used as a monitoring tool for diabetes. As a monitoring tool, data from maximum glucose serum levels can provide information about glycemic control and if any interventions need to be taken.\n",
        "\n",
        "DV: time_in_hospital\n",
        "- Time in hospital was the last variable I picked because the overall goal for hospitals is to reduce the time patients have to spend in the hospital. Hospitals have successfully been able to reduce the amount of time patients spend in a hospital by advancements in diagnostics.\n",
        "\n",
        "Challenges\n",
        "- When I initially did the Levene test with a different set of variables, my p-value returned a \"NaN\" value. I double checked that I dropped all missing values from the dataset, which I had done. Then, I reran the all the code from the top to see if I had missed something, but I was still producing the same \"NaN\" value for the Levene test. I ended up deleting the entire Colab document and started over from scratch. I was still getting the same result whenever I ran the Levene test, so I ended up picking a different set of variables, which resulted in an actual numerical p-value."
      ],
      "metadata": {
        "id": "jufnh970J67p"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Checking for Normality\n",
        "\n",
        "**Interpretation**: Based on the p-value of the stats.shapiro test, the p-value of 0.0 indicates that the data is not normally distributed from the parametrics statistics perspective because the p-value is too low. The histogram further proves this because the data is skewed to the right. Additionally, from a more comprehensive Shapiro-Wilks test, it's observed that the p-values generated from each combination of gender and max_glu_serum produces p-values that are all below 0.05. Therefore, the data is not normally distributed. Another conclusion that can be drawn from the p-value derived from the Shapiro-Wilks test is that gender and maximum glucose serum have a significant effect on a patient's time spent in the hospital."
      ],
      "metadata": {
        "id": "JXZ7Mvh8m3cI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "stats.shapiro(df['time_in_hospital'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "em4VwcYcnBmu",
        "outputId": "03314805-4d18-4bc0-822d-4f044cd63a3e"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ShapiroResult(statistic=0.8869192600250244, pvalue=0.0)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(df['time_in_hospital'], bins=10, edgecolor='k', alpha=0.7)\n",
        "plt.title('Histogram of Time Spent in Hospital')\n",
        "plt.xlabel('Days')\n",
        "plt.ylabel('Number')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "RycKpbrAnGuK",
        "outputId": "ba6ea5f6-1289-40a7-9d09-fd5353f2e035"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "groups = df.groupby(['gender', 'max_glu_serum'])\n",
        "\n",
        "for (gender_status, max_glu_serum_status), group_df in groups:\n",
        "    _, p_value = stats.shapiro(group_df['time_in_hospital'])\n",
        "\n",
        "    print(f\"Group ({gender_status}, {max_glu_serum_status}):\")\n",
        "    print(f\"P-value from Shapiro-Wilk Test: {p_value}\\n\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X006hgXdnJ6r",
        "outputId": "a3ad7fa6-703c-4fca-a2e8-61899d659b8a"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Group (Female, >200):\n",
            "P-value from Shapiro-Wilk Test: 1.2738052328530276e-21\n",
            "\n",
            "Group (Female, >300):\n",
            "P-value from Shapiro-Wilk Test: 1.1768474598826276e-15\n",
            "\n",
            "Group (Female, None):\n",
            "P-value from Shapiro-Wilk Test: 0.0\n",
            "\n",
            "Group (Female, Norm):\n",
            "P-value from Shapiro-Wilk Test: 2.2239057252585333e-30\n",
            "\n",
            "Group (Male, >200):\n",
            "P-value from Shapiro-Wilk Test: 4.7388259639670904e-20\n",
            "\n",
            "Group (Male, >300):\n",
            "P-value from Shapiro-Wilk Test: 1.030565796334096e-16\n",
            "\n",
            "Group (Male, None):\n",
            "P-value from Shapiro-Wilk Test: 0.0\n",
            "\n",
            "Group (Male, Norm):\n",
            "P-value from Shapiro-Wilk Test: 1.6725332351183306e-29\n",
            "\n",
            "Group (Unknown/Invalid, None):\n",
            "P-value from Shapiro-Wilk Test: 1.3144705723888706e-07\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/scipy/stats/_morestats.py:1882: UserWarning: p-value may not be accurate for N > 5000.\n",
            "  warnings.warn(\"p-value may not be accurate for N > 5000.\")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Homoscedasticity (Equal Variances)\n",
        "\n",
        "**Interpretation**: The p-value from the Levene Test is ~4.12 * 10 <sup> -11 </sup> , which is less than 0.05. This means that there's a significant difference in variances in the time spent in the hospital, gender, and maximum glucose serum data. This also means that the variances across these three groups are not equal."
      ],
      "metadata": {
        "id": "PTrBqVlkotaJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "stats.levene(\n",
        "    df['time_in_hospital'][df['gender'] == 'Female'][df['max_glu_serum'] == '>300'],\n",
        "    df['time_in_hospital'][df['gender'] == 'Male'][df['max_glu_serum'] == '>300'],\n",
        "    df['time_in_hospital'][df['gender'] == 'Female'][df['max_glu_serum'] == '>200'],\n",
        "    df['time_in_hospital'][df['gender'] == 'Male'][df['max_glu_serum'] == '>200'],\n",
        "    df['time_in_hospital'][df['gender'] == 'Female'][df['max_glu_serum'] == 'None'],\n",
        "    df['time_in_hospital'][df['gender'] == 'Male'][df['max_glu_serum'] == 'None'],\n",
        "    df['time_in_hospital'][df['gender'] == 'Female'][df['max_glu_serum'] == 'Norm'],\n",
        "    df['time_in_hospital'][df['gender'] == 'Male'][df['max_glu_serum'] == 'Norm'],\n",
        "\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ODqm9Meln5Lc",
        "outputId": "1eed78f1-4277-40e7-9122-0235607e10a6"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LeveneResult(statistic=8.976356359620828, pvalue=4.1246433591964124e-11)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ANOVA\n",
        "\n",
        "**Interpretation**: The purpose of the ANOVA test is to allow for a comparison of more than two groups at the same time to determine if there's a relationship between them. If the p-value is less than 0.05, then it means that the independent variable has a significant effect on the dependent variable. On the other hand, if the p-value is greater than 0.05, then it means that the independent variable does not have a significant effect on the dependent variable.\n",
        "\n",
        "For C(gender), the p-value is 9.999869e-01, which is much greater than the alpha level (0.05). This means that gender does not have a statistically significant impact on the dependent variable.\n",
        "\n",
        "For C(max_glu_serum), the p-value is 3.658665e-24, which is much smaller than 0.05. Therefore, max_glu_serum is highly significant in predicting the dependent variable.\n",
        "\n",
        "For C(gender):C(max_glu_serum), the p-value is 1.875794e-01. This p-value is greater than the alpha level of 0.05, indicating that the interaction effect is not statistically significant."
      ],
      "metadata": {
        "id": "7NeRw4a-oxSy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = ols('time_in_hospital ~ C(gender) * C(max_glu_serum)', data=df).fit()"
      ],
      "metadata": {
        "id": "e9iMg9NKoksI"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "anova_table = sm.stats.anova_lm(model, typ=2)\n",
        "print(anova_table)"
      ],
      "metadata": {
        "id": "HRdRU4gqo2IE",
        "outputId": "fe2b0bdf-649c-419c-9291-7de5890932aa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                  sum_sq        df             F        PR(>F)\n",
            "C(gender)                   4.794263e-09       2.0  2.698080e-10  9.999869e-01\n",
            "C(max_glu_serum)            1.439131e+03       3.0  5.399357e+01  3.658665e-24\n",
            "C(gender):C(max_glu_serum)  8.208285e+01       6.0  1.539800e+00  1.875794e-01\n",
            "Residual                    9.040684e+05  101757.0           NaN           NaN\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/statsmodels/base/model.py:1888: ValueWarning: covariance of constraints does not have full rank. The number of constraints is 2, but rank is 1\n",
            "  warnings.warn('covariance of constraints does not have full '\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/base/model.py:1888: ValueWarning: covariance of constraints does not have full rank. The number of constraints is 3, but rank is 2\n",
            "  warnings.warn('covariance of constraints does not have full '\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/base/model.py:1888: ValueWarning: covariance of constraints does not have full rank. The number of constraints is 6, but rank is 4\n",
            "  warnings.warn('covariance of constraints does not have full '\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Post-Hoc Test\n",
        "\n",
        "**Interpretation**: Based on the Tukey Post-Hoc Test, these groups had significant differences between them:\n",
        "- Female > 200 vs. Female > 300\n",
        "- Female > 200 vs. Female None\n",
        "- Female > 200 vs. Female Norm\n",
        "- Female > 200 vs. Male None\n",
        "- Female > 200 vs. Male Norm\n",
        "- Female > 300 vs. Female None\n",
        "- Female > 300 vs. Female Norm\n",
        "- Female > 300 vs. Male > 200\n",
        "- Female > 300 vs. Male > 300\n",
        "- Female > 300 vs. Male None\n",
        "- Female > 300 vs. Male Norm\n",
        "- Female None vs. Female Norm\n",
        "- Female None vs. Male > 300\n",
        "- Female None vs. Male None\n",
        "- Female None vs. Male Norm\n",
        "- Male > 200 vs. Male > 300\n",
        "- Male > 200 vs. Male None\n",
        "- Male > 200 vs. Male Norm\n",
        "- Male > 300 vs. Male None\n",
        "- Male > 300 vs. Male Norm\n",
        "- Male None vs. Male Norm"
      ],
      "metadata": {
        "id": "V316OXERG4fW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Tukey Post-Hoc Test"
      ],
      "metadata": {
        "id": "pTaD2ObeG_uC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tukey_results = pairwise_tukeyhsd(df['time_in_hospital'], df['gender'] + df['max_glu_serum'])\n",
        "print(tukey_results.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g635GNW4HC1U",
        "outputId": "4daf0039-f18c-4bec-e895-333e256a0497"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "         Multiple Comparison of Means - Tukey HSD, FWER=0.05         \n",
            "=====================================================================\n",
            "  group1          group2       meandiff p-adj   lower   upper  reject\n",
            "---------------------------------------------------------------------\n",
            "Female>200          Female>300   0.8531    0.0   0.374  1.3322   True\n",
            "Female>200          FemaleNone  -0.3268 0.0522 -0.6552  0.0015  False\n",
            "Female>200          FemaleNorm  -0.7327    0.0 -1.1416 -0.3239   True\n",
            "Female>200            Male>200  -0.1183 0.9978 -0.5999  0.3632  False\n",
            "Female>200            Male>300    0.279   0.74 -0.2268  0.7849  False\n",
            "Female>200            MaleNone  -0.5009 0.0001 -0.8296 -0.1721   True\n",
            "Female>200            MaleNorm  -0.9192    0.0 -1.3407 -0.4977   True\n",
            "Female>200 Unknown/InvalidNone  -1.4629 0.9953 -6.8107  3.8848  False\n",
            "Female>300          FemaleNone  -1.1799    0.0 -1.5335 -0.8264   True\n",
            "Female>300          FemaleNorm  -1.5858    0.0 -2.0152 -1.1565   True\n",
            "Female>300            Male>200  -0.9714    0.0 -1.4705 -0.4724   True\n",
            "Female>300            Male>300   -0.574 0.0189 -1.0966 -0.0515   True\n",
            "Female>300            MaleNone  -1.3539    0.0 -1.7079    -1.0   True\n",
            "Female>300            MaleNorm  -1.7723    0.0 -2.2136 -1.3309   True\n",
            "Female>300 Unknown/InvalidNone   -2.316 0.9184 -7.6654  3.0333  False\n",
            "FemaleNone          FemaleNorm  -0.4059    0.0 -0.6562 -0.1556   True\n",
            "FemaleNone            Male>200   0.2085 0.6741 -0.1484  0.5654  False\n",
            "FemaleNone            Male>300   0.6059    0.0  0.2168  0.9949   True\n",
            "FemaleNone            MaleNone   -0.174    0.0 -0.2337 -0.1143   True\n",
            "FemaleNone            MaleNorm  -0.5923    0.0 -0.8627 -0.3219   True\n",
            "FemaleNone Unknown/InvalidNone  -1.1361 0.9992 -6.4741  4.2018  False\n",
            "FemaleNorm            Male>200   0.6144 0.0004  0.1823  1.0465   True\n",
            "FemaleNorm            Male>300   1.0118    0.0  0.5528  1.4708   True\n",
            "FemaleNorm            MaleNone   0.2319 0.0967  -0.019  0.4827  False\n",
            "FemaleNorm            MaleNorm  -0.1864 0.8113 -0.5504  0.1775  False\n",
            "FemaleNorm Unknown/InvalidNone  -0.7302    1.0 -6.0737  4.6133  False\n",
            "  Male>200            Male>300   0.3974 0.3129 -0.1274  0.9221  False\n",
            "  Male>200            MaleNone  -0.3825 0.0252 -0.7398 -0.0253   True\n",
            "  Male>200            MaleNorm  -0.8009    0.0 -1.2449 -0.3568   True\n",
            "  Male>200 Unknown/InvalidNone  -1.3446 0.9974 -6.6942  4.0049  False\n",
            "  Male>300            MaleNone  -0.7799    0.0 -1.1693 -0.3905   True\n",
            "  Male>300            MaleNorm  -1.1982    0.0 -1.6685 -0.7279   True\n",
            "  Male>300 Unknown/InvalidNone   -1.742 0.9851 -7.0938  3.6098  False\n",
            "  MaleNone            MaleNorm  -0.4183 0.0001 -0.6892 -0.1474   True\n",
            "  MaleNone Unknown/InvalidNone  -0.9621 0.9998 -6.3001  4.3759  False\n",
            "  MaleNorm Unknown/InvalidNone  -0.5438    1.0 -5.8882  4.8007  False\n",
            "---------------------------------------------------------------------\n"
          ]
        }
      ]
    }
  ]
}