Notebook_Mixed ANOVA.ipynb

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        "# Student ID: 1010752829\n",
        "# Student name: Nargiz Guliyeva\n",
        "# Instructor: Dr. Shion Gupa\n",
        "# Course code:INF 2178\n",
        "# Course name: Experimental Design for Data Science\n",
        "# Program: MI\n",
        "# Faculty of Information\n",
        "# University of Toronto\n",
        "\n",
        "# Technical Assignment 4\n",
        "# Date submitted: 04.05.2024"
      ]
    },
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      "source": [
        "# Import packages\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import statsmodels.api as sm\n",
        "import statsmodels.stats.power as smp\n",
        "\n",
        "%pip install pingouin\n",
        "import pingouin as pg\n",
        "\n",
        "from statsmodels.stats.multicomp import MultiComparison\n",
        "from statsmodels.stats.multicomp import pairwise_tukeyhsd\n",
        "from scipy.stats import shapiro, levene\n",
        "\n",
        "import statsmodels.stats.power as smp\n",
        "from statsmodels.stats.power import TTestIndPower"
      ],
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      "execution_count": 39,
      "outputs": [
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        "# Import the excel file in the coLab \"Files\" section and then assign it to a new variable \"df\" and print the heads\n",
        "df=pd.read_csv(\"INF2178_A4_data.csv\")\n",
        "df.head(10)"
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              "   Unnamed: 0 Subject ID         MRI ID        Group  Visit  MR Delay M/F  \\\n",
              "0           0  OAS2_0001  OAS2_0001_MR1  Nondemented      1         0   M   \n",
              "1           1  OAS2_0001  OAS2_0001_MR2  Nondemented      2       457   M   \n",
              "2           2  OAS2_0002  OAS2_0002_MR1     Demented      1         0   M   \n",
              "3           3  OAS2_0002  OAS2_0002_MR2     Demented      2       560   M   \n",
              "4           5  OAS2_0004  OAS2_0004_MR1  Nondemented      1         0   F   \n",
              "5           6  OAS2_0004  OAS2_0004_MR2  Nondemented      2       538   F   \n",
              "6           7  OAS2_0005  OAS2_0005_MR1  Nondemented      1         0   M   \n",
              "7           8  OAS2_0005  OAS2_0005_MR2  Nondemented      2      1010   M   \n",
              "8          10  OAS2_0007  OAS2_0007_MR1     Demented      1         0   M   \n",
              "9          13  OAS2_0008  OAS2_0008_MR1  Nondemented      1         0   F   \n",
              "\n",
              "  Hand  Age  EDUC  SES  MMSE  CDR  eTIV   nWBV    ASF  \n",
              "0    R   87    14  2.0  27.0  0.0  1987  0.696  0.883  \n",
              "1    R   88    14  2.0  30.0  0.0  2004  0.681  0.876  \n",
              "2    R   75    12  NaN  23.0  0.5  1678  0.736  1.046  \n",
              "3    R   76    12  NaN  28.0  0.5  1738  0.713  1.010  \n",
              "4    R   88    18  3.0  28.0  0.0  1215  0.710  1.444  \n",
              "5    R   90    18  3.0  27.0  0.0  1200  0.718  1.462  \n",
              "6    R   80    12  4.0  28.0  0.0  1689  0.712  1.039  \n",
              "7    R   83    12  4.0  29.0  0.5  1701  0.711  1.032  \n",
              "8    R   71    16  NaN  28.0  0.5  1357  0.748  1.293  \n",
              "9    R   93    14  2.0  30.0  0.0  1272  0.698  1.380  "
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              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>Subject ID</th>\n",
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              "      <td>M</td>\n",
              "      <td>R</td>\n",
              "      <td>76</td>\n",
              "      <td>12</td>\n",
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              "      <td>28.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1738</td>\n",
              "      <td>0.713</td>\n",
              "      <td>1.010</td>\n",
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              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>OAS2_0004</td>\n",
              "      <td>OAS2_0004_MR1</td>\n",
              "      <td>Nondemented</td>\n",
              "      <td>1</td>\n",
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              "      <td>88</td>\n",
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              "      <td>28.0</td>\n",
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              "      <td>6</td>\n",
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              "      <td>Nondemented</td>\n",
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              "      <td>F</td>\n",
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              "      <td>27.0</td>\n",
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              "      <td>1200</td>\n",
              "      <td>0.718</td>\n",
              "      <td>1.462</td>\n",
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              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>OAS2_0005</td>\n",
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              "      <td>OAS2_0005</td>\n",
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              "      --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-1d588d38-ac57-44c4-9b9d-e9e4ba8d7226 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 294,\n  \"fields\": [\n    {\n      \"column\": \"Unnamed: 0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 106,\n        \"min\": 0,\n        \"max\": 371,\n        \"num_unique_values\": 294,\n        \"samples\": [\n          46,\n          13,\n          195\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Subject ID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 150,\n        \"samples\": [\n          \"OAS2_0090\",\n          \"OAS2_0026\",\n          \"OAS2_0144\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MRI ID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 294,\n        \"samples\": [\n          \"OAS2_0026_MR2\",\n          \"OAS2_0008_MR1\",\n          \"OAS2_0092_MR2\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Group\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Nondemented\",\n          \"Demented\",\n          \"Converted\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Visit\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 2,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MR Delay\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 400,\n        \"min\": 0,\n        \"max\": 1707,\n        \"num_unique_values\": 129,\n        \"samples\": [\n          1393,\n          640\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"M/F\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"F\",\n          \"M\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Hand\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"R\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7,\n        \"min\": 60,\n        \"max\": 98,\n        \"num_unique_values\": 38,\n        \"samples\": [\n          65\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EDUC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 6,\n        \"max\": 23,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          23\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SES\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1280077276737763,\n        \"min\": 1.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MMSE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.413453785330679,\n        \"min\": 15.0,\n        \"max\": 30.0,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          27.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CDR\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.38134705502192395,\n        \"min\": 0.0,\n        \"max\": 2.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"eTIV\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 176,\n        \"min\": 1106,\n        \"max\": 2004,\n        \"num_unique_values\": 238,\n        \"samples\": [\n          1492\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"nWBV\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03737346494083379,\n        \"min\": 0.646,\n        \"max\": 0.837,\n        \"num_unique_values\": 129,\n        \"samples\": [\n          0.794\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ASF\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.13936485027255185,\n        \"min\": 0.876,\n        \"max\": 1.587,\n        \"num_unique_values\": 229,\n        \"samples\": [\n          1.274\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Explore the data with the various methods through the function 'explore data'\n",
        "def explore_data(df):\n",
        "    df.shape # Display shape of the DataFrame\n",
        "    print(f\"Data Shape: {df.shape}\\n\")\n",
        "\n",
        "    # Display information about the DataFrame\n",
        "    print(\"Data Info:\")\n",
        "    df.info()\n",
        "    print(\"\\n\")\n",
        "\n",
        "    # Display the number of duplicated rows\n",
        "    duplicated_count = df.duplicated().sum()\n",
        "    print(f\"Number of Duplicated Rows: {duplicated_count}\\n\")\n",
        "\n",
        "explore_data(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YB70EIr_zdpn",
        "outputId": "b2baac3d-ac62-497f-93bc-b167e2198fa0"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data Shape: (294, 16)\n",
            "\n",
            "Data Info:\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 294 entries, 0 to 293\n",
            "Data columns (total 16 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   Unnamed: 0  294 non-null    int64  \n",
            " 1   Subject ID  294 non-null    object \n",
            " 2   MRI ID      294 non-null    object \n",
            " 3   Group       294 non-null    object \n",
            " 4   Visit       294 non-null    int64  \n",
            " 5   MR Delay    294 non-null    int64  \n",
            " 6   M/F         294 non-null    object \n",
            " 7   Hand        294 non-null    object \n",
            " 8   Age         294 non-null    int64  \n",
            " 9   EDUC        294 non-null    int64  \n",
            " 10  SES         279 non-null    float64\n",
            " 11  MMSE        293 non-null    float64\n",
            " 12  CDR         294 non-null    float64\n",
            " 13  eTIV        294 non-null    int64  \n",
            " 14  nWBV        294 non-null    float64\n",
            " 15  ASF         294 non-null    float64\n",
            "dtypes: float64(5), int64(6), object(5)\n",
            "memory usage: 36.9+ KB\n",
            "\n",
            "\n",
            "Number of Duplicated Rows: 0\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Describe the dataframe\n",
        "df.describe(include=\"all\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "9kBOUAH1049x",
        "outputId": "e5cdd19d-9de4-4ae2-8a41-73f1c461be27"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Unnamed: 0 Subject ID         MRI ID        Group       Visit  \\\n",
              "count   294.000000        294            294          294  294.000000   \n",
              "unique         NaN        150            294            3         NaN   \n",
              "top            NaN  OAS2_0001  OAS2_0001_MR1  Nondemented         NaN   \n",
              "freq           NaN          2              1          142         NaN   \n",
              "mean    190.421769        NaN            NaN          NaN    1.489796   \n",
              "std     106.686740        NaN            NaN          NaN    0.500748   \n",
              "min       0.000000        NaN            NaN          NaN    1.000000   \n",
              "25%      99.000000        NaN            NaN          NaN    1.000000   \n",
              "50%     195.500000        NaN            NaN          NaN    1.000000   \n",
              "75%     282.750000        NaN            NaN          NaN    2.000000   \n",
              "max     371.000000        NaN            NaN          NaN    2.000000   \n",
              "\n",
              "           MR Delay  M/F Hand         Age        EDUC         SES        MMSE  \\\n",
              "count    294.000000  294  294  294.000000  294.000000  279.000000  293.000000   \n",
              "unique          NaN    2    1         NaN         NaN         NaN         NaN   \n",
              "top             NaN    F    R         NaN         NaN         NaN         NaN   \n",
              "freq            NaN  173  294         NaN         NaN         NaN         NaN   \n",
              "mean     349.785714  NaN  NaN   76.411565   14.561224    2.491039   27.259386   \n",
              "std      400.741520  NaN  NaN    7.607074    2.884818    1.128008    3.413454   \n",
              "min        0.000000  NaN  NaN   60.000000    6.000000    1.000000   15.000000   \n",
              "25%        0.000000  NaN  NaN   71.000000   12.000000    2.000000   26.000000   \n",
              "50%        0.000000  NaN  NaN   76.000000   14.500000    2.000000   29.000000   \n",
              "75%      671.500000  NaN  NaN   81.000000   16.000000    3.000000   30.000000   \n",
              "max     1707.000000  NaN  NaN   98.000000   23.000000    5.000000   30.000000   \n",
              "\n",
              "               CDR         eTIV        nWBV         ASF  \n",
              "count   294.000000   294.000000  294.000000  294.000000  \n",
              "unique         NaN          NaN         NaN         NaN  \n",
              "top            NaN          NaN         NaN         NaN  \n",
              "freq           NaN          NaN         NaN         NaN  \n",
              "mean      0.301020  1478.853741    0.731381    1.203109  \n",
              "std       0.381347   176.559755    0.037373    0.139365  \n",
              "min       0.000000  1106.000000    0.646000    0.876000  \n",
              "25%       0.000000  1347.250000    0.703000    1.118250  \n",
              "50%       0.000000  1461.500000    0.732000    1.201000  \n",
              "75%       0.500000  1569.000000    0.756000    1.302750  \n",
              "max       2.000000  2004.000000    0.837000    1.587000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a6c23ff7-939f-4aba-9534-8e67c013d986\" 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>Unnamed: 0</th>\n",
              "      <th>Subject ID</th>\n",
              "      <th>MRI ID</th>\n",
              "      <th>Group</th>\n",
              "      <th>Visit</th>\n",
              "      <th>MR Delay</th>\n",
              "      <th>M/F</th>\n",
              "      <th>Hand</th>\n",
              "      <th>Age</th>\n",
              "      <th>EDUC</th>\n",
              "      <th>SES</th>\n",
              "      <th>MMSE</th>\n",
              "      <th>CDR</th>\n",
              "      <th>eTIV</th>\n",
              "      <th>nWBV</th>\n",
              "      <th>ASF</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>294.000000</td>\n",
              "      <td>294</td>\n",
              "      <td>294</td>\n",
              "      <td>294</td>\n",
              "      <td>294.000000</td>\n",
              "      <td>294.000000</td>\n",
              "      <td>294</td>\n",
              "      <td>294</td>\n",
              "      <td>294.000000</td>\n",
              "      <td>294.000000</td>\n",
              "      <td>279.000000</td>\n",
              "      <td>293.000000</td>\n",
              "      <td>294.000000</td>\n",
              "      <td>294.000000</td>\n",
              "      <td>294.000000</td>\n",
              "      <td>294.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>NaN</td>\n",
              "      <td>150</td>\n",
              "      <td>294</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>1</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>NaN</td>\n",
              "      <td>OAS2_0001</td>\n",
              "      <td>OAS2_0001_MR1</td>\n",
              "      <td>Nondemented</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>F</td>\n",
              "      <td>R</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>142</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>173</td>\n",
              "      <td>294</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>190.421769</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.489796</td>\n",
              "      <td>349.785714</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>76.411565</td>\n",
              "      <td>14.561224</td>\n",
              "      <td>2.491039</td>\n",
              "      <td>27.259386</td>\n",
              "      <td>0.301020</td>\n",
              "      <td>1478.853741</td>\n",
              "      <td>0.731381</td>\n",
              "      <td>1.203109</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>106.686740</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.500748</td>\n",
              "      <td>400.741520</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7.607074</td>\n",
              "      <td>2.884818</td>\n",
              "      <td>1.128008</td>\n",
              "      <td>3.413454</td>\n",
              "      <td>0.381347</td>\n",
              "      <td>176.559755</td>\n",
              "      <td>0.037373</td>\n",
              "      <td>0.139365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1106.000000</td>\n",
              "      <td>0.646000</td>\n",
              "      <td>0.876000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>99.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1347.250000</td>\n",
              "      <td>0.703000</td>\n",
              "      <td>1.118250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>195.500000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>76.000000</td>\n",
              "      <td>14.500000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1461.500000</td>\n",
              "      <td>0.732000</td>\n",
              "      <td>1.201000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>282.750000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>671.500000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>81.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>1569.000000</td>\n",
              "      <td>0.756000</td>\n",
              "      <td>1.302750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>371.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1707.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>98.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2004.000000</td>\n",
              "      <td>0.837000</td>\n",
              "      <td>1.587000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a6c23ff7-939f-4aba-9534-8e67c013d986')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\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",
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              "      fill: #D2E3FC;\n",
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              "\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",
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              "  </style>\n",
              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-a6c23ff7-939f-4aba-9534-8e67c013d986 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
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              "        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",
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              "  </div>\n",
              "\n",
              "\n",
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              "    </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",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    border-radius: 50%;\n",
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              "    display: none;\n",
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              "    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-5f96c0ec-a1e3-4f43-850b-7aa988f4fe77 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 11,\n  \"fields\": [\n    {\n      \"column\": \"Unnamed: 0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 121.58785522205459,\n        \"min\": 0.0,\n        \"max\": 371.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          190.42176870748298,\n          195.5,\n          294.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Subject ID\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          150,\n          \"2\",\n          \"294\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MRI ID\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"294\",\n          \"OAS2_0001_MR1\",\n          \"1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Group\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          \"142\",\n          \"294\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Visit\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 103.49193309639895,\n        \"min\": 0.5007482038602737,\n        \"max\": 294.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1.489795918367347,\n          2.0,\n          0.5007482038602737\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MR Delay\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 569.3983831219463,\n        \"min\": 0.0,\n        \"max\": 1707.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          294.0,\n          349.7857142857143,\n          1707.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"M/F\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          \"173\",\n          \"294\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Hand\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"294\",\n          1,\n          \"R\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 84.47234096190336,\n        \"min\": 7.607074280796572,\n        \"max\": 294.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          76.41156462585035,\n          76.0,\n          294.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EDUC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 99.64267764878137,\n        \"min\": 2.8848184232457093,\n        \"max\": 294.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          14.561224489795919,\n          14.5,\n          294.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SES\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 97.81005648618417,\n        \"min\": 1.0,\n        \"max\": 279.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          279.0,\n          2.4910394265232974,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MMSE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 95.93029099796593,\n        \"min\": 3.413453785330679,\n        \"max\": 293.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          293.0,\n          27.25938566552901,\n          29.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CDR\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 103.7860547317582,\n        \"min\": 0.0,\n        \"max\": 294.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          294.0,\n          0.3010204081632653,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"eTIV\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 635.1814915649204,\n        \"min\": 176.55975514290927,\n        \"max\": 2004.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1478.8537414965986,\n          1461.5,\n          294.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"nWBV\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 103.72060413909273,\n        \"min\": 0.03737346494083379,\n        \"max\": 294.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.7313809523809525,\n          0.732,\n          294.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ASF\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 103.57042300007193,\n        \"min\": 0.13936485027255185,\n        \"max\": 294.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1.2031088435374149,\n          1.201,\n          294.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Explore the missing data\n",
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lcQpRbQK2mDP",
        "outputId": "48126303-5503-4584-98b9-21f947d5ea27"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Unnamed: 0     0\n",
              "Subject ID     0\n",
              "MRI ID         0\n",
              "Group          0\n",
              "Visit          0\n",
              "MR Delay       0\n",
              "M/F            0\n",
              "Hand           0\n",
              "Age            0\n",
              "EDUC           0\n",
              "SES           15\n",
              "MMSE           1\n",
              "CDR            0\n",
              "eTIV           0\n",
              "nWBV           0\n",
              "ASF            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Keep the relevant columns\n",
        "columns = [\"Subject ID\", \"Group\", \"Visit\", \"Age\", \"MMSE\", \"eTIV\", \"nWBV\", \"ASF\"]\n",
        "data = df[columns].copy()"
      ],
      "metadata": {
        "id": "ZVemv6M7hV0j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Ensure that the data types are correct\n",
        "data[\"Visit\"] = data[\"Visit\"].astype(str)\n",
        "data[\"eTIV\"] = data[\"eTIV\"].astype(float)\n",
        "data[\"MMSE\"] = data[\"MMSE\"].astype(float)"
      ],
      "metadata": {
        "id": "vJTUsQNhjBn3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a new categorical variable on the age groups\n",
        "age_group_mapping = {'60-69': 1, '70-79': 2, '80-89': 3, '90-99': 4}\n",
        "\n",
        "data['Agegroup'] = data['Age'].apply(lambda x: age_group_mapping['60-69'] if 60 <= x < 70 else\n",
        "                                    age_group_mapping['70-79'] if 70 <= x < 80 else\n",
        "                                    age_group_mapping['80-89'] if 80 <= x < 90 else\n",
        "                                    age_group_mapping['90-99'] if 90 <= x < 100 else None)\n",
        "data[\"Agegroup\"] = data[\"Agegroup\"].astype(str)"
      ],
      "metadata": {
        "id": "390JaZNOjB1y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Treat the missing values\n",
        "data.dropna(subset=['MMSE'], inplace=True)"
      ],
      "metadata": {
        "id": "NZTK9pbJjB6C"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create variable by extracting 'Subject Id'\n",
        "data['Subject'] = data['Subject ID'].str.extract('(\\d+)$').astype(int)"
      ],
      "metadata": {
        "id": "iZ8Dfp-HjKON"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a new dataframe and keep only relevant variables\n",
        "data = data[[\"Subject\", \"Group\", \"Visit\",\"MMSE\", \"Age\", \"Agegroup\", \"eTIV\", \"nWBV\", \"ASF\"]]\n",
        "data.head(10)"
      ],
      "metadata": {
        "id": "mLvVtn4xSEqW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "87e42d3b-0b9f-452f-8a30-0dcdbf895978"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Subject        Group Visit  MMSE  Age Agegroup    eTIV   nWBV    ASF\n",
              "0        1  Nondemented     1  27.0   87        3  1987.0  0.696  0.883\n",
              "1        1  Nondemented     2  30.0   88        3  2004.0  0.681  0.876\n",
              "2        2     Demented     1  23.0   75        2  1678.0  0.736  1.046\n",
              "3        2     Demented     2  28.0   76        2  1738.0  0.713  1.010\n",
              "4        4  Nondemented     1  28.0   88        3  1215.0  0.710  1.444\n",
              "5        4  Nondemented     2  27.0   90        4  1200.0  0.718  1.462\n",
              "6        5  Nondemented     1  28.0   80        3  1689.0  0.712  1.039\n",
              "7        5  Nondemented     2  29.0   83        3  1701.0  0.711  1.032\n",
              "8        7     Demented     1  28.0   71        2  1357.0  0.748  1.293\n",
              "9        8  Nondemented     1  30.0   93        4  1272.0  0.698  1.380"
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              "type": "dataframe",
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              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 293,\n  \"fields\": [\n    {\n      \"column\": \"Subject\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52,\n        \"min\": 1,\n        \"max\": 186,\n        \"num_unique_values\": 150,\n        \"samples\": [\n          90,\n          26,\n          144\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Group\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Nondemented\",\n          \"Demented\",\n          \"Converted\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Visit\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"2\",\n          \"1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MMSE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.413453785330679,\n        \"min\": 15.0,\n        \"max\": 30.0,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          27.0,\n          30.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7,\n        \"min\": 60,\n        \"max\": 98,\n        \"num_unique_values\": 38,\n        \"samples\": [\n          65,\n          98\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Agegroup\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"2\",\n          \"1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"eTIV\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 175.92664346131392,\n        \"min\": 1106.0,\n        \"max\": 2004.0,\n        \"num_unique_values\": 237,\n        \"samples\": [\n          1492.0,\n          1333.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"nWBV\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.037432230284104966,\n        \"min\": 0.646,\n        \"max\": 0.837,\n        \"num_unique_values\": 129,\n        \"samples\": [\n          0.794,\n          0.789\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ASF\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.13850681838645712,\n        \"min\": 0.876,\n        \"max\": 1.587,\n        \"num_unique_values\": 228,\n        \"samples\": [\n          1.273,\n          1.189\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Explarotory Data Analysis**"
      ],
      "metadata": {
        "id": "xJ76euceAm9U"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Vizualize the relationship between Mini-Mental State Examination score, which is a measure of cognitive function,\n",
        "# group of nondemented, demented, converted with points grouped by visitation\n",
        "\n",
        "# Set the figure size and style\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.set_style(\"white\")\n",
        "plt.gca().set_facecolor('white')\n",
        "\n",
        "# Create the point plot\n",
        "sns.pointplot(data=data, x='Group', y='MMSE', hue='Visit', dodge=True,\n",
        "\t      capsize=.1, errwidth=1, palette='colorblind')\n",
        "\n",
        "# Set the title of the plot, x and y axis\n",
        "plt.title('MMSE Score by Demented Group and Visitation', fontweight = 'bold')\n",
        "plt.xlabel('Demented Groups', fontweight = 'bold')\n",
        "plt.ylabel('Mini-Mental State Examination Score', fontweight = 'bold')\n",
        "\n",
        "# Remove spines and y-axis grid\n",
        "sns.despine(left=True)\n",
        "\n",
        "# Display\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 655
        },
        "id": "zOv_edvjTUW2",
        "outputId": "a7ef0926-0d64-4ff2-f5c1-9b60da0a2543"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-15-26a256a1726b>:9: FutureWarning: \n",
            "\n",
            "The `errwidth` parameter is deprecated. And will be removed in v0.15.0. Pass `err_kws={'linewidth': 1}` instead.\n",
            "\n",
            "  sns.pointplot(data=data, x='Group', y='MMSE', hue='Visit', dodge=True,\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Vizualize the relationship between estimated total intracranial volume\n",
        "# and  visit type grouped by age\n",
        "\n",
        "# Set the figure size and style\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.set_style(\"white\")\n",
        "\n",
        "# Create the plot\n",
        "sns.pointplot(data=data, x='Agegroup', y='eTIV', hue='Visit', dodge=True,\n",
        "\t      capsize=.1, errwidth=1, palette='flare')\n",
        "\n",
        "# Set the title of the plot, x and y axis\n",
        "plt.title('eTIV Score by Visit and Age group', fontweight = 'bold')\n",
        "plt.xlabel('Age Group', fontweight = 'bold')\n",
        "plt.ylabel('Estimated Total Intracranial Volume', fontweight = 'bold')\n",
        "\n",
        "# Remove the surrounding lines (spines) and y-axis grid\n",
        "sns.despine(left=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 655
        },
        "id": "-UYcU-30Uw8d",
        "outputId": "8e64b103-1b6b-4ebc-cefe-dfd5d66c70c7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-16-13283f2f6994>:9: FutureWarning: \n",
            "\n",
            "The `errwidth` parameter is deprecated. And will be removed in v0.15.0. Pass `err_kws={'linewidth': 1}` instead.\n",
            "\n",
            "  sns.pointplot(data=data, x='Agegroup', y='eTIV', hue='Visit', dodge=True,\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Vizualize the distribution for Mini-Mental State Examination Score\n",
        "\n",
        "# Set the figure size\n",
        "plt.figure(figsize=(10, 6))\n",
        "\n",
        "# Create the plot\n",
        "plt.hist(data['MMSE'], bins=10, edgecolor='black')\n",
        "\n",
        "# Set the labels and a title\n",
        "plt.xlabel('Mini-Mental State Examination Score', fontweight='bold')\n",
        "plt.ylabel('Frequency', fontweight='bold')\n",
        "plt.title('Distribution of Mini-Mental State Examination Score', fontweight='bold')\n",
        "\n",
        "# Display\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "71Q4_n4LYBl5",
        "outputId": "e97bca27-89aa-4b8a-c286-f36e528a343e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Vizualize Mini-Mental State Examination score by visit\n",
        "\n",
        "# Set the size\n",
        "plt.figure(figsize=(8, 6))\n",
        "\n",
        "# Create a boxplot\n",
        "sns.boxplot(x='Visit', y='MMSE', data=data, color='green')\n",
        "\n",
        "# Set label and title\n",
        "plt.xlabel('Visit',fontweight = 'bold')\n",
        "plt.ylabel('Mini-Mental State Examination score',fontweight = 'bold')\n",
        "plt.title('Mini-Mental State Examination score by Visit', fontweight = 'bold')\n",
        "\n",
        "# Display\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "UTjMDoOCjdm1",
        "outputId": "f566e7f6-cc54-4c7b-bfbc-c751d6075ab4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Vizualize estimated total intracranial volume by age group\n",
        "\n",
        "# Set figure size\n",
        "plt.figure(figsize=(8, 6))\n",
        "\n",
        "# Create a boxplot\n",
        "sns.boxplot(x='Agegroup', y='eTIV', data=data, color='orange')\n",
        "\n",
        "# Set label and title\n",
        "plt.xlabel('Age Group',fontweight = 'bold')\n",
        "plt.ylabel('Estimated Total Intracranial Volume',fontweight = 'bold')\n",
        "plt.title('Estimated Total Intracranial Volume by Age Group', fontweight = 'bold')\n",
        "\n",
        "# Display\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "QiqFrRwtdfYU",
        "outputId": "df0ec9f5-1503-4aa5-ba6e-c8f2082bfdf7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Model 1**"
      ],
      "metadata": {
        "id": "qtLiEFypJu5H"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Research question:** What is the effect of cognitive status and initial or subsequent visist on Mini-Mental State Examination (MMSE) scores among individuals aged 60 and above?\n",
        "\n",
        "\n",
        "**H0:** There is no significant interaction effect between cognitive status and visit on Mini-Mental State Examination (MMSE) scores.\n",
        "**H1:** There is a significant interaction effect between cognitive status and visit on MMSE scores.\n",
        "\n",
        "**H0:** There are no significant differences in MMSE scores among individuals with different cognitive statuses.\n",
        "**H1:** There are significant differences in MMSE scores among individuals with different cognitive statuses.\n",
        "\n",
        "**H0:** There are no significant differences in MMSE scores between initial and subsequent visit, regardless of cognitive status.\n",
        "**H1:** There are significant differences in MMSE scores between initial and subsequent visit, regardless of cognitive status.\n"
      ],
      "metadata": {
        "id": "lDK9B4HjwTDZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Group the date by selected variables and calculate MMSE means and std\n",
        "data.groupby(['Group', 'Visit'])['MMSE'].agg(['mean', 'std']).round(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "wC4b9kZkoFmM",
        "outputId": "3583f3d1-f795-4a14-af64-92824c1fc173"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    mean   std\n",
              "Group       Visit             \n",
              "Converted   1      29.36  0.93\n",
              "            2      28.00  2.09\n",
              "Demented    1      25.33  3.32\n",
              "            2      24.25  4.40\n",
              "Nondemented 1      29.19  0.85\n",
              "            2      29.11  0.96"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c7d5b81d-d6a1-42ad-8acf-1a3ffdc02412\" 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></th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Group</th>\n",
              "      <th>Visit</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Converted</th>\n",
              "      <th>1</th>\n",
              "      <td>29.36</td>\n",
              "      <td>0.93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>28.00</td>\n",
              "      <td>2.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Demented</th>\n",
              "      <th>1</th>\n",
              "      <td>25.33</td>\n",
              "      <td>3.32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>24.25</td>\n",
              "      <td>4.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Nondemented</th>\n",
              "      <th>1</th>\n",
              "      <td>29.19</td>\n",
              "      <td>0.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>29.11</td>\n",
              "      <td>0.96</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c7d5b81d-d6a1-42ad-8acf-1a3ffdc02412')\"\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-c7d5b81d-d6a1-42ad-8acf-1a3ffdc02412 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-c7d5b81d-d6a1-42ad-8acf-1a3ffdc02412');\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-3291bd05-fe00-4a3f-b647-70638bdffd64\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3291bd05-fe00-4a3f-b647-70638bdffd64')\"\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-3291bd05-fe00-4a3f-b647-70638bdffd64 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.2099592756428796,\n        \"min\": 24.25,\n        \"max\": 29.36,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          29.36,\n          28.0,\n          29.11\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.4838519692565921,\n        \"min\": 0.85,\n        \"max\": 4.4,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.93,\n          2.09,\n          0.96\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Compute the two-way mixed-design ANOVA\n",
        "pg.mixed_anova(data=data, dv=\"MMSE\", between=\"Group\", within=\"Visit\", subject=\"Subject\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "qixRurUylPnM",
        "outputId": "be507d9c-02cb-495a-cb58-b694c7ac8fbd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Source           SS  DF1  DF2          MS          F         p-unc  \\\n",
              "0        Group  1328.421218    2  140  664.210609  56.212233  1.201604e-18   \n",
              "1        Visit    22.377622    1  140   22.377622   8.859368  3.436529e-03   \n",
              "2  Interaction    17.000403    2  140    8.500201   3.365255  3.737081e-02   \n",
              "\n",
              "        np2  eps  \n",
              "0  0.445379  NaN  \n",
              "1  0.059515  1.0  \n",
              "2  0.045870  NaN  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-518913b5-dc23-44a6-854d-5a20e0f89fcd\" 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",
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              "\n",
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              "</style>\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Source</th>\n",
              "      <th>SS</th>\n",
              "      <th>DF1</th>\n",
              "      <th>DF2</th>\n",
              "      <th>MS</th>\n",
              "      <th>F</th>\n",
              "      <th>p-unc</th>\n",
              "      <th>np2</th>\n",
              "      <th>eps</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Group</td>\n",
              "      <td>1328.421218</td>\n",
              "      <td>2</td>\n",
              "      <td>140</td>\n",
              "      <td>664.210609</td>\n",
              "      <td>56.212233</td>\n",
              "      <td>1.201604e-18</td>\n",
              "      <td>0.445379</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Visit</td>\n",
              "      <td>22.377622</td>\n",
              "      <td>1</td>\n",
              "      <td>140</td>\n",
              "      <td>22.377622</td>\n",
              "      <td>8.859368</td>\n",
              "      <td>3.436529e-03</td>\n",
              "      <td>0.059515</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Interaction</td>\n",
              "      <td>17.000403</td>\n",
              "      <td>2</td>\n",
              "      <td>140</td>\n",
              "      <td>8.500201</td>\n",
              "      <td>3.365255</td>\n",
              "      <td>3.737081e-02</td>\n",
              "      <td>0.045870</td>\n",
              "      <td>NaN</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\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",
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              "      border-radius: 50%;\n",
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              "      display: none;\n",
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              "      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-518913b5-dc23-44a6-854d-5a20e0f89fcd 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-518913b5-dc23-44a6-854d-5a20e0f89fcd');\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-68160ddf-c86b-4ea1-b5b8-946bb227a3ee\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-68160ddf-c86b-4ea1-b5b8-946bb227a3ee')\"\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",
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            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Model 1: Assumption Check**"
      ],
      "metadata": {
        "id": "91KsMhuUskB6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Check assumption 1 on sphericity\n",
        "pg.sphericity(data=data, dv=\"MMSE\", within=\"Visit\", subject=\"Subject\")[-1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4ptyDUCwsjB9",
        "outputId": "c0e53587-95c0-46f0-9b51-388ced065009"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check assumption 2 on normality\n",
        "pg.normality(data, dv=\"MMSE\", group = \"Group\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "1ZGKrktzs7ip",
        "outputId": "7a077237-55f6-4c6b-ff01-d237e6566c56"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    W          pval  normal\n",
              "Group                                      \n",
              "Nondemented  0.809528  2.635762e-12   False\n",
              "Demented     0.929083  5.677786e-06   False\n",
              "Converted    0.770938  5.867723e-05   False"
            ],
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            }
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check assumption 3 on homogeneity\n",
        "\n",
        "print(pg.homoscedasticity(data[data['Visit'] == \"1\"], dv=\"MMSE\", group=\"Group\"))\n",
        "print(pg.homoscedasticity(data[data['Visit'] == \"2\"], dv=\"MMSE\", group=\"Group\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7DAKZDnytEGV",
        "outputId": "99a53a9f-04bd-4c87-e7d7-344e3adeba10"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "               W          pval  equal_var\n",
            "levene  36.89391  1.036701e-13      False\n",
            "               W          pval  equal_var\n",
            "levene  33.69637  1.130873e-12      False\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Model 2**"
      ],
      "metadata": {
        "id": "E0TeJa-jk6Cm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Research question:** What is the impact of age group and initial or subsequent visit  on estimated Total Intracranial Volume (eTIV) among individuals over 60?\n",
        "\n",
        "**H0:**There is no significant interaction effect between age group and visit on eTIV.\n",
        "**H1:** There is a significant interaction effect between age group and visit on eTIV.\n",
        "\n",
        "**H0:**There are no significant differences in eTIV among individuals belonging to different age groups.\n",
        "**H1:**There are significant differences in eTIV among individuals belonging to different age groups.\n",
        "\n",
        "**H0:**There are no significant differences in eTIV between initial and subsequent visits, regardless of age group.\n",
        "**H1:** There are significant differences in eTIV between initial and subsequent visits, regardless of age group.\n"
      ],
      "metadata": {
        "id": "AytYey5_qA6U"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Group the date by selected variables and calculate eTIV means and std\n",
        "data.groupby(['Agegroup', 'Visit'])['eTIV'].agg(['mean', 'std']).round(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "id": "xOWfSeZ8wTio",
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      },
      "execution_count": null,
      "outputs": [
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              "                   mean     std\n",
              "Agegroup Visit                 \n",
              "1        1      1457.91  155.20\n",
              "         2      1451.61  154.59\n",
              "2        1      1483.17  177.54\n",
              "         2      1504.72  186.66\n",
              "3        1      1482.73  190.95\n",
              "         2      1485.76  174.12\n",
              "4        1      1374.50   99.29\n",
              "         2      1445.64  189.59"
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              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 40.18460508346805,\n        \"min\": 1374.5,\n        \"max\": 1504.72,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1451.61,\n          1485.76,\n          1457.91\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 30.47712010672924,\n        \"min\": 99.29,\n        \"max\": 190.95,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          154.59,\n          174.12,\n          155.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Compute the two-way mixed-design ANOVA\n",
        "pg.mixed_anova(data=data, dv=\"eTIV\", between=\"Agegroup\", within=\"Visit\", subject=\"Subject\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
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        "id": "NgZ-GxXSU2Au",
        "outputId": "7b413df0-a0db-4770-f9fe-1e23d4bf271e"
      },
      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "        Source             SS  DF1  DF2            MS         F     p-unc  \\\n",
              "0     Agegroup  160469.899747    3  109  53489.966582  0.824793  0.482953   \n",
              "1        Visit    1875.225664    1  109   1875.225664  4.558583  0.034994   \n",
              "2  Interaction     517.864438    3  109    172.621479  0.419634  0.739272   \n",
              "\n",
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              "                                                    [key], {});\n",
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              "        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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              "    20% {\n",
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              "      border-right-color: var(--fill-color);\n",
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              "    }\n",
              "  }\n",
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              "\n",
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            }
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Model 2: Assumption Check**"
      ],
      "metadata": {
        "id": "q2TBajmQuGSd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Check assumption 1 on sphericity\n",
        "pg.sphericity(data=data, dv=\"eTIV\", within=\"Visit\", subject=\"Subject\")[-1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AT0dJR8buQ-j",
        "outputId": "585ea2cb-a7f5-4d55-8cda-0a887ef54037"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check assumption 2 on normality\n",
        "pg.normality(data, dv=\"eTIV\", group = \"Agegroup\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "F6xVpkZkuRIh",
        "outputId": "b072aa75-5e75-497c-8b52-6250cf2c866f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 W      pval  normal\n",
              "Agegroup                            \n",
              "3         0.969872  0.041868   False\n",
              "2         0.973761  0.010350   False\n",
              "4         0.943061  0.422421    True\n",
              "1         0.896050  0.000140   False"
            ],
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"pg\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"Agegroup\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"2\",\n          \"1\",\n          \"3\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"W\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03579492926534665,\n        \"min\": 0.8960495591163635,\n        \"max\": 0.9737613201141357,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.9737613201141357,\n          0.8960495591163635,\n          0.9698715209960938\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pval\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.20326146431242323,\n        \"min\": 0.0001397859159624204,\n        \"max\": 0.4224206805229187,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.010350196622312069,\n          0.0001397859159624204,\n          0.041867632418870926\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"normal\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check assumption 3 on homogeneity\n",
        "\n",
        "print(pg.homoscedasticity(data[data['Visit'] == \"1\"], dv=\"eTIV\", group=\"Agegroup\"))\n",
        "print(pg.homoscedasticity(data[data['Visit'] == \"2\"], dv=\"eTIV\", group=\"Agegroup\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fFj8AyLYuRL9",
        "outputId": "2b2286b7-c788-4d3d-992c-a6e819965dcf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "               W     pval  equal_var\n",
            "levene  1.102045  0.35038       True\n",
            "               W      pval  equal_var\n",
            "levene  0.530752  0.661914       True\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Sample size**"
      ],
      "metadata": {
        "id": "ID7pb5jWvmQo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Perform power analysis\n",
        "power_analysis = smp.TTestIndPower()\n",
        "\n",
        "# Identify sample size\n",
        "sample_size = power_analysis.solve_power(effect_size=0.7, power=0.91, alpha=0.05)\n",
        "\n",
        "print(\"Sample Size:\", round(sample_size, 0))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t_fSY7M2xkRS",
        "outputId": "3d006074-9146-4c6e-8b26-939fc8e86c53"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sample Size: 45.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot power analysis\n",
        "fig, ax = plt.subplots()\n",
        "power_analysis.plot_power(dep_var='nobs', nobs=np.arange(2, 100), effect_size=[0.7], alpha=0.05, ax=ax)\n",
        "ax.set_title('Power Analysis')\n",
        "ax.set_xlabel('Sample Size')\n",
        "ax.set_ylabel('Power')\n",
        "ax.grid(True)\n",
        "\n",
        "# Add the vertical line at sample size 45\n",
        "ax.axvline(x=45, color='r', linestyle='--', label='Sample Size = 45')\n",
        "ax.legend()\n",
        "\n",
        "# Display\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "3YVL0I752Y55",
        "outputId": "903c937a-46de-4fed-e3cb-5d1ae5cee894"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}