OLS_2_Ajuste_Propiedades_Test_de_Hipótesis.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "OLS 2 Ajuste Propiedades Test de Hipótesis.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMNyKMqsewf+asbzYlyT41y",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/rpasquini/econometria-iae/blob/main/OLS_2_Ajuste_Propiedades_Test_de_Hip%C3%B3tesis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NNmORTIw0Z_t"
      },
      "source": [
        "import statsmodels.api as sm\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "El objetivo de este notebook es introducir a la estimación del modelo de regresión lineal, y aplicar conceptos relacionados con predicción y análisis de residuos estimados"
      ],
      "metadata": {
        "id": "B5EmVVIGBnzZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Nuestro primer objetivo será estimar el modelo:\n",
        "\n",
        "$$ ingreso_i=\\beta_0+\\beta_1 aesc_i+\\epsilon_i $$\n",
        "\n",
        "donde ingreso es el ingreso total individual y aesc son los años de escolaridad.\n"
      ],
      "metadata": {
        "id": "GvVNPeIkCFrl"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OTliy8gp0cuf"
      },
      "source": [
        "df=pd.read_csv('https://github.com/rpasquini/econometria-iae/blob/main/datos/eah2022_bu_ampliada_ind.txt?raw=true',sep=';')"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "gnEnavpyAvuG",
        "outputId": "bfe8f04c-72a1-4e45-bce3-8a117242140b"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         id  nhogar  miembro  comuna  dominio  edad  sexo  parentes_2  p5_2  \\\n",
              "0         1       1        1       8        3    18     2           1     6   \n",
              "1         1       1        2       8        3    22     1           7     6   \n",
              "2         1       1        3       8        3    45     1           6     3   \n",
              "3         2       1        1       5        4    18     2           1     6   \n",
              "4         3       1        1       2        4    18     1           1     6   \n",
              "...     ...     ...      ...     ...      ...   ...   ...         ...   ...   \n",
              "12496  5373       1        1       8        4    95     2           1     4   \n",
              "12497  5374       1        1       7        4    98     2           1     4   \n",
              "12498  5374       1        2       7        4    76     2           7     3   \n",
              "12499  5375       1        1      15        4   100     2           1     4   \n",
              "12500  5375       1        2      15        4    50     2           9     3   \n",
              "\n",
              "       p6_a  ...  t50d_a  t50d_b  t50d_c  t50d_d  t50d_e  t50d_f  t51_d  \\\n",
              "0         3  ...       0       0       0       0       0       0      0   \n",
              "1         3  ...       0       0       0       0       0       0      0   \n",
              "2         0  ...       0       0       0       0       0       0      0   \n",
              "3        95  ...       0       0       0       0       0       0      0   \n",
              "4        95  ...       0       0       0       0       0       0      0   \n",
              "...     ...  ...     ...     ...     ...     ...     ...     ...    ...   \n",
              "12496     0  ...       0       0       0       0       0       0      0   \n",
              "12497     0  ...       0       0       0       0       0       0      0   \n",
              "12498     0  ...       0       0       0       0       0       0      0   \n",
              "12499     0  ...       0       0       0       0       0       0      0   \n",
              "12500     0  ...       0       0       0       0       0       0      0   \n",
              "\n",
              "       t23_1_cod  t24_1_cod  fexp  \n",
              "0              0          0    16  \n",
              "1              0          0    16  \n",
              "2              0          0    16  \n",
              "3              0          0   357  \n",
              "4              0          0   106  \n",
              "...          ...        ...   ...  \n",
              "12496          0          0    40  \n",
              "12497          0          0   205  \n",
              "12498          0          0   205  \n",
              "12499          0          0   165  \n",
              "12500          0          0   165  \n",
              "\n",
              "[12501 rows x 140 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7918c078-e684-4edc-beef-2527c8d73326\" 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>id</th>\n",
              "      <th>nhogar</th>\n",
              "      <th>miembro</th>\n",
              "      <th>comuna</th>\n",
              "      <th>dominio</th>\n",
              "      <th>edad</th>\n",
              "      <th>sexo</th>\n",
              "      <th>parentes_2</th>\n",
              "      <th>p5_2</th>\n",
              "      <th>p6_a</th>\n",
              "      <th>...</th>\n",
              "      <th>t50d_a</th>\n",
              "      <th>t50d_b</th>\n",
              "      <th>t50d_c</th>\n",
              "      <th>t50d_d</th>\n",
              "      <th>t50d_e</th>\n",
              "      <th>t50d_f</th>\n",
              "      <th>t51_d</th>\n",
              "      <th>t23_1_cod</th>\n",
              "      <th>t24_1_cod</th>\n",
              "      <th>fexp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>18</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>22</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>45</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>18</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>95</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>357</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>18</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>95</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>106</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12496</th>\n",
              "      <td>5373</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "      <td>95</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12497</th>\n",
              "      <td>5374</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>4</td>\n",
              "      <td>98</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>205</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12498</th>\n",
              "      <td>5374</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>4</td>\n",
              "      <td>76</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>205</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12499</th>\n",
              "      <td>5375</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>15</td>\n",
              "      <td>4</td>\n",
              "      <td>100</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>165</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12500</th>\n",
              "      <td>5375</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>15</td>\n",
              "      <td>4</td>\n",
              "      <td>50</td>\n",
              "      <td>2</td>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>165</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>12501 rows × 140 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7918c078-e684-4edc-beef-2527c8d73326')\"\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",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </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-7918c078-e684-4edc-beef-2527c8d73326 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-7918c078-e684-4edc-beef-2527c8d73326');\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-a62a3062-2d75-43a5-8df2-713f2715fa7b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a62a3062-2d75-43a5-8df2-713f2715fa7b')\"\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-a62a3062-2d75-43a5-8df2-713f2715fa7b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A2djnzeh2ZKi",
        "outputId": "9d7fe6b5-d07d-4391-9181-0307b11ac7aa"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['id', 'nhogar', 'miembro', 'comuna', 'dominio', 'edad', 'sexo',\n",
              "       'parentes_2', 'p5_2', 'p6_a',\n",
              "       ...\n",
              "       't50d_a', 't50d_b', 't50d_c', 't50d_d', 't50d_e', 't50d_f', 't51_d',\n",
              "       't23_1_cod', 't24_1_cod', 'fexp'],\n",
              "      dtype='object', length=140)"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['ingreso']=df['ingtot_2']"
      ],
      "metadata": {
        "id": "2SxzcPBT8ffC"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df=df[df['ingreso']>0]"
      ],
      "metadata": {
        "id": "hWsI8i1B_A2R"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['ingreso'].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LhPsAJKQ80k6",
        "outputId": "9a881cd1-3acf-4901-f82a-0a9ab7e8b0d7"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    9.105000e+03\n",
              "mean     1.294673e+05\n",
              "std      1.175164e+05\n",
              "min      5.000000e+02\n",
              "25%      5.030000e+04\n",
              "50%      1.000000e+05\n",
              "75%      1.700000e+05\n",
              "max      2.000000e+06\n",
              "Name: ingreso, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['ingreso'].hist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "id": "C6P675cr9xqe",
        "outputId": "7db61aeb-a591-4be6-9634-aac39318aca2"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 47
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['aesc']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Oj9doxS_ApWi",
        "outputId": "d1d4220a-889b-4eb7-f598-199f0a21855b"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        11\n",
              "1        12\n",
              "2         9\n",
              "3        12\n",
              "4        12\n",
              "         ..\n",
              "12496     0\n",
              "12497     3\n",
              "12498     3\n",
              "12499     7\n",
              "12500    12\n",
              "Name: aesc, Length: 9105, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['aesc'].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wngx4OdUACwE",
        "outputId": "4c101a2a-653a-49a3-bb74-46eb0050fb32"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    9105.000000\n",
              "mean       13.584734\n",
              "std         6.957779\n",
              "min         0.000000\n",
              "25%        12.000000\n",
              "50%        13.000000\n",
              "75%        17.000000\n",
              "max        99.000000\n",
              "Name: aesc, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qVD7lheoveIN"
      },
      "source": [
        "df=df.loc[df.aesc<90]"
      ],
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X2MRv2sjvlKK",
        "outputId": "1ad7cb2b-c3d8-4428-b89f-1dec454aaa72",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        }
      },
      "source": [
        "df.aesc.hist(bins=40)"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 51
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Estimar la regresion va a requerir varios pasos que me tengo que asegurar que se cumplan:\n",
        "\n",
        "1. Asegurarme de que los datos (las filas en cuestion) que voy a estar usando no tienen datos missing en ninguna variable.\n",
        "2. Separar a la variable a explicar (dependiente) de la(s) explicativas\n",
        "3. Si la regresión va a tener una constante, como en nuestro caso, agregar a las variables explicativas una columna de 1s (unos).\n",
        "4. Definir el modelo en una variable sm.OLS\n",
        "5. Correr el modelo con el metodo fit() y guardar los resultados en otra variable\n",
        "6. Imprimir los resultados en pantalla (imprimir los contenidos de la variable del paso anterior)"
      ],
      "metadata": {
        "id": "bwXTZnxgCyfd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Altenativa 1:\n",
        "Defino el modelo con sm.OLS\n",
        "Agrego la constante usando sm.add_constant\n",
        "Uso la opción \"drop\" para eliminar filas con missing values"
      ],
      "metadata": {
        "id": "CpkVU_sCEooE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "modelo=sm.OLS(df['ingreso'],sm.add_constant(df['aesc']), missing='drop')\n"
      ],
      "metadata": {
        "id": "95pSPoz5C61f"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "resultados=modelo.fit()\n",
        "print(resultados.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7Z1Ewo4iEPwL",
        "outputId": "9fbb9d23-26aa-46d7-eb36-57ad0f6a3399"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                ingreso   R-squared:                       0.176\n",
            "Model:                            OLS   Adj. R-squared:                  0.176\n",
            "Method:                 Least Squares   F-statistic:                     1930.\n",
            "Date:                Mon, 23 Oct 2023   Prob (F-statistic):               0.00\n",
            "Time:                        20:42:38   Log-Likelihood:            -1.1779e+05\n",
            "No. Observations:                9062   AIC:                         2.356e+05\n",
            "Df Residuals:                    9060   BIC:                         2.356e+05\n",
            "Df Model:                           1                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const      -4.489e+04   4129.920    -10.868      0.000    -5.3e+04   -3.68e+04\n",
            "aesc        1.325e+04    301.567     43.937      0.000    1.27e+04    1.38e+04\n",
            "==============================================================================\n",
            "Omnibus:                     7371.512   Durbin-Watson:                   1.790\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           361472.985\n",
            "Skew:                           3.555   Prob(JB):                         0.00\n",
            "Kurtosis:                      33.113   Cond. No.                         50.6\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Alternativa 2: Usando sintaxis de estilo R\n",
        "* Por default agrega la constante\n",
        "* Por defaul elimina los missing"
      ],
      "metadata": {
        "id": "jDONsgMyFEwt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import statsmodels.formula.api as smf"
      ],
      "metadata": {
        "id": "d-eZcfgwE78M"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mod = smf.ols(formula='ingreso ~ aesc', data=df, )\n",
        "\n",
        "res = mod.fit()\n",
        "\n",
        "print(res.summary())\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V7oLYfSXErRO",
        "outputId": "ab92974e-8852-44ed-d746-bc435a9494d2"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                ingreso   R-squared:                       0.176\n",
            "Model:                            OLS   Adj. R-squared:                  0.176\n",
            "Method:                 Least Squares   F-statistic:                     1930.\n",
            "Date:                Mon, 23 Oct 2023   Prob (F-statistic):               0.00\n",
            "Time:                        20:42:38   Log-Likelihood:            -1.1779e+05\n",
            "No. Observations:                9062   AIC:                         2.356e+05\n",
            "Df Residuals:                    9060   BIC:                         2.356e+05\n",
            "Df Model:                           1                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "Intercept  -4.489e+04   4129.920    -10.868      0.000    -5.3e+04   -3.68e+04\n",
            "aesc        1.325e+04    301.567     43.937      0.000    1.27e+04    1.38e+04\n",
            "==============================================================================\n",
            "Omnibus:                     7371.512   Durbin-Watson:                   1.790\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           361472.985\n",
            "Skew:                           3.555   Prob(JB):                         0.00\n",
            "Kurtosis:                      33.113   Cond. No.                         50.6\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4jyDpmY40I8-",
        "outputId": "d5b69ecf-627a-4f93-bda1-8d0188afbaa0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "resultados.params['aesc']"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "13249.928839499735"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Missing values"
      ],
      "metadata": {
        "id": "rzDQ8b852JNR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Una manera alternativa de eliminar filas con missing values es utilizando el metodo dropna()"
      ],
      "metadata": {
        "id": "McDc67rs18zj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dfmodel=df[['ingreso','aesc']].dropna()"
      ],
      "metadata": {
        "id": "-7i9PZzg08CT"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S9dlbmRICHfI"
      },
      "source": [
        "# Predicción"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ONHB57OpCWiR"
      },
      "source": [
        "$\\hat{Y}=\\hat{\\beta_0}+\\hat{\\beta_1}*X$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "la4qtvGUCNgJ",
        "outputId": "8dcd6537-4d7a-4562-8892-a9f06548874c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# 10 años de escolaridad\n",
        "-4.489e+04+1.325e+04*10"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "87610.0"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G60RKQOvCrW5",
        "outputId": "0e4c1c45-8565-4f12-8dbb-cd51c2ae954b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# 10 años de escolaridad levantando los parametros de los resultados del ajuste\n",
        "resultados.params['const']+resultados.params['aesc']*10"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "87614.1083950932"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8PzA4iHtDLSw",
        "outputId": "008044a6-cd8e-41f4-8b2b-34eb8623f60c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Predicciones para observaciones en la data\n",
        "resultados.params['const']+resultados.params['aesc']*dfmodel.aesc"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        100864.037235\n",
              "1        114113.966074\n",
              "2         74364.179556\n",
              "3        114113.966074\n",
              "4        114113.966074\n",
              "             ...      \n",
              "12496    -44885.180000\n",
              "12497     -5135.393481\n",
              "12498     -5135.393481\n",
              "12499     47864.321877\n",
              "12500    114113.966074\n",
              "Name: aesc, Length: 9062, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mahOOQFj0Thc",
        "outputId": "3956c1ef-c947-4b44-fd31-cb373e258240",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "resultados.predict()"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([100864.03723459, 114113.96607409,  74364.17955559, ...,\n",
              "        -5135.3934814 ,  47864.32187659, 114113.96607409])"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NWU5mDsvDhBf",
        "outputId": "a07f7e7e-f283-4bc3-8250-f22f00f91389",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "resultados.predict()"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([100864.03723459, 114113.96607409,  74364.17955559, ...,\n",
              "        -5135.3934814 ,  47864.32187659, 114113.96607409])"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iMdWCqIh1Lm4",
        "outputId": "5c0e721b-3e04-4ada-c035-d6dad2782ad6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "dfmodel.ingreso-resultados.predict()"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        -77364.037235\n",
              "1       -106113.966074\n",
              "2         15635.820444\n",
              "3        -74113.966074\n",
              "4        -94113.966074\n",
              "             ...      \n",
              "12496    130885.180000\n",
              "12497     86135.393481\n",
              "12498     49635.393481\n",
              "12499     62135.678123\n",
              "12500    -39113.966074\n",
              "Name: ingreso, Length: 9062, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cw17S2gYDrhp",
        "outputId": "c61937e1-70e6-4e22-804c-d49d0b8d5674",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        }
      },
      "source": [
        "# Ploteamos prediccion con matplolib\n",
        "plt.scatter(dfmodel.aesc, resultados.predict())"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7eae4ce5a140>"
            ]
          },
          "metadata": {},
          "execution_count": 64
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MIFvCC4syPnZ",
        "outputId": "d196bc18-5db5-4fd7-fcc9-ec93a16f1820",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        }
      },
      "source": [
        "plt.scatter(dfmodel.aesc, dfmodel.ingreso, color='green')\n",
        "\n",
        "plt.plot(dfmodel.aesc, resultados.predict(), color= \"red\", marker='.', linestyle=':')\n",
        "\n",
        "plt.xlabel('Escolaridad')\n",
        "\n",
        "plt.ylabel('Ingreso')\n",
        "\n",
        "plt.title('Data y valores ajustados')\n",
        "\n",
        "#plt.ylim(0,15)\n"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Data y valores ajustados')"
            ]
          },
          "metadata": {},
          "execution_count": 65
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QuoymAZp3_tU",
        "outputId": "a259e19b-80f5-4233-bc19-167e3d16f9ab",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "resultados.predict()"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([100864.03723459, 114113.96607409,  74364.17955559, ...,\n",
              "        -5135.3934814 ,  47864.32187659, 114113.96607409])"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QZRn9Kqu4Kyf"
      },
      "source": [
        "# Error Cuadrático Medio\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kF_enYAB1f8Z",
        "outputId": "2c150fca-1375-48e6-f6eb-08c3b9fc0355",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "resultados.mse_resid"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11415526141.572683"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hl56Qpf43A49"
      },
      "source": [
        "Error Cuadrático Medio (MSE) a mano\n",
        "\n",
        "$MSE=\\sqrt{\\frac{\\sum^n_{i=1} e_i^2}{n-2}}$ donde\n",
        "\n",
        "$e=y-\\hat{y}$\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uQngrpST12tx",
        "outputId": "c8550df7-8a3c-4db2-e3a8-00acb742d0bb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "ypred = resultados.predict()\n",
        "residuos=dfmodel['ingreso']-ypred\n",
        "rss=np.sum(residuos**2)\n",
        "MSE=(rss/(resultados.nobs-2))\n",
        "MSE"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11415526141.57268"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MLzHND1dJzv8",
        "outputId": "2d4fa023-1ce2-4e25-9127-d61770a28648",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "MSE**0.5"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "106843.46560072206"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l8MelTxsLrzq"
      },
      "source": [
        "# Cómo son los residuos (errores estimados) en mis datos?\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n4sZ7Av3LnnM",
        "outputId": "bb59622b-a9bd-4998-8e4e-982ed73ccfd7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        }
      },
      "source": [
        "residuos.hist()"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 70
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import statsmodels.api as sm\n"
      ],
      "metadata": {
        "id": "vV4FGZ-PK2Gm"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Assuming 'res' is your fitted regression model\n",
        "residuals = res.resid\n",
        "fitted_values = res.fittedvalues\n",
        "\n",
        "plt.scatter(fitted_values, residuals)\n",
        "plt.xlabel('Fitted Values')\n",
        "plt.ylabel('Residuals')\n",
        "plt.title('Residuals vs Fitted Values')\n",
        "plt.axhline(y=0, color='gray', linestyle='--')  # Add horizontal line at y=0\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "S12oRq0WKlc6",
        "outputId": "f3c6bd7c-d06f-486b-ebca-62492b2ddada",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        }
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Test de White\n",
        "Utilizamos p-value del LM-Test  que es el  p-value para la hipotesis nula de que la varianza del error es homocedastica\n"
      ],
      "metadata": {
        "id": "bJ-naTj4L9QV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from statsmodels.stats.diagnostic import het_white\n",
        "from statsmodels.compat import lzip\n",
        "\n",
        "# Assuming 'model' is your fitted regression model\n",
        "white_test_results = het_white(res.resid, res.model.exog)\n",
        "\n",
        "print(\"White Test Results:\")\n",
        "print(\"LM Statistic:\", white_test_results[0])\n",
        "print(\"LM-Test p-value:\", white_test_results[1])\n"
      ],
      "metadata": {
        "id": "F6QPyK4ILaKZ",
        "outputId": "a5a85994-ac4c-4c44-cc13-b952fb0d120b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "White Test Results:\n",
            "LM Statistic: 150.3665607670011\n",
            "LM-Test p-value: 2.2300582033825483e-33\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Estimación Regresion con varianza robusta\n",
        "\n",
        "Para los ajustes a los errores estándares por heterocedasticidad, usamos los errores robustos tipicamente denotados HC0-HC3\n",
        "https://jslsoc.sitehost.iu.edu/files_research/testing_tests/hccm/00TAS.pdf"
      ],
      "metadata": {
        "id": "bHl2quhBJzmi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mod = smf.ols(formula='ingreso ~ aesc', data=df, )\n",
        "\n",
        "res = mod.fit(cov_type='HC0')\n",
        "\n",
        "print(res.summary())\n"
      ],
      "metadata": {
        "id": "DR8pXlMiJEm3",
        "outputId": "feb4d8ae-5a50-46e3-f5a0-21d29e52d8c1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                ingreso   R-squared:                       0.176\n",
            "Model:                            OLS   Adj. R-squared:                  0.176\n",
            "Method:                 Least Squares   F-statistic:                     1609.\n",
            "Date:                Mon, 23 Oct 2023   Prob (F-statistic):          4.94e-324\n",
            "Time:                        20:42:39   Log-Likelihood:            -1.1779e+05\n",
            "No. Observations:                9062   AIC:                         2.356e+05\n",
            "Df Residuals:                    9060   BIC:                         2.356e+05\n",
            "Df Model:                           1                                         \n",
            "Covariance Type:                  HC0                                         \n",
            "==============================================================================\n",
            "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "Intercept  -4.489e+04   3867.026    -11.607      0.000   -5.25e+04   -3.73e+04\n",
            "aesc        1.325e+04    330.296     40.115      0.000    1.26e+04    1.39e+04\n",
            "==============================================================================\n",
            "Omnibus:                     7371.512   Durbin-Watson:                   1.790\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           361472.985\n",
            "Skew:                           3.555   Prob(JB):                         0.00\n",
            "Kurtosis:                      33.113   Cond. No.                         50.6\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors are heteroscedasticity robust (HC0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mod = smf.ols(formula='ingreso ~ aesc', data=df, )\n",
        "\n",
        "res = mod.fit(cov_type='HC1')\n",
        "\n",
        "print(res.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HYH-44nR4UE1",
        "outputId": "5f656246-7010-49ee-dd3e-d878912fc3a7"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                ingreso   R-squared:                       0.176\n",
            "Model:                            OLS   Adj. R-squared:                  0.176\n",
            "Method:                 Least Squares   F-statistic:                     1609.\n",
            "Date:                Mon, 23 Oct 2023   Prob (F-statistic):          4.94e-324\n",
            "Time:                        20:42:40   Log-Likelihood:            -1.1779e+05\n",
            "No. Observations:                9062   AIC:                         2.356e+05\n",
            "Df Residuals:                    9060   BIC:                         2.356e+05\n",
            "Df Model:                           1                                         \n",
            "Covariance Type:                  HC1                                         \n",
            "==============================================================================\n",
            "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "Intercept  -4.489e+04   3867.453    -11.606      0.000   -5.25e+04   -3.73e+04\n",
            "aesc        1.325e+04    330.333     40.111      0.000    1.26e+04    1.39e+04\n",
            "==============================================================================\n",
            "Omnibus:                     7371.512   Durbin-Watson:                   1.790\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           361472.985\n",
            "Skew:                           3.555   Prob(JB):                         0.00\n",
            "Kurtosis:                      33.113   Cond. No.                         50.6\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors are heteroscedasticity robust (HC1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Revisamos el supuesto de normalidad"
      ],
      "metadata": {
        "id": "wWf7HRVFCCin"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "residuos.hist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "9VmIWAFMCFqg",
        "outputId": "49e912f1-2d3b-40e2-cc81-af2cc08f6340"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 76
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjAAAAGwCAYAAAC3qV8qAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA1CklEQVR4nO3de3RU5b3/8U8SkgkBJjEgCSmQBq1IuF9KmHopSEigqUsqbUURcxTkaINtSBXlHEUutlwEATVKW5HgqYjQiiIgJIKBIuHSQNpwKQVNixYntCKEi0yGZP/+cGX/GHMhEwjJM75fa80i8+zv3vN88+TyYe+ZTJBlWZYAAAAMEtzUEwAAAPAXAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAME6Lpp5AY6msrNSxY8fUpk0bBQUFNfV0AABAPViWpdOnTysuLk7BwXWcZ7H89Omnn1pjxoyxoqOjrfDwcKtHjx7W7t277e2VlZXWU089ZcXGxlrh4eHW0KFDrb///e8+x/j888+te+65x2rTpo0VGRlpPfDAA9bp06d9av7yl79YN998s+VwOKyOHTtac+bM8Wuen3zyiSWJGzdu3Lhx42bg7ZNPPqnz97xfZ2C++OIL3XTTTRoyZIjee+89XXvttTp8+LCuueYau2bu3Ll6/vnntWzZMiUkJOipp55SamqqDhw4oPDwcEnSmDFj9NlnnykvL09er1f333+/JkyYoOXLl0uSysrKlJKSouTkZC1evFjFxcV64IEHFBUVpQkTJtRrrm3atJEkffLJJ3I6nTXWeL1e5ebmKiUlRaGhof58KoxBj4Eh0HsM9P4kegwU9Nj4ysrK1KlTJ/v3eG38CjBz5sxRp06dtHTpUnssISHB/tiyLC1cuFBPPvmk7rjjDknSa6+9ppiYGL399tsaPXq0Dh48qA0bNmj37t0aMGCAJOmFF17QD37wA82bN09xcXF6/fXXVV5erldffVVhYWHq3r27ioqK9Nxzz9U7wFRdNnI6nXUGmIiICDmdzoD+QqRH8wV6j4Hen0SPgYIer55LPf3DrwCzZs0apaam6ic/+Ym2bNmib33rW/rZz36mBx98UJJUUlIit9ut5ORke5/IyEglJSWpoKBAo0ePVkFBgaKiouzwIknJyckKDg7Wzp079aMf/UgFBQW69dZbFRYWZtekpqZqzpw5+uKLL3zO+FTxeDzyeDz2/bKyMklfLYTX662xn6rx2rYHAnoMDIHeY6D3J9FjoKDHq/f4l+JXgPn444/18ssvKysrS//zP/+j3bt36+c//7nCwsKUnp4ut9stSYqJifHZLyYmxt7mdrvVvn1730m0aKHo6GifmovP7Fx8TLfbXWOAmTVrlqZPn15tPDc3VxEREXX2lZeXV+f2QECPgSHQewz0/iR6DBT02HjOnTtXrzq/AkxlZaUGDBigX//615Kkvn37at++fVq8eLHS09P9n+UVNGXKFGVlZdn3q66hpaSk1HkJKS8vT8OGDQvoU4H0aL5A7zHQ+5PoMVDQY+OruoJyKX4FmA4dOigxMdFnrFu3bvrjH/8oSYqNjZUklZaWqkOHDnZNaWmp+vTpY9ccP37c5xgXLlzQiRMn7P1jY2NVWlrqU1N1v6rm6xwOhxwOR7Xx0NDQSy5AfWpMR4+BIdB7DPT+JHoMFPTYuI9bH379IbubbrpJhw4d8hn7+9//rvj4eElfPaE3NjZWmzZtsreXlZVp586dcrlckiSXy6WTJ0+qsLDQrtm8ebMqKyuVlJRk12zdutXnOlheXp66du1a4+UjAADwzeJXgJk0aZJ27NihX//61zpy5IiWL1+u3/72t8rIyJD01TOGMzMz9cwzz2jNmjUqLi7Wfffdp7i4OI0cOVLSV2dshg8frgcffFC7du3Shx9+qIkTJ2r06NGKi4uTJN1zzz0KCwvTuHHjtH//fr355ptatGiRzyUiAADwzeXXJaTvfve7Wr16taZMmaIZM2YoISFBCxcu1JgxY+yayZMn6+zZs5owYYJOnjypm2++WRs2bLD/Bowkvf7665o4caKGDh2q4OBgjRo1Ss8//7y9PTIyUrm5ucrIyFD//v3Vrl07TZ06td4voQYAAIHN77cS+OEPf6gf/vCHtW4PCgrSjBkzNGPGjFproqOj7T9aV5tevXrpT3/6k7/TAwAA3wC8mSMAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHH8fhk1pG8/sa6pp1BvjhBLcwc29SwAALiyOAMDAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4/gVYKZNm6agoCCf24033mhvP3/+vDIyMtS2bVu1bt1ao0aNUmlpqc8xjh49qrS0NEVERKh9+/Z67LHHdOHCBZ+a/Px89evXTw6HQ9dff71ycnIa3iEAAAg4fp+B6d69uz777DP7tm3bNnvbpEmT9O6772rVqlXasmWLjh07pjvvvNPeXlFRobS0NJWXl2v79u1atmyZcnJyNHXqVLumpKREaWlpGjJkiIqKipSZmanx48dr48aNl9kqAAAIFC383qFFC8XGxlYbP3XqlJYsWaLly5frtttukyQtXbpU3bp1044dOzRo0CDl5ubqwIEDev/99xUTE6M+ffpo5syZevzxxzVt2jSFhYVp8eLFSkhI0Pz58yVJ3bp107Zt27RgwQKlpqZeZrsAACAQ+B1gDh8+rLi4OIWHh8vlcmnWrFnq3LmzCgsL5fV6lZycbNfeeOON6ty5swoKCjRo0CAVFBSoZ8+eiomJsWtSU1P18MMPa//+/erbt68KCgp8jlFVk5mZWee8PB6PPB6Pfb+srEyS5PV65fV6a9ynary27bVxhFh+1TclR/BXc/W3R5M0dB1NEug9Bnp/Ej0GCnq8eo9/KX4FmKSkJOXk5Khr16767LPPNH36dN1yyy3at2+f3G63wsLCFBUV5bNPTEyM3G63JMntdvuEl6rtVdvqqikrK9OXX36pli1b1ji3WbNmafr06dXGc3NzFRERUWdfeXl5dW7/urkD/SpvFvzt0UT0aL5A70+ix0BBj43n3Llz9arzK8CMGDHC/rhXr15KSkpSfHy8Vq5cWWuwuFqmTJmirKws+35ZWZk6deqklJQUOZ3OGvfxer3Ky8vTsGHDFBoaWu/H6jHNnOfjOIItzRxQ6XePJmnoOpok0HsM9P4kegwU9Nj4qq6gXIrfl5AuFhUVpRtuuEFHjhzRsGHDVF5erpMnT/qchSktLbWfMxMbG6tdu3b5HKPqVUoX13z9lUulpaVyOp11hiSHwyGHw1FtPDQ09JILUJ+ai3kqgupd21z426OJ6NF8gd6fRI+Bgh4b93Hr47L+DsyZM2f00UcfqUOHDurfv79CQ0O1adMme/uhQ4d09OhRuVwuSZLL5VJxcbGOHz9u1+Tl5cnpdCoxMdGuufgYVTVVxwAAAPArwDz66KPasmWL/vGPf2j79u360Y9+pJCQEN19992KjIzUuHHjlJWVpQ8++ECFhYW6//775XK5NGjQIElSSkqKEhMTNXbsWP3lL3/Rxo0b9eSTTyojI8M+e/LQQw/p448/1uTJk/W3v/1NL730klauXKlJkyZd+e4BAICR/LqE9Omnn+ruu+/W559/rmuvvVY333yzduzYoWuvvVaStGDBAgUHB2vUqFHyeDxKTU3VSy+9ZO8fEhKitWvX6uGHH5bL5VKrVq2Unp6uGTNm2DUJCQlat26dJk2apEWLFqljx4565ZVXeAk1AACw+RVgVqxYUef28PBwZWdnKzs7u9aa+Ph4rV+/vs7jDB48WHv37vVnagAA4BuE90ICAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjXFaAmT17toKCgpSZmWmPnT9/XhkZGWrbtq1at26tUaNGqbS01Ge/o0ePKi0tTREREWrfvr0ee+wxXbhwwacmPz9f/fr1k8Ph0PXXX6+cnJzLmSoAAAggDQ4wu3fv1m9+8xv16tXLZ3zSpEl69913tWrVKm3ZskXHjh3TnXfeaW+vqKhQWlqaysvLtX37di1btkw5OTmaOnWqXVNSUqK0tDQNGTJERUVFyszM1Pjx47Vx48aGThcAAASQBgWYM2fOaMyYMfrd736na665xh4/deqUlixZoueee0633Xab+vfvr6VLl2r79u3asWOHJCk3N1cHDhzQ73//e/Xp00cjRozQzJkzlZ2drfLycknS4sWLlZCQoPnz56tbt26aOHGifvzjH2vBggVXoGUAAGC6Fg3ZKSMjQ2lpaUpOTtYzzzxjjxcWFsrr9So5Odkeu/HGG9W5c2cVFBRo0KBBKigoUM+ePRUTE2PXpKam6uGHH9b+/fvVt29fFRQU+ByjqubiS1Vf5/F45PF47PtlZWWSJK/XK6/XW+M+VeO1ba+NI8Tyq74pOYK/mqu/PZqkoetokkDvMdD7k+gxUNDj1Xv8S/E7wKxYsUJ79uzR7t27q21zu90KCwtTVFSUz3hMTIzcbrddc3F4qdpeta2umrKyMn355Zdq2bJltceeNWuWpk+fXm08NzdXERERdfaUl5dX5/avmzvQr/Jmwd8eTUSP5gv0/iR6DBT02HjOnTtXrzq/Aswnn3yiX/ziF8rLy1N4eHiDJtZYpkyZoqysLPt+WVmZOnXqpJSUFDmdzhr38Xq9ysvL07BhwxQaGlrvx+oxzZzn4jiCLc0cUOl3jyZp6DqaJNB7DPT+JHoMFPTY+KquoFyKXwGmsLBQx48fV79+/eyxiooKbd26VS+++KI2btyo8vJynTx50ucsTGlpqWJjYyVJsbGx2rVrl89xq16ldHHN11+5VFpaKqfTWePZF0lyOBxyOBzVxkNDQy+5APWpuZinIqjetc2Fvz2aiB7NF+j9SfQYKOixcR+3Pvx6Eu/QoUNVXFysoqIi+zZgwACNGTPG/jg0NFSbNm2y9zl06JCOHj0ql8slSXK5XCouLtbx48ftmry8PDmdTiUmJto1Fx+jqqbqGAAA4JvNrzMwbdq0UY8ePXzGWrVqpbZt29rj48aNU1ZWlqKjo+V0OvXII4/I5XJp0KBBkqSUlBQlJiZq7Nixmjt3rtxut5588kllZGTYZ1Aeeughvfjii5o8ebIeeOABbd68WStXrtS6deuuRM8AAMBwDXoVUl0WLFig4OBgjRo1Sh6PR6mpqXrppZfs7SEhIVq7dq0efvhhuVwutWrVSunp6ZoxY4Zdk5CQoHXr1mnSpElatGiROnbsqFdeeUWpqalXeroAAMBAlx1g8vPzfe6Hh4crOztb2dnZte4THx+v9evX13ncwYMHa+/evZc7PQAAEIB4LyQAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADCOXwHm5ZdfVq9eveR0OuV0OuVyufTee+/Z28+fP6+MjAy1bdtWrVu31qhRo1RaWupzjKNHjyotLU0RERFq3769HnvsMV24cMGnJj8/X/369ZPD4dD111+vnJychncIAAACjl8BpmPHjpo9e7YKCwv15z//WbfddpvuuOMO7d+/X5I0adIkvfvuu1q1apW2bNmiY8eO6c4777T3r6ioUFpamsrLy7V9+3YtW7ZMOTk5mjp1ql1TUlKitLQ0DRkyREVFRcrMzNT48eO1cePGK9QyAAAwXQt/im+//Xaf+7/61a/08ssva8eOHerYsaOWLFmi5cuX67bbbpMkLV26VN26ddOOHTs0aNAg5ebm6sCBA3r//fcVExOjPn36aObMmXr88cc1bdo0hYWFafHixUpISND8+fMlSd26ddO2bdu0YMECpaam1jo3j8cjj8dj3y8rK5Mkeb1eeb3eGvepGq9te20cIZZf9U3JEfzVXP3t0SQNXUeTBHqPgd6fRI+Bgh6v3uNfSpBlWQ36bVxRUaFVq1YpPT1de/fuldvt1tChQ/XFF18oKirKrouPj1dmZqYmTZqkqVOnas2aNSoqKrK3l5SUqEuXLtqzZ4/69u2rW2+9Vf369dPChQvtmqVLlyozM1OnTp2qdT7Tpk3T9OnTq40vX75cERERDWkRAABcZefOndM999yjU6dOyel01lrn1xkYSSouLpbL5dL58+fVunVrrV69WomJiSoqKlJYWJhPeJGkmJgYud1uSZLb7VZMTEy17VXb6qopKyvTl19+qZYtW9Y4rylTpigrK8u+X1ZWpk6dOiklJaXWT4DX61VeXp6GDRum0NDQen8Oekwz53KWI9jSzAGVfvdokoauo0kCvcdA70+ix0BBj42v6grKpfgdYLp27aqioiKdOnVKf/jDH5Senq4tW7b4PcErzeFwyOFwVBsPDQ295ALUp+Zinoogv+fX1Pzt0UT0aL5A70+ix0BBj437uPXhd4AJCwvT9ddfL0nq37+/du/erUWLFumuu+5SeXm5Tp486XMWprS0VLGxsZKk2NhY7dq1y+d4Va9Surjm669cKi0tldPprPXsCwAA+Ga57L8DU1lZKY/Ho/79+ys0NFSbNm2ytx06dEhHjx6Vy+WSJLlcLhUXF+v48eN2TV5enpxOpxITE+2ai49RVVN1DAAAAL/OwEyZMkUjRoxQ586ddfr0aS1fvlz5+fnauHGjIiMjNW7cOGVlZSk6OlpOp1OPPPKIXC6XBg0aJElKSUlRYmKixo4dq7lz58rtduvJJ59URkaGffnnoYce0osvvqjJkyfrgQce0ObNm7Vy5UqtW7fuynf/DdJj2kYjL339Y3ZaU08BANAM+RVgjh8/rvvuu0+fffaZIiMj1atXL23cuFHDhg2TJC1YsEDBwcEaNWqUPB6PUlNT9dJLL9n7h4SEaO3atXr44YflcrnUqlUrpaena8aMGXZNQkKC1q1bp0mTJmnRokXq2LGjXnnllTpfQg0AAL5Z/AowS5YsqXN7eHi4srOzlZ2dXWtNfHy81q9fX+dxBg8erL179/ozNQAA8A3CeyEBAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBx/Aows2bN0ne/+121adNG7du318iRI3Xo0CGfmvPnzysjI0Nt27ZV69atNWrUKJWWlvrUHD16VGlpaYqIiFD79u312GOP6cKFCz41+fn56tevnxwOh66//nrl5OQ0rEMAABBw/AowW7ZsUUZGhnbs2KG8vDx5vV6lpKTo7Nmzds2kSZP07rvvatWqVdqyZYuOHTumO++8095eUVGhtLQ0lZeXa/v27Vq2bJlycnI0depUu6akpERpaWkaMmSIioqKlJmZqfHjx2vjxo1XoGUAAGC6Fv4Ub9iwwed+Tk6O2rdvr8LCQt166606deqUlixZouXLl+u2226TJC1dulTdunXTjh07NGjQIOXm5urAgQN6//33FRMToz59+mjmzJl6/PHHNW3aNIWFhWnx4sVKSEjQ/PnzJUndunXTtm3btGDBAqWmptY4N4/HI4/HY98vKyuTJHm9Xnm93hr3qRqvbXttHCGWX/VNyRFs+fxrmvqsTUPX0SSB3mOg9yfRY6Cgx6v3+JcSZFlWg3+zHTlyRN/5zndUXFysHj16aPPmzRo6dKi++OILRUVF2XXx8fHKzMzUpEmTNHXqVK1Zs0ZFRUX29pKSEnXp0kV79uxR3759deutt6pfv35auHChXbN06VJlZmbq1KlTNc5l2rRpmj59erXx5cuXKyIioqEtAgCAq+jcuXO65557dOrUKTmdzlrr/DoDc7HKykplZmbqpptuUo8ePSRJbrdbYWFhPuFFkmJiYuR2u+2amJiYaturttVVU1ZWpi+//FItW7asNp8pU6YoKyvLvl9WVqZOnTopJSWl1k+A1+tVXl6ehg0bptDQ0Hr33mOaOZeyHMGWZg6o1FN/DpanMqipp+O3fdNqPuN2sYauo0kCvcdA70+ix0BBj42v6grKpTQ4wGRkZGjfvn3atm1bQw9xRTkcDjkcjmrjoaGhl1yA+tRczFNhXhDwVAYZOW9/1sXfdTRRoPcY6P1J9Bgo6LFxH7c+GvQy6okTJ2rt2rX64IMP1LFjR3s8NjZW5eXlOnnypE99aWmpYmNj7Zqvvyqp6v6lapxOZ41nXwAAwDeLXwHGsixNnDhRq1ev1ubNm5WQkOCzvX///goNDdWmTZvssUOHDuno0aNyuVySJJfLpeLiYh0/ftyuycvLk9PpVGJiol1z8TGqaqqOAQAAvtn8uoSUkZGh5cuX65133lGbNm3s56xERkaqZcuWioyM1Lhx45SVlaXo6Gg5nU498sgjcrlcGjRokCQpJSVFiYmJGjt2rObOnSu3260nn3xSGRkZ9iWghx56SC+++KImT56sBx54QJs3b9bKlSu1bt26K9w+AAAwkV9nYF5++WWdOnVKgwcPVocOHezbm2++adcsWLBAP/zhDzVq1Cjdeuutio2N1VtvvWVvDwkJ0dq1axUSEiKXy6V7771X9913n2bMmGHXJCQkaN26dcrLy1Pv3r01f/58vfLKK7W+hBoAAHyz+HUGpj6vuA4PD1d2drays7NrrYmPj9f69evrPM7gwYO1d+9ef6YHAAC+IXgvJAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBy/A8zWrVt1++23Ky4uTkFBQXr77bd9tluWpalTp6pDhw5q2bKlkpOTdfjwYZ+aEydOaMyYMXI6nYqKitK4ceN05swZn5q//vWvuuWWWxQeHq5OnTpp7ty5/ncHAAACkt8B5uzZs+rdu7eys7Nr3D537lw9//zzWrx4sXbu3KlWrVopNTVV58+ft2vGjBmj/fv3Ky8vT2vXrtXWrVs1YcIEe3tZWZlSUlIUHx+vwsJCPfvss5o2bZp++9vfNqBFAAAQaFr4u8OIESM0YsSIGrdZlqWFCxfqySef1B133CFJeu211xQTE6O3335bo0eP1sGDB7Vhwwbt3r1bAwYMkCS98MIL+sEPfqB58+YpLi5Or7/+usrLy/Xqq68qLCxM3bt3V1FRkZ577jmfoAMAAL6Z/A4wdSkpKZHb7VZycrI9FhkZqaSkJBUUFGj06NEqKChQVFSUHV4kKTk5WcHBwdq5c6d+9KMfqaCgQLfeeqvCwsLsmtTUVM2ZM0dffPGFrrnmmmqP7fF45PF47PtlZWWSJK/XK6/XW+N8q8Zr214bR4jlV31TcgRbPv+apj5r09B1NEmg9xjo/Un0GCjo8eo9/qVc0QDjdrslSTExMT7jMTEx9ja326327dv7TqJFC0VHR/vUJCQkVDtG1baaAsysWbM0ffr0auO5ubmKiIioc955eXl1bv+6uQP9Km8WZg6obOopNMj69evrXevvOpoo0HsM9P4kegwU9Nh4zp07V6+6KxpgmtKUKVOUlZVl3y8rK1OnTp2UkpIip9NZ4z5er1d5eXkaNmyYQkND6/1YPaZtvOz5Xi2OYEszB1TqqT8Hy1MZ1NTT8du+aamXrGnoOpok0HsM9P4kegwU9Nj4qq6gXMoVDTCxsbGSpNLSUnXo0MEeLy0tVZ8+feya48eP++x34cIFnThxwt4/NjZWpaWlPjVV96tqvs7hcMjhcFQbDw0NveQC1KfmYp4K84KApzLIyHn7sy7+rqOJAr3HQO9PosdAQY+N+7j1cUX/DkxCQoJiY2O1adMme6ysrEw7d+6Uy+WSJLlcLp08eVKFhYV2zebNm1VZWamkpCS7ZuvWrT7XwfLy8tS1a9caLx8BAIBvFr8DzJkzZ1RUVKSioiJJXz1xt6ioSEePHlVQUJAyMzP1zDPPaM2aNSouLtZ9992nuLg4jRw5UpLUrVs3DR8+XA8++KB27dqlDz/8UBMnTtTo0aMVFxcnSbrnnnsUFhamcePGaf/+/XrzzTe1aNEin0tEAADgm8vvS0h//vOfNWTIEPt+VahIT09XTk6OJk+erLNnz2rChAk6efKkbr75Zm3YsEHh4eH2Pq+//romTpyooUOHKjg4WKNGjdLzzz9vb4+MjFRubq4yMjLUv39/tWvXTlOnTuUl1AAAQFIDAszgwYNlWbW/JDcoKEgzZszQjBkzaq2Jjo7W8uXL63ycXr166U9/+pO/0wMAAN8AvBcSAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHFaNPUEgLp8+4l1l6xxhFiaO1DqMW2jPBVBV2FWdfvH7LSmngIABDzOwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOM36vZCys7P17LPPyu12q3fv3nrhhRc0cODApp4WUKf6vH+Tvxr7/Z54/yYApmm2Z2DefPNNZWVl6emnn9aePXvUu3dvpaam6vjx4009NQAA0MSabYB57rnn9OCDD+r+++9XYmKiFi9erIiICL366qtNPTUAANDEmuUlpPLychUWFmrKlCn2WHBwsJKTk1VQUFDjPh6PRx6Px75/6tQpSdKJEyfk9Xpr3Mfr9ercuXP6/PPPFRoaWu/5tbhwtt61Ta1FpaVz5yrVwhusisorf+mhOaDHy3f9oyuv+DH94Qi29GTfSvX537fkqWd/O6cMbeRZXVkN/XljEnoMDE3d4+nTpyVJlmXVWdcsA8x//vMfVVRUKCYmxmc8JiZGf/vb32rcZ9asWZo+fXq18YSEhEaZo0nuaeoJXAX0aD5/+2s3v1GmAaCZOH36tCIjI2vd3iwDTENMmTJFWVlZ9v3KykqdOHFCbdu2VVBQzf+jKysrU6dOnfTJJ5/I6XReraleVfQYGAK9x0DvT6LHQEGPjc+yLJ0+fVpxcXF11jXLANOuXTuFhISotLTUZ7y0tFSxsbE17uNwOORwOHzGoqKi6vV4TqczYL8Qq9BjYAj0HgO9P4keAwU9Nq66zrxUaZZP4g0LC1P//v21adMme6yyslKbNm2Sy+VqwpkBAIDmoFmegZGkrKwspaena8CAARo4cKAWLlyos2fP6v7772/qqQEAgCbWbAPMXXfdpX//+9+aOnWq3G63+vTpow0bNlR7Yu/lcDgcevrpp6tdegok9BgYAr3HQO9PosdAQY/NR5B1qdcpAQAANDPN8jkwAAAAdSHAAAAA4xBgAACAcQgwAADAOAQYAABgnIAPMCdOnNCYMWPkdDoVFRWlcePG6cyZM3XWP/LII+ratatatmypzp076+c//7n95pBVgoKCqt1WrFjR2O1IkrKzs/Xtb39b4eHhSkpK0q5du+qsX7VqlW688UaFh4erZ8+eWr9+vc92y7I0depUdejQQS1btlRycrIOHz7cmC3UyZ/+fve73+mWW27RNddco2uuuUbJycnV6v/rv/6r2loNHz68sduokz895uTkVJt/eHi4T01zW0PJvx4HDx5c4/dUWlqaXdOc1nHr1q26/fbbFRcXp6CgIL399tuX3Cc/P1/9+vWTw+HQ9ddfr5ycnGo1/n5vNyZ/e3zrrbc0bNgwXXvttXI6nXK5XNq4caNPzbRp06qt4Y033tiIXdTN3x7z8/Nr/Dp1u90+dSavY03fZ0FBQerevbtd01zWMeADzJgxY7R//37l5eVp7dq12rp1qyZMmFBr/bFjx3Ts2DHNmzdP+/btU05OjjZs2KBx48ZVq126dKk+++wz+zZy5MhG7OQrb775prKysvT0009rz5496t27t1JTU3X8+PEa67dv3667775b48aN0969ezVy5EiNHDlS+/bts2vmzp2r559/XosXL9bOnTvVqlUrpaam6vz5843ez9f5219+fr7uvvtuffDBByooKFCnTp2UkpKif/3rXz51w4cP91mrN95442q0UyN/e5S++pPeF8//n//8p8/25rSGkv89vvXWWz797du3TyEhIfrJT37iU9dc1vHs2bPq3bu3srOz61VfUlKitLQ0DRkyREVFRcrMzNT48eN9fsE35OuiMfnb49atWzVs2DCtX79ehYWFGjJkiG6//Xbt3bvXp6579+4+a7ht27bGmH69+NtjlUOHDvn00L59e3ub6eu4aNEin94++eQTRUdHV/tebBbraAWwAwcOWJKs3bt322PvvfeeFRQUZP3rX/+q93FWrlxphYWFWV6v1x6TZK1evfpKTrdeBg4caGVkZNj3KyoqrLi4OGvWrFk11v/0pz+10tLSfMaSkpKs//7v/7Ysy7IqKyut2NhY69lnn7W3nzx50nI4HNYbb7zRCB3Uzd/+vu7ChQtWmzZtrGXLltlj6enp1h133HGlp9pg/va4dOlSKzIystbjNbc1tKzLX8cFCxZYbdq0sc6cOWOPNbd1rFKfnwWTJ0+2unfv7jN21113Wampqfb9y/2cNaaG/rxLTEy0pk+fbt9/+umnrd69e1+5iV1B9enxgw8+sCRZX3zxRa01gbaOq1evtoKCgqx//OMf9lhzWceAPgNTUFCgqKgoDRgwwB5LTk5WcHCwdu7cWe/jnDp1Sk6nUy1a+P7h4oyMDLVr104DBw7Uq6++KquR/yZgeXm5CgsLlZycbI8FBwcrOTlZBQUFNe5TUFDgUy9Jqampdn1JSYncbrdPTWRkpJKSkmo9ZmNpSH9fd+7cOXm9XkVHR/uM5+fnq3379uratasefvhhff7551d07vXV0B7PnDmj+Ph4derUSXfccYf2799vb2tOayhdmXVcsmSJRo8erVatWvmMN5d19Nelvg+vxOesuamsrNTp06erfS8ePnxYcXFx6tKli8aMGaOjR4820Qwbrk+fPurQoYOGDRumDz/80B4PxHVcsmSJkpOTFR8f7zPeHNYxoAOM2+32ObUnSS1atFB0dHS1a5a1+c9//qOZM2dWu+w0Y8YMrVy5Unl5eRo1apR+9rOf6YUXXrhic69tLhUVFdXeTiEmJqbWftxud531Vf/6c8zG0pD+vu7xxx9XXFyczw+Q4cOH67XXXtOmTZs0Z84cbdmyRSNGjFBFRcUVnX99NKTHrl276tVXX9U777yj3//+96qsrNT3vvc9ffrpp5Ka1xpKl7+Ou3bt0r59+zR+/Hif8ea0jv6q7fuwrKxMX3755RX52m9u5s2bpzNnzuinP/2pPZaUlGRfln/55ZdVUlKiW265RadPn27CmdZfhw4dtHjxYv3xj3/UH//4R3Xq1EmDBw/Wnj17JF2Zn2HNybFjx/Tee+9V+15sLuvYbN8LqS5PPPGE5syZU2fNwYMHL/txysrKlJaWpsTERE2bNs1n21NPPWV/3LdvX509e1bPPvusfv7zn1/246JhZs+erRUrVig/P9/nSa6jR4+2P+7Zs6d69eql6667Tvn5+Ro6dGhTTNUvLpfL513Yv/e976lbt276zW9+o5kzZzbhzBrHkiVL1LNnTw0cONBn3PR1/CZZvny5pk+frnfeecfnP5EjRoywP+7Vq5eSkpIUHx+vlStX1vg8w+ama9eu6tq1q33/e9/7nj766CMtWLBA//d//9eEM2scy5YtU1RUVLXndzaXdTTyDMwvf/lLHTx4sM5bly5dFBsbW+2JUxcuXNCJEycUGxtb52OcPn1aw4cPV5s2bbR69WqFhobWWZ+UlKRPP/1UHo/nsvurTbt27RQSEqLS0lKf8dLS0lr7iY2NrbO+6l9/jtlYGtJflXnz5mn27NnKzc1Vr1696qzt0qWL2rVrpyNHjlz2nP11OT1WCQ0NVd++fe35N6c1lC6vx7Nnz2rFihX1+iHYlOvor9q+D51Op1q2bHlFvi6aixUrVmj8+PFauXJltctmXxcVFaUbbrjBiDWszcCBA+35B9I6WpalV199VWPHjlVYWFidtU21jkYGmGuvvVY33nhjnbewsDC5XC6dPHlShYWF9r6bN29WZWWlkpKSaj1+WVmZUlJSFBYWpjVr1lR7yWpNioqKdM011zTqu3eGhYWpf//+2rRpkz1WWVmpTZs2+fwP/WIul8unXpLy8vLs+oSEBMXGxvrUlJWVaefOnbUes7E0pD/pq1fgzJw5Uxs2bPB5vlNtPv30U33++efq0KHDFZm3Pxra48UqKipUXFxsz785raF0eT2uWrVKHo9H99577yUfpynX0V+X+j68El8XzcEbb7yh+++/X2+88YbPS+Brc+bMGX300UdGrGFtioqK7PkHyjpK0pYtW3TkyJF6/WeiydaxqZ9F3NiGDx9u9e3b19q5c6e1bds26zvf+Y51991329s//fRTq2vXrtbOnTsty7KsU6dOWUlJSVbPnj2tI0eOWJ999pl9u3DhgmVZlrVmzRrrd7/7nVVcXGwdPnzYeumll6yIiAhr6tSpjd7PihUrLIfDYeXk5FgHDhywJkyYYEVFRVlut9uyLMsaO3as9cQTT9j1H374odWiRQtr3rx51sGDB62nn37aCg0NtYqLi+2a2bNnW1FRUdY777xj/fWvf7XuuOMOKyEhwfryyy8bvZ/L7W/27NlWWFiY9Yc//MFnrU6fPm1ZlmWdPn3aevTRR62CggKrpKTEev/9961+/fpZ3/nOd6zz589f9f4a0uP06dOtjRs3Wh999JFVWFhojR492goPD7f2799v1zSnNbQs/3uscvPNN1t33XVXtfHmto6nT5+29u7da+3du9eSZD333HPW3r17rX/+85+WZVnWE088YY0dO9au//jjj62IiAjrsccesw4ePGhlZ2dbISEh1oYNG+yaS33OrjZ/e3z99detFi1aWNnZ2T7fiydPnrRrfvnLX1r5+flWSUmJ9eGHH1rJyclWu3btrOPHj1/1/izL/x4XLFhgvf3229bhw4et4uJi6xe/+IUVHBxsvf/++3aN6etY5d5777WSkpJqPGZzWceADzCff/65dffdd1utW7e2nE6ndf/999u/3CzLskpKSixJ1gcffGBZ1v9/mVxNt5KSEsuyvnopdp8+fazWrVtbrVq1snr37m0tXrzYqqiouCo9vfDCC1bnzp2tsLAwa+DAgdaOHTvsbd///vet9PR0n/qVK1daN9xwgxUWFmZ1797dWrdunc/2yspK66mnnrJiYmIsh8NhDR061Dp06NDVaKVG/vQXHx9f41o9/fTTlmVZ1rlz56yUlBTr2muvtUJDQ634+HjrwQcfbLIfJlX86TEzM9OujYmJsX7wgx9Ye/bs8Tlec1tDy/L/6/Rvf/ubJcnKzc2tdqzmto61/Zyo6ik9Pd36/ve/X22fPn36WGFhYVaXLl2spUuXVjtuXZ+zq83fHr///e/XWW9ZX710vEOHDlZYWJj1rW99y7rrrrusI0eOXN3GLuJvj3PmzLGuu+46Kzw83IqOjrYGDx5sbd68udpxTV5Hy/rqzzC0bNnS+u1vf1vjMZvLOgZZViO/9hcAAOAKM/I5MAAA4JuNAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAqLetW7fq9ttvV1xcnIKCgvT222/7fQzLsjRv3jzdcMMNcjgc+ta3vqVf/epXfh3DyHejBgAATePs2bPq3bu3HnjgAd15550NOsYvfvEL5ebmat68eerZs6dOnDihEydO+HUM/hIvAABokKCgIK1evVojR460xzwej/73f/9Xb7zxhk6ePKkePXpozpw5Gjx4sCTp4MGD6tWrl/bt26euXbs2+LG5hAQAAK6YiRMnqqCgQCtWrNBf//pX/eQnP9Hw4cN1+PBhSdK7776rLl26aO3atUpISNC3v/1tjR8/3u8zMAQYAABwRRw9elRLly7VqlWrdMstt+i6667To48+qptvvllLly6VJH388cf65z//qVWrVum1115TTk6OCgsL9eMf/9ivx+I5MAAA4IooLi5WRUWFbrjhBp9xj8ejtm3bSpIqKyvl8Xj02muv2XVLlixR//79dejQoXpfViLAAACAK+LMmTMKCQlRYWGhQkJCfLa1bt1aktShQwe1aNHCJ+R069ZN0ldncAgwAADgqurbt68qKip0/Phx3XLLLTXW3HTTTbpw4YI++ugjXXfddZKkv//975Kk+Pj4ej8Wr0ICAAD1dubMGR05ckTSV4Hlueee05AhQxQdHa3OnTvr3nvv1Ycffqj58+erb9+++ve//61NmzapV69eSktLU2Vlpb773e+qdevWWrhwoSorK5WRkSGn06nc3Nx6z4MAAwAA6i0/P19DhgypNp6enq6cnBx5vV4988wzeu211/Svf/1L7dq106BBgzR9+nT17NlTknTs2DE98sgjys3NVatWrTRixAjNnz9f0dHR9Z4HAQYAABiHl1EDAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDj/DzCqBgTRKbr/AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import scipy.stats as stats\n",
        "fig, ax = plt.subplots(figsize=(8, 6))\n",
        "stats.probplot(residuals, dist=\"norm\", plot=ax)\n",
        "ax.set_title('QQ Plot of Residuals')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "koLlaHJBC3Js",
        "outputId": "c57e8334-4841-47ee-82d3-7da9f819e744"
      },
      "execution_count": 77,
      "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": [
        "### Shapiro-Wilk Normality Test\n",
        "La hipótesis nula es la normalidad de la distribución\n"
      ],
      "metadata": {
        "id": "zKI7wfk-E5L6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import shapiro\n",
        "\n",
        "# Perform the Shapiro-Wilk test\n",
        "stat, p = shapiro(residuals)\n",
        "\n",
        "# Print the test statistic and the p-value\n",
        "print(\"Test Statistic:\", stat)\n",
        "print(\"P-Value:\", p)\n",
        "\n",
        "# Interpret the result\n",
        "alpha = 0.05  # Significance level\n",
        "if p > alpha:\n",
        "    print(\"Sample looks Gaussian (fail to reject H0)\")\n",
        "else:\n",
        "    print(\"Sample does not look Gaussian (reject H0)\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qJkSAtRiDY2a",
        "outputId": "7220702d-4be3-4ecc-bc57-c486e651f816"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Statistic: 0.7791448831558228\n",
            "P-Value: 0.0\n",
            "Sample does not look Gaussian (reject H0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/scipy/stats/_morestats.py:1882: UserWarning: p-value may not be accurate for N > 5000.\n",
            "  warnings.warn(\"p-value may not be accurate for N > 5000.\")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Vamos a probar la transformación logarítmica al modelo\n",
        "\n",
        "$$ log(ingreso_i)=\\beta_0+\\beta_1 aesc_i+\\epsilon_i $$"
      ],
      "metadata": {
        "id": "TTDKpRVaUunQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['logingreso']=np.where(df['ingtot_2']>0,np.log(df['ingtot_2']),np.nan)"
      ],
      "metadata": {
        "id": "F9RTKEcLM_ef"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mod = smf.ols(formula='logingreso ~ aesc', data=df, )\n",
        "\n",
        "res = mod.fit(cov_type='HC1')\n",
        "\n",
        "print(res.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hbkVnmxASKcn",
        "outputId": "4a954caf-e99d-4ee5-9534-497500cf51e2"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:             logingreso   R-squared:                       0.207\n",
            "Model:                            OLS   Adj. R-squared:                  0.207\n",
            "Method:                 Least Squares   F-statistic:                     2373.\n",
            "Date:                Mon, 23 Oct 2023   Prob (F-statistic):               0.00\n",
            "Time:                        20:45:16   Log-Likelihood:                -10880.\n",
            "No. Observations:                9062   AIC:                         2.176e+04\n",
            "Df Residuals:                    9060   BIC:                         2.178e+04\n",
            "Df Model:                           1                                         \n",
            "Covariance Type:                  HC1                                         \n",
            "==============================================================================\n",
            "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "Intercept      9.9699      0.032    310.331      0.000       9.907      10.033\n",
            "aesc           0.1104      0.002     48.710      0.000       0.106       0.115\n",
            "==============================================================================\n",
            "Omnibus:                     1489.077   Durbin-Watson:                   1.822\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3561.197\n",
            "Skew:                          -0.933   Prob(JB):                         0.00\n",
            "Kurtosis:                       5.440   Cond. No.                         50.6\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors are heteroscedasticity robust (HC1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "res.resid.hist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "cf7fzBfxSOst",
        "outputId": "1f6915f3-fc82-414a-8b66-28a5626672d4"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 83
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjAAAAGdCAYAAAAMm0nCAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA0uElEQVR4nO3dfVSU953//xcgjKIOig2MHJFQ3arE22jVaVMPKkIMa5OGdpvGKk2MqR50V+mqpT9jUJNoaY2xCdF0k0p2IxtNNyaN2sCIRZuKd1RWxcbTuGZJokC/sTKKcRhhfn/0MOvUGxwcnPng83HOHLyu6zOf6/1mYHh5XdfMhHk8Ho8AAAAMEh7sAgAAAPxFgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGKdLsAvoKC0tLTp9+rR69uypsLCwYJcDAABugsfj0fnz55WQkKDw8OsfZ+m0Aeb06dNKTEwMdhkAAKAdPvnkE/Xr1++62zttgOnZs6ekv30DrFZrQOZ0u90qLS1Venq6IiMjAzJnKKE/s9Gf2ejPbPQXOE6nU4mJid6/49fTaQNM62kjq9Ua0AATHR0tq9XaaX9A6c9c9Gc2+jMb/QVeW5d/cBEvAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHG6BLsAALhT3P3j7dfdZonwqGCsNDS/RK7msNtY1Y19vDoz2CUA18QRGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA49xSgFm9erXCwsK0YMEC77pLly4pJydHffr0UY8ePZSVlaW6ujqf+9XU1CgzM1PR0dGKi4vTokWLdPnyZZ8x5eXluvfee2WxWDRw4EAVFRXdSqkAAKATaXeAOXjwoF555RUNHz7cZ/3ChQv13nvv6a233tLu3bt1+vRpPfzww97tzc3NyszMVFNTk/bu3avXX39dRUVFWrZsmXfMqVOnlJmZqYkTJ6qqqkoLFizQE088oZKSkvaWCwAAOpF2BZgLFy5o+vTp+rd/+zf17t3bu76hoUGvvfaann/+eU2aNEmjR4/Wxo0btXfvXu3bt0+SVFpaquPHj+uNN97QyJEjNXXqVK1cuVKFhYVqamqSJG3YsEHJyclas2aNhgwZonnz5unb3/621q5dG4CWAQCA6dr1Trw5OTnKzMxUWlqannnmGe/6yspKud1upaWledcNHjxY/fv3V0VFhcaPH6+KigoNGzZM8fHx3jEZGRmaO3euqqurNWrUKFVUVPjM0TrmylNVf8/lcsnlcnmXnU6nJMntdsvtdrenzau0zhOo+UIN/ZmN/kKfJcJz/W3hHp+voYLnz5tDf4HfV1v8DjBvvvmm/vjHP+rgwYNXbautrVVUVJR69erlsz4+Pl61tbXeMVeGl9btrdtuNMbpdOqLL75Qt27drtr3qlWrtHz58qvWl5aWKjo6+uYbvAkOhyOg84Ua+jMb/YWugrFtj1k5pqXjC/HDjh07AjqfyY/fzaC/W3fx4sWbGudXgPnkk0/0L//yL3I4HOratWu7CusoeXl5ys3N9S47nU4lJiYqPT1dVqs1IPtwu91yOByaMmWKIiMjAzJnKKE/s9Ff6Buaf/3r+CzhHq0c06KnDoXL1RI6n4V0LD8jIPN0hsfvRugvcFrPoLTFrwBTWVmp+vp63Xvvvd51zc3N2rNnj1566SWVlJSoqalJ586d8zkKU1dXJ5vNJkmy2Ww6cOCAz7ytr1K6cszfv3Kprq5OVqv1mkdfJMlischisVy1PjIyMuDf7I6YM5TQn9noL3TdzIc0ulrCQurDHHn+9A/9BWYfN8Ovi3gnT56so0ePqqqqynsbM2aMpk+f7v13ZGSkysrKvPc5ceKEampqZLfbJUl2u11Hjx5VfX29d4zD4ZDValVKSop3zJVztI5pnQMAANzZ/DoC07NnTw0dOtRnXffu3dWnTx/v+lmzZik3N1exsbGyWq2aP3++7Ha7xo8fL0lKT09XSkqKZsyYoYKCAtXW1mrp0qXKycnxHkGZM2eOXnrpJS1evFiPP/64du3apS1btmj79ut/FD0AALhztOtVSDeydu1ahYeHKysrSy6XSxkZGXr55Ze92yMiIrRt2zbNnTtXdrtd3bt3V3Z2tlasWOEdk5ycrO3bt2vhwoVat26d+vXrp1dffVUZGYE5FwsAAMx2ywGmvLzcZ7lr164qLCxUYWHhde+TlJTU5pXtqampOnz48K2WBwAAOiE+CwkAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMI5fAWb9+vUaPny4rFarrFar7Ha7fvvb33q3p6amKiwszOc2Z84cnzlqamqUmZmp6OhoxcXFadGiRbp8+bLPmPLyct17772yWCwaOHCgioqK2t8hAADodLr4M7hfv35avXq1/uEf/kEej0evv/66HnzwQR0+fFj33HOPJGn27NlasWKF9z7R0dHefzc3NyszM1M2m0179+7VmTNnNHPmTEVGRuq5556TJJ06dUqZmZmaM2eONm3apLKyMj3xxBPq27evMjIyAtEzAAAwnF8BZtq0aT7Lzz77rNavX699+/Z5A0x0dLRsNts1719aWqrjx49r586dio+P18iRI7Vy5UotWbJE+fn5ioqK0oYNG5ScnKw1a9ZIkoYMGaIPPvhAa9euJcAAAABJfgaYKzU3N+utt95SY2Oj7Ha7d/2mTZv0xhtvyGazadq0aXrqqae8R2EqKio0bNgwxcfHe8dnZGRo7ty5qq6u1qhRo1RRUaG0tDSffWVkZGjBggU3rMflcsnlcnmXnU6nJMntdsvtdre3TR+t8wRqvlBDf2ajv9BnifBcf1u4x+drqOD58+bQX+D31Ra/A8zRo0dlt9t16dIl9ejRQ1u3blVKSook6dFHH1VSUpISEhJ05MgRLVmyRCdOnNDbb78tSaqtrfUJL5K8y7W1tTcc43Q69cUXX6hbt27XrGvVqlVavnz5VetLS0t9TmMFgsPhCOh8oYb+zEZ/oatgbNtjVo5p6fhC/LBjx46Azmfy43cz6O/WXbx48abG+R1gBg0apKqqKjU0NOjXv/61srOztXv3bqWkpOjJJ5/0jhs2bJj69u2ryZMn6+TJkxowYIC/u/JLXl6ecnNzvctOp1OJiYlKT0+X1WoNyD7cbrccDoemTJmiyMjIgMwZSujPbPQX+obml1x3myXco5VjWvTUoXC5WsJuY1U3diw/MKfuO8PjdyP0FzitZ1Da4neAiYqK0sCBAyVJo0eP1sGDB7Vu3Tq98sorV40dN26cJOmjjz7SgAEDZLPZdODAAZ8xdXV1kuS9bsZms3nXXTnGarVe9+iLJFksFlkslqvWR0ZGBvyb3RFzhhL6Mxv9hS5Xc9vBxNUSdlPjbheeP/1Df4HZx8245feBaWlp8bn25EpVVVWSpL59+0qS7Ha7jh49qvr6eu8Yh8Mhq9XqPQ1lt9tVVlbmM4/D4fC5zgYAANzZ/DoCk5eXp6lTp6p///46f/68iouLVV5erpKSEp08eVLFxcV64IEH1KdPHx05ckQLFy7UhAkTNHz4cElSenq6UlJSNGPGDBUUFKi2tlZLly5VTk6O9+jJnDlz9NJLL2nx4sV6/PHHtWvXLm3ZskXbt28PfPcAAMBIfgWY+vp6zZw5U2fOnFFMTIyGDx+ukpISTZkyRZ988ol27typF154QY2NjUpMTFRWVpaWLl3qvX9ERIS2bdumuXPnym63q3v37srOzvZ535jk5GRt375dCxcu1Lp169SvXz+9+uqrvIQaAAB4+RVgXnvttetuS0xM1O7du9ucIykpqc2r2lNTU3X48GF/SgMAAHcQPgsJAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcvwLM+vXrNXz4cFmtVlmtVtntdv32t7/1br906ZJycnLUp08f9ejRQ1lZWaqrq/OZo6amRpmZmYqOjlZcXJwWLVqky5cv+4wpLy/XvffeK4vFooEDB6qoqKj9HQIAgE7HrwDTr18/rV69WpWVlTp06JAmTZqkBx98UNXV1ZKkhQsX6r333tNbb72l3bt36/Tp03r44Ye9929ublZmZqaampq0d+9evf766yoqKtKyZcu8Y06dOqXMzExNnDhRVVVVWrBggZ544gmVlJQEqGUAAGC6Lv4MnjZtms/ys88+q/Xr12vfvn3q16+fXnvtNRUXF2vSpEmSpI0bN2rIkCHat2+fxo8fr9LSUh0/flw7d+5UfHy8Ro4cqZUrV2rJkiXKz89XVFSUNmzYoOTkZK1Zs0aSNGTIEH3wwQdau3atMjIyAtQ2AAAwmV8B5krNzc1666231NjYKLvdrsrKSrndbqWlpXnHDB48WP3791dFRYXGjx+viooKDRs2TPHx8d4xGRkZmjt3rqqrqzVq1ChVVFT4zNE6ZsGCBTesx+VyyeVyeZedTqckye12y+12t7dNH63zBGq+UEN/ZqO/0GeJ8Fx/W7jH52uo4Pnz5tBf4PfVFr8DzNGjR2W323Xp0iX16NFDW7duVUpKiqqqqhQVFaVevXr5jI+Pj1dtba0kqba21ie8tG5v3XajMU6nU1988YW6det2zbpWrVql5cuXX7W+tLRU0dHR/rZ5Qw6HI6DzhRr6Mxv9ha6CsW2PWTmmpeML8cOOHTsCOp/Jj9/NoL9bd/HixZsa53eAGTRokKqqqtTQ0KBf//rXys7O1u7du/0uMNDy8vKUm5vrXXY6nUpMTFR6erqsVmtA9uF2u+VwODRlyhRFRkYGZM5QQn9mo7/QNzT/+tfyWcI9WjmmRU8dCperJew2VnVjx/IDc+q+Mzx+N0J/gdN6BqUtfgeYqKgoDRw4UJI0evRoHTx4UOvWrdN3v/tdNTU16dy5cz5HYerq6mSz2SRJNptNBw4c8Jmv9VVKV475+1cu1dXVyWq1XvfoiyRZLBZZLJar1kdGRgb8m90Rc4YS+jMb/YUuV3PbwcTVEnZT424Xnj/9Q3+B2cfNuOX3gWlpaZHL5dLo0aMVGRmpsrIy77YTJ06opqZGdrtdkmS323X06FHV19d7xzgcDlmtVqWkpHjHXDlH65jWOQAAAPw6ApOXl6epU6eqf//+On/+vIqLi1VeXq6SkhLFxMRo1qxZys3NVWxsrKxWq+bPny+73a7x48dLktLT05WSkqIZM2aooKBAtbW1Wrp0qXJycrxHT+bMmaOXXnpJixcv1uOPP65du3Zpy5Yt2r59e+C7BwAARvIrwNTX12vmzJk6c+aMYmJiNHz4cJWUlGjKlCmSpLVr1yo8PFxZWVlyuVzKyMjQyy+/7L1/RESEtm3bprlz58put6t79+7Kzs7WihUrvGOSk5O1fft2LVy4UOvWrVO/fv306quv8hJqAADg5VeAee211264vWvXriosLFRhYeF1xyQlJbV5VXtqaqoOHz7sT2kAAOAOwmchAQAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADG8SvArFq1Sl/96lfVs2dPxcXF6aGHHtKJEyd8xqSmpiosLMznNmfOHJ8xNTU1yszMVHR0tOLi4rRo0SJdvnzZZ0x5ebnuvfdeWSwWDRw4UEVFRe3rEAAAdDp+BZjdu3crJydH+/btk8PhkNvtVnp6uhobG33GzZ49W2fOnPHeCgoKvNuam5uVmZmppqYm7d27V6+//rqKioq0bNky75hTp04pMzNTEydOVFVVlRYsWKAnnnhCJSUlt9guAADoDLr4M/j999/3WS4qKlJcXJwqKys1YcIE7/ro6GjZbLZrzlFaWqrjx49r586dio+P18iRI7Vy5UotWbJE+fn5ioqK0oYNG5ScnKw1a9ZIkoYMGaIPPvhAa9euVUZGhr89AgCATsavAPP3GhoaJEmxsbE+6zdt2qQ33nhDNptN06ZN01NPPaXo6GhJUkVFhYYNG6b4+Hjv+IyMDM2dO1fV1dUaNWqUKioqlJaW5jNnRkaGFixYcN1aXC6XXC6Xd9npdEqS3G633G73rbTp1TpPoOYLNfRnNvoLfZYIz/W3hXt8voYKnj9vDv0Ffl9tCfN4PO36bWlpadE3v/lNnTt3Th988IF3/S9/+UslJSUpISFBR44c0ZIlSzR27Fi9/fbbkqQnn3xS//u//+tzOujixYvq3r27duzYoalTp+orX/mKHnvsMeXl5XnH7NixQ5mZmbp48aK6det2VT35+flavnz5VeuLi4u94QkAAIS2ixcv6tFHH1VDQ4OsVut1x7X7CExOTo6OHTvmE16kvwWUVsOGDVPfvn01efJknTx5UgMGDGjv7tqUl5en3Nxc77LT6VRiYqLS09Nv+A3wh9vtlsPh0JQpUxQZGRmQOUMJ/ZmN/kLf0PzrX8dnCfdo5ZgWPXUoXK6WsNtY1Y0dyw/MafvO8PjdCP0FTusZlLa0K8DMmzdP27Zt0549e9SvX78bjh03bpwk6aOPPtKAAQNks9l04MABnzF1dXWS5L1uxmazedddOcZqtV7z6IskWSwWWSyWq9ZHRkYG/JvdEXOGEvozG/2FLldz28HE1RJ2U+NuF54//UN/gdnHzfDrVUgej0fz5s3T1q1btWvXLiUnJ7d5n6qqKklS3759JUl2u11Hjx5VfX29d4zD4ZDValVKSop3TFlZmc88DodDdrvdn3IBAEAn5VeAycnJ0RtvvKHi4mL17NlTtbW1qq2t1RdffCFJOnnypFauXKnKykp9/PHH+s1vfqOZM2dqwoQJGj58uCQpPT1dKSkpmjFjhv77v/9bJSUlWrp0qXJycrxHUObMmaP/+Z//0eLFi/Xhhx/q5Zdf1pYtW7Rw4cIAtw8AAEzkV4BZv369GhoalJqaqr59+3pvmzdvliRFRUVp586dSk9P1+DBg/WjH/1IWVlZeu+997xzREREaNu2bYqIiJDdbtf3v/99zZw5UytWrPCOSU5O1vbt2+VwODRixAitWbNGr776Ki+hBgAAkvy8BqatFywlJiZq9+7dbc6TlJSkHTt23HBMamqqDh8+7E95AADgDsFnIQEAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjNPuD3MEgGC6+8fbg10CgCDiCAwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4fgWYVatW6atf/ap69uypuLg4PfTQQzpx4oTPmEuXLiknJ0d9+vRRjx49lJWVpbq6Op8xNTU1yszMVHR0tOLi4rRo0SJdvnzZZ0x5ebnuvfdeWSwWDRw4UEVFRe3rEAAAdDp+BZjdu3crJydH+/btk8PhkNvtVnp6uhobG71jFi5cqPfee09vvfWWdu/erdOnT+vhhx/2bm9ublZmZqaampq0d+9evf766yoqKtKyZcu8Y06dOqXMzExNnDhRVVVVWrBggZ544gmVlJQEoGUAAGC6Lv4Mfv/9932Wi4qKFBcXp8rKSk2YMEENDQ167bXXVFxcrEmTJkmSNm7cqCFDhmjfvn0aP368SktLdfz4ce3cuVPx8fEaOXKkVq5cqSVLlig/P19RUVHasGGDkpOTtWbNGknSkCFD9MEHH2jt2rXKyMgIUOsAAMBUt3QNTENDgyQpNjZWklRZWSm32620tDTvmMGDB6t///6qqKiQJFVUVGjYsGGKj4/3jsnIyJDT6VR1dbV3zJVztI5pnQMAANzZ/DoCc6WWlhYtWLBAX//61zV06FBJUm1traKiotSrVy+fsfHx8aqtrfWOuTK8tG5v3XajMU6nU1988YW6det2VT0ul0sul8u77HQ6JUlut1tut7u9bfponSdQ84Ua+jPbndafJcITzHICzhLu8fkaKnj+vDn0F/h9taXdASYnJ0fHjh3TBx980N4pAmrVqlVavnz5VetLS0sVHR0d0H05HI6Azhdq6M9sd0p/BWODXEgHWTmmJdgl+NixY0dA57tTfj47q9vR38WLF29qXLsCzLx587Rt2zbt2bNH/fr186632WxqamrSuXPnfI7C1NXVyWazecccOHDAZ77WVyldOebvX7lUV1cnq9V6zaMvkpSXl6fc3FzvstPpVGJiotLT02W1WtvT5lXcbrccDoemTJmiyMjIgMwZSujPbHdaf0PzO9dF/ZZwj1aOadFTh8LlagkLdjlex/IDc93hnfbz2dnczv5az6C0xa8A4/F4NH/+fG3dulXl5eVKTk722T569GhFRkaqrKxMWVlZkqQTJ06opqZGdrtdkmS32/Xss8+qvr5ecXFxkv6W6KxWq1JSUrxj/j71OxwO7xzXYrFYZLFYrlofGRkZ8G92R8wZSujPbHdKf67m0PkjH0iulrCQ6o3nT//QX2D2cTP8CjA5OTkqLi7Wu+++q549e3qvWYmJiVG3bt0UExOjWbNmKTc3V7GxsbJarZo/f77sdrvGjx8vSUpPT1dKSopmzJihgoIC1dbWaunSpcrJyfEGkDlz5uill17S4sWL9fjjj2vXrl3asmWLtm/f7k+5AACgk/LrVUjr169XQ0ODUlNT1bdvX+9t8+bN3jFr167VP/7jPyorK0sTJkyQzWbT22+/7d0eERGhbdu2KSIiQna7Xd///vc1c+ZMrVixwjsmOTlZ27dvl8Ph0IgRI7RmzRq9+uqrvIQaAABIascppLZ07dpVhYWFKiwsvO6YpKSkNi8MS01N1eHDh/0pDwAA3CH4LCQAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDh+B5g9e/Zo2rRpSkhIUFhYmN555x2f7T/4wQ8UFhbmc7v//vt9xpw9e1bTp0+X1WpVr169NGvWLF24cMFnzJEjR/SNb3xDXbt2VWJiogoKCvzvDgAAdEp+B5jGxkaNGDFChYWF1x1z//3368yZM97bf/7nf/psnz59uqqrq+VwOLRt2zbt2bNHTz75pHe70+lUenq6kpKSVFlZqZ/97GfKz8/XL3/5S3/LBQAAnVAXf+8wdepUTZ069YZjLBaLbDbbNbf96U9/0vvvv6+DBw9qzJgxkqQXX3xRDzzwgH7+858rISFBmzZtUlNTk371q18pKipK99xzj6qqqvT888/7BB0AAHBn8jvA3Izy8nLFxcWpd+/emjRpkp555hn16dNHklRRUaFevXp5w4skpaWlKTw8XPv379e3vvUtVVRUaMKECYqKivKOycjI0E9/+lP99a9/Ve/eva/ap8vlksvl8i47nU5JktvtltvtDkhfrfMEar5QQ39mu9P6s0R4gllOwFnCPT5fQwXPnzeH/gK/r7YEPMDcf//9evjhh5WcnKyTJ0/qJz/5iaZOnaqKigpFRESotrZWcXFxvkV06aLY2FjV1tZKkmpra5WcnOwzJj4+3rvtWgFm1apVWr58+VXrS0tLFR0dHaj2JEkOhyOg84Ua+jPbndJfwdggF9JBVo5pCXYJPnbs2BHQ+e6Un8/O6nb0d/HixZsaF/AA88gjj3j/PWzYMA0fPlwDBgxQeXm5Jk+eHOjdeeXl5Sk3N9e77HQ6lZiYqPT0dFmt1oDsw+12y+FwaMqUKYqMjAzInKGE/sx2p/U3NL8k2CUFlCXco5VjWvTUoXC5WsKCXU7A3c7+juVndOj813Kn/f51pNYzKG3pkFNIV/ryl7+sL33pS/roo480efJk2Ww21dfX+4y5fPmyzp49671uxmazqa6uzmdM6/L1rq2xWCyyWCxXrY+MjAz4N7sj5gwl9Ge2O6U/V3Pn+yMvSa6WsE7bm3R7+gvmz/+d8vvX0fu4GR3+PjCffvqpPv/8c/Xt21eSZLfbde7cOVVWVnrH7Nq1Sy0tLRo3bpx3zJ49e3zOgzkcDg0aNOiap48AAMCdxe8Ac+HCBVVVVamqqkqSdOrUKVVVVammpkYXLlzQokWLtG/fPn388ccqKyvTgw8+qIEDByoj42+H9IYMGaL7779fs2fP1oEDB/SHP/xB8+bN0yOPPKKEhARJ0qOPPqqoqCjNmjVL1dXV2rx5s9atW+dziggAANy5/A4whw4d0qhRozRq1ChJUm5urkaNGqVly5YpIiJCR44c0Te/+U195Stf0axZszR69Gj9/ve/9zm9s2nTJg0ePFiTJ0/WAw88oPvuu8/nPV5iYmJUWlqqU6dOafTo0frRj36kZcuW8RJqAAAgqR3XwKSmpsrjuf7L/EpK2r6wLjY2VsXFxTccM3z4cP3+97/3tzwAAHAH4LOQAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADj+B1g9uzZo2nTpikhIUFhYWF65513fLZ7PB4tW7ZMffv2Vbdu3ZSWlqY///nPPmPOnj2r6dOny2q1qlevXpo1a5YuXLjgM+bIkSP6xje+oa5duyoxMVEFBQX+dwcAADolvwNMY2OjRowYocLCwmtuLygo0C9+8Qtt2LBB+/fvV/fu3ZWRkaFLly55x0yfPl3V1dVyOBzatm2b9uzZoyeffNK73el0Kj09XUlJSaqsrNTPfvYz5efn65e//GU7WgQAAJ1NF3/vMHXqVE2dOvWa2zwej1544QUtXbpUDz74oCTp3//93xUfH6933nlHjzzyiP70pz/p/fff18GDBzVmzBhJ0osvvqgHHnhAP//5z5WQkKBNmzapqalJv/rVrxQVFaV77rlHVVVVev75532CDgAAuDP5HWBu5NSpU6qtrVVaWpp3XUxMjMaNG6eKigo98sgjqqioUK9evbzhRZLS0tIUHh6u/fv361vf+pYqKio0YcIERUVFecdkZGTopz/9qf7617+qd+/eV+3b5XLJ5XJ5l51OpyTJ7XbL7XYHpL/WeQI1X6ihP7Pdaf1ZIjzBLCfgLOEen6+dze3sLxi/A3fa79/t2FdbAhpgamtrJUnx8fE+6+Pj473bamtrFRcX51tEly6KjY31GZOcnHzVHK3brhVgVq1apeXLl1+1vrS0VNHR0e3s6NocDkdA5ws19Ge2O6W/grFBLqSDrBzTEuwSOtTt6G/Hjh0dvo/ruVN+/zrSxYsXb2pcQANMMOXl5Sk3N9e77HQ6lZiYqPT0dFmt1oDsw+12y+FwaMqUKYqMjAzInKGE/sx2p/U3NL8k2CUFlCXco5VjWvTUoXC5WsKCXU7A3c7+juVndOj813Kn/f51pNYzKG0JaICx2WySpLq6OvXt29e7vq6uTiNHjvSOqa+v97nf5cuXdfbsWe/9bTab6urqfMa0LreO+XsWi0UWi+Wq9ZGRkQH/ZnfEnKGE/sx2p/Tnau58f+QlydUS1ml7k25Pf8H8+b9Tfv86eh83I6DvA5OcnCybzaaysjLvOqfTqf3798tut0uS7Ha7zp07p8rKSu+YXbt2qaWlRePGjfOO2bNnj895MIfDoUGDBl3z9BEAALiz+B1gLly4oKqqKlVVVUn624W7VVVVqqmpUVhYmBYsWKBnnnlGv/nNb3T06FHNnDlTCQkJeuihhyRJQ4YM0f3336/Zs2frwIED+sMf/qB58+bpkUceUUJCgiTp0UcfVVRUlGbNmqXq6mpt3rxZ69at8zlFBAAA7lx+n0I6dOiQJk6c6F1uDRXZ2dkqKirS4sWL1djYqCeffFLnzp3Tfffdp/fff19du3b13mfTpk2aN2+eJk+erPDwcGVlZekXv/iFd3tMTIxKS0uVk5Oj0aNH60tf+pKWLVvGS6gBAICkdgSY1NRUeTzXfxlcWFiYVqxYoRUrVlx3TGxsrIqLi2+4n+HDh+v3v/+9v+UBAIA7AJ+FBAAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHG6BLsAAMF394+3B7uENlkiPCoYKw3NL5GrOSzY5QAIMo7AAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxAh5g8vPzFRYW5nMbPHiwd/ulS5eUk5OjPn36qEePHsrKylJdXZ3PHDU1NcrMzFR0dLTi4uK0aNEiXb58OdClAgAAQ3XIZyHdc8892rlz5//tpMv/7WbhwoXavn273nrrLcXExGjevHl6+OGH9Yc//EGS1NzcrMzMTNlsNu3du1dnzpzRzJkzFRkZqeeee64jygUAAIbpkADTpUsX2Wy2q9Y3NDTotddeU3FxsSZNmiRJ2rhxo4YMGaJ9+/Zp/PjxKi0t1fHjx7Vz507Fx8dr5MiRWrlypZYsWaL8/HxFRUV1RMkAAMAgHXINzJ///GclJCToy1/+sqZPn66amhpJUmVlpdxut9LS0rxjBw8erP79+6uiokKSVFFRoWHDhik+Pt47JiMjQ06nU9XV1R1RLgAAMEzAj8CMGzdORUVFGjRokM6cOaPly5frG9/4ho4dO6ba2lpFRUWpV69ePveJj49XbW2tJKm2ttYnvLRub912PS6XSy6Xy7vsdDolSW63W263OxCteecJ1Hyhhv7Mdiv9WSI8gS4n4CzhHp+vnQ39BU4wfsd5fgn8vtoS8AAzdepU77+HDx+ucePGKSkpSVu2bFG3bt0CvTuvVatWafny5VetLy0tVXR0dED35XA4AjpfqKE/s7Wnv4KxHVBIB1k5piXYJXQo+rt1O3bs6PB9XA/PL7fu4sWLNzWuQ66BuVKvXr30la98RR999JGmTJmipqYmnTt3zucoTF1dnfeaGZvNpgMHDvjM0foqpWtdV9MqLy9Pubm53mWn06nExESlp6fLarUGpBe32y2Hw6EpU6YoMjIyIHOGEvoz2630NzS/pIOqChxLuEcrx7ToqUPhcrWEBbucgKO/wDmWn9Gh818Lzy+B03oGpS0dHmAuXLigkydPasaMGRo9erQiIyNVVlamrKwsSdKJEydUU1Mju90uSbLb7Xr22WdVX1+vuLg4SX9LfFarVSkpKdfdj8VikcViuWp9ZGRkwL/ZHTFnKKE/s7WnP1ezOX8wXS1hRtXrL/q7dcH8/eb5JTD7uBkBDzD/+q//qmnTpikpKUmnT5/W008/rYiICH3ve99TTEyMZs2apdzcXMXGxspqtWr+/Pmy2+0aP368JCk9PV0pKSmaMWOGCgoKVFtbq6VLlyonJ+eaAQUAANx5Ah5gPv30U33ve9/T559/rrvuukv33Xef9u3bp7vuukuStHbtWoWHhysrK0sul0sZGRl6+eWXvfePiIjQtm3bNHfuXNntdnXv3l3Z2dlasWJFoEsFAACGCniAefPNN2+4vWvXriosLFRhYeF1xyQlJQX1IiwAABDa+CwkAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA43QJdgFAZ3P3j7cHZb+WCI8KxkpD80vkag4LSg0AcLtwBAYAABiHIzAAgE4lGEdBb/UI6MerMzugqs6NIzAAAMA4BBgAAGAcAgwAADAOAQYAABiHi3gR0gJ5MR4vMwaAzoMjMAAAwDgEGAAAYJyQDjCFhYW6++671bVrV40bN04HDhwIdkkAACAEhGyA2bx5s3Jzc/X000/rj3/8o0aMGKGMjAzV19cHuzQAABBkIRtgnn/+ec2ePVuPPfaYUlJStGHDBkVHR+tXv/pVsEsDAABBFpKvQmpqalJlZaXy8vK868LDw5WWlqaKiopr3sflcsnlcnmXGxoaJElnz56V2+0OSF1ut1sXL17U559/rsjIyIDMebuMW1XW5hhLuEdLR7Vo5P/3tlwtofEqnUD+gHZp8ejixRZ1cYerOUT6CyT6Mxv9me1W+/v88887oKrAuZ1//86fPy9J8ng8Nx7oCUGfffaZR5Jn7969PusXLVrkGTt27DXv8/TTT3skcePGjRs3btw6we2TTz65YVYIySMw7ZGXl6fc3FzvcktLi86ePas+ffooLCwwad/pdCoxMVGffPKJrFZrQOYMJfRnNvozG/2Zjf4Cx+Px6Pz580pISLjhuJAMMF/60pcUERGhuro6n/V1dXWy2WzXvI/FYpHFYvFZ16tXrw6pz2q1dsof0Fb0Zzb6Mxv9mY3+AiMmJqbNMSF5EW9UVJRGjx6tsrL/u26jpaVFZWVlstvtQawMAACEgpA8AiNJubm5ys7O1pgxYzR27Fi98MILamxs1GOPPRbs0gAAQJCFbID57ne/q7/85S9atmyZamtrNXLkSL3//vuKj48PWk0Wi0VPP/30VaeqOgv6Mxv9mY3+zEZ/t1+Yx9PW65QAAABCS0heAwMAAHAjBBgAAGAcAgwAADAOAQYAABiHANNOd999t8LCwnxuq1evDnZZAedyuTRy5EiFhYWpqqoq2OUEzDe/+U31799fXbt2Vd++fTVjxgydPn062GUFxMcff6xZs2YpOTlZ3bp104ABA/T000+rqakp2KUFzLPPPquvfe1rio6O7rA3rLzdCgsLdffdd6tr164aN26cDhw4EOySAmLPnj2aNm2aEhISFBYWpnfeeSfYJQXUqlWr9NWvflU9e/ZUXFycHnroIZ04cSLYZQXM+vXrNXz4cO8b2Nntdv32t78NdlmSCDC3ZMWKFTpz5oz3Nn/+/GCXFHCLFy9u8+2cTTRx4kRt2bJFJ06c0H/913/p5MmT+va3vx3ssgLiww8/VEtLi1555RVVV1dr7dq12rBhg37yk58Eu7SAaWpq0ne+8x3NnTs32KUExObNm5Wbm6unn35af/zjHzVixAhlZGSovr4+2KXdssbGRo0YMUKFhYXBLqVD7N69Wzk5Odq3b58cDofcbrfS09PV2NgY7NICol+/flq9erUqKyt16NAhTZo0SQ8++KCqq6uDXZpC8sMcTZCUlORZu3ZtsMvoUDt27PAMHjzYU11d7ZHkOXz4cLBL6jDvvvuuJywszNPU1BTsUjpEQUGBJzk5OdhlBNzGjRs9MTExwS7jlo0dO9aTk5PjXW5ubvYkJCR4Vq1aFcSqAk+SZ+vWrcEuo0PV19d7JHl2794d7FI6TO/evT2vvvpqsMvwcATmFqxevVp9+vTRqFGj9LOf/UyXL18OdkkBU1dXp9mzZ+s//uM/FB0dHexyOtTZs2e1adMmfe1rX+vwj4kPloaGBsXGxga7DFxDU1OTKisrlZaW5l0XHh6utLQ0VVRUBLEytEdDQ4Mkdcrft+bmZr355ptqbGwMiY/1IcC00z//8z/rzTff1O9+9zv98Ic/1HPPPafFixcHu6yA8Hg8+sEPfqA5c+ZozJgxwS6nwyxZskTdu3dXnz59VFNTo3fffTfYJXWIjz76SC+++KJ++MMfBrsUXMP/+3//T83NzVe9y3h8fLxqa2uDVBXao6WlRQsWLNDXv/51DR06NNjlBMzRo0fVo0cPWSwWzZkzR1u3blVKSkqwyyLAXOnHP/7xVRfm/v3tww8/lPS3z2pKTU3V8OHDNWfOHK1Zs0YvvviiXC5XkLu4vpvt78UXX9T58+eVl5cX7JL94s/jJ0mLFi3S4cOHVVpaqoiICM2cOVOeEH5jan/7k6TPPvtM999/v77zne9o9uzZQar85rSnPyCU5OTk6NixY3rzzTeDXUpADRo0SFVVVdq/f7/mzp2r7OxsHT9+PNhl8VECV/rLX/6izz///IZjvvzlLysqKuqq9dXV1Ro6dKg+/PBDDRo0qKNKvCU3298//dM/6b333lNYWJh3fXNzsyIiIjR9+nS9/vrrHV1qu9zK4/fpp58qMTFRe/fuDYlDo9fib3+nT59Wamqqxo8fr6KiIoWHh/b/V9rz+BUVFWnBggU6d+5cB1fXcZqamhQdHa1f//rXeuihh7zrs7Ozde7cuU51ZDAsLExbt2716bOzmDdvnt59913t2bNHycnJwS6nQ6WlpWnAgAF65ZVXglpHyH6YYzDcdddduuuuu9p136qqKoWHhysuLi7AVQXOzfb3i1/8Qs8884x3+fTp08rIyNDmzZs1bty4jizxltzK49fS0iJJIX0EzZ/+PvvsM02cOFGjR4/Wxo0bQz68SLf2+JksKipKo0ePVllZmfcPe0tLi8rKyjRv3rzgFoc2eTwezZ8/X1u3blV5eXmnDy/S334+Q+G5kgDTDhUVFdq/f78mTpyonj17qqKiQgsXLtT3v/999e7dO9jl3bL+/fv7LPfo0UOSNGDAAPXr1y8YJQXU/v37dfDgQd13333q3bu3Tp48qaeeekoDBgwI2aMv/vjss8+UmpqqpKQk/fznP9df/vIX7zabzRbEygKnpqZGZ8+eVU1NjZqbm73vUTRw4EDvz6tJcnNzlZ2drTFjxmjs2LF64YUX1NjYqMceeyzYpd2yCxcu6KOPPvIunzp1SlVVVYqNjb3qucZEOTk5Ki4u1rvvvquePXt6r1uKiYlRt27dglzdrcvLy9PUqVPVv39/nT9/XsXFxSovL1dJSUmwS+Nl1O1RWVnpGTdunCcmJsbTtWtXz5AhQzzPPfec59KlS8EurUOcOnWqU72M+siRI56JEyd6YmNjPRaLxXP33Xd75syZ4/n000+DXVpAbNy40SPpmrfOIjs7+5r9/e53vwt2ae324osvevr37++JioryjB071rNv375glxQQv/vd7675WGVnZwe7tIC43u/axo0bg11aQDz++OOepKQkT1RUlOeuu+7yTJ482VNaWhrssjwej8fDNTAAAMA4oX9iHAAA4O8QYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgnP8f32PdttCIuM4AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import shapiro\n",
        "\n",
        "# Perform the Shapiro-Wilk test\n",
        "stat, p = shapiro(res.resid)\n",
        "\n",
        "# Print the test statistic and the p-value\n",
        "print(\"Test Statistic:\", stat)\n",
        "print(\"P-Value:\", p)\n",
        "\n",
        "# Interpret the result\n",
        "alpha = 0.05  # Significance level\n",
        "if p > alpha:\n",
        "    print(\"Sample looks Gaussian (fail to reject H0)\")\n",
        "else:\n",
        "    print(\"Sample does not look Gaussian (reject H0)\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tzgJPCdETSwO",
        "outputId": "710d369a-0ee1-4d7b-9b1a-6116fefddcfe"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Statistic: 0.9568549394607544\n",
            "P-Value: 1.401298464324817e-45\n",
            "Sample does not look Gaussian (reject H0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/scipy/stats/_morestats.py:1882: UserWarning: p-value may not be accurate for N > 5000.\n",
            "  warnings.warn(\"p-value may not be accurate for N > 5000.\")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import scipy.stats as stats\n",
        "fig, ax = plt.subplots(figsize=(8, 6))\n",
        "stats.probplot(res.resid, dist=\"norm\", plot=ax)\n",
        "ax.set_title('QQ Plot of Residuals')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "7HoThDSzTAMq",
        "outputId": "f7806eb3-81e1-4b09-ed74-b005a9edf88d"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}