OLS_2_Ajuste_Propiedades_Test_de_Hipótesis.ipynb

{
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  "metadata": {
    "colab": {
      "name": "OLS 2 Ajuste Propiedades Test de Hipótesis.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPr8JLjKdSDH1PyMx2n6N9x",
      "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/herramientas-econometricas/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",
        "outputId": "96638075-177a-4a8a-cbc4-09f0880c1417",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "import statsmodels.api as sm\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OTliy8gp0cuf"
      },
      "source": [
        "df=pd.read_csv('https://github.com/rpasquini/herramientas-econometricas/blob/main/datos/eah2016_individual.csv?raw=true')"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ymxAvnv7vXrV",
        "outputId": "5eb03cb8-93f0-4581-8f81-1ac3ec895d7d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "df.aesc.hist()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fade7cb5b00>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qVD7lheoveIN"
      },
      "source": [
        "df=df.loc[df.aesc<20]"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4ZcVS617w1kt",
        "outputId": "be0f55dc-0781-4967-94e0-92b5b589b9bb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        }
      },
      "source": [
        "# dropna elimina filas que para algun valor hay un missing\n",
        "df[['aesc','logingreso']].dropna()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>aesc</th>\n",
              "      <th>logingreso</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12</td>\n",
              "      <td>8.384804</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12</td>\n",
              "      <td>9.104980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12</td>\n",
              "      <td>8.482602</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12</td>\n",
              "      <td>9.615806</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12</td>\n",
              "      <td>9.615806</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14507</th>\n",
              "      <td>8</td>\n",
              "      <td>8.748305</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14508</th>\n",
              "      <td>15</td>\n",
              "      <td>9.952278</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14509</th>\n",
              "      <td>4</td>\n",
              "      <td>9.210340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14510</th>\n",
              "      <td>12</td>\n",
              "      <td>8.922658</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14511</th>\n",
              "      <td>11</td>\n",
              "      <td>9.190138</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10082 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       aesc  logingreso\n",
              "0        12    8.384804\n",
              "1        12    9.104980\n",
              "2        12    8.482602\n",
              "3        12    9.615806\n",
              "4        12    9.615806\n",
              "...     ...         ...\n",
              "14507     8    8.748305\n",
              "14508    15    9.952278\n",
              "14509     4    9.210340\n",
              "14510    12    8.922658\n",
              "14511    11    9.190138\n",
              "\n",
              "[10082 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X2MRv2sjvlKK",
        "outputId": "28baf949-004b-4e59-e403-e94d84d8d1ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "df.aesc.hist(bins=40)"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7faddc149be0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAD4CAYAAAAAczaOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAT2klEQVR4nO3df6zddX3H8ed7/FDDJbQMd1dLs+LSmaCNDG6ATWduw4RSFqvGGAjDophqBotmLLNqFKKS1E00YziWKo0wmVc2dTZQh7WjIf6BQkmlFNResU5uShuFFSvEre69P87nkuPlnHtPz7nnnFs+z0dycr/n8/18z/d9Pvfb1/328/3ecyMzkSTV4beGXYAkaXAMfUmqiKEvSRUx9CWpIoa+JFXk+GEXMJvTTjstly9f3vX2v/zlLznppJPmr6B5Zn29sb7eWF9vFnJ9O3fu/FlmvrzlysxcsI9zzjkne3Hvvff2tH2/WV9vrK831tebhVwf8GC2yVWndySpIoa+JFVkztCPiGURcW9EPBoReyLifaX9+oiYiohd5bGmaZsPRsRkRPwgIi5qal9d2iYjYkN/3pIkqZ1OLuQeAa7NzIci4mRgZ0RsK+s+k5mfau4cEWcClwKvBl4BfCsi/qCs/izwRuAJ4IGI2JKZj87HG5EkzW3O0M/M/cD+svyLiHgMWDrLJmuBicz8FfDjiJgEzi3rJjPzcYCImCh9DX1JGpDIo/jAtYhYDtwHvAb4K+BK4BngQRr/G3g6Im4G7s/ML5ZtbgW+UV5idWa+u7RfAZyXmdfM2Md6YD3A6OjoORMTE92+Nw4fPszIyEjX2/eb9fXG+npjfb1ZyPWtWrVqZ2aOtVzZ7raemQ9gBNgJvLU8HwWOo3Fd4AZgc2m/Gfjzpu1uBd5WHp9var8CuHm2fXrL5nBZX2+srzfW1z1muWWzo1/OiogTgK8Ad2TmV8sPiwNN6z8H3FWeTgHLmjY/vbQxS7skaQA6uXsnaJytP5aZn25qX9LU7S3AI2V5C3BpRLwkIs4AVgDfBR4AVkTEGRFxIo2LvVvm521IkjrRyZn+62hMxeyOiF2l7UPAZRFxFpDAPuA9AJm5JyLupHGB9ghwdWb+GiAirgHuoTEttDkz98zje5EELN9w96zr9228ZECVaCHq5O6dbwPRYtXWWba5gcY8/8z2rbNtJ0nqL38jV5IqYuhLUkUMfUmqiKEvSRUx9CWpIoa+JFXE0Jekihj6klQRQ1+SKmLoS1JFDH1JqoihL0kVMfQlqSKGviRVxNCXpIoY+pJUEUNfkipi6EtSRQx9SaqIoS9JFTH0Jakihr4kVcTQl6SKGPqSVBFDX5IqYuhLUkUMfUmqiKEvSRUx9CWpIoa+JFXE0Jekihj6klQRQ1+SKjJn6EfEsoi4NyIejYg9EfG+0n5qRGyLiL3l6+LSHhFxU0RMRsTDEXF202utK/33RsS6/r0tSVIrnZzpHwGuzcwzgfOBqyPiTGADsD0zVwDby3OAi4EV5bEeuAUaPySA64DzgHOB66Z/UEiSBmPO0M/M/Zn5UFn+BfAYsBRYC9xWut0GvLksrwVuz4b7gUURsQS4CNiWmU9l5tPANmD1vL4bSdKsIjM77xyxHLgPeA3wX5m5qLQH8HRmLoqIu4CNmfntsm478AFgHHhpZn6itH8EeC4zPzVjH+tp/A+B0dHRcyYmJrp+c4cPH2ZkZKTr7fvN+npjfa3tnjo06/qVS08BHL9eLeT6Vq1atTMzx1qtO77TF4mIEeArwPsz85lGzjdkZkZE5z89ZpGZm4BNAGNjYzk+Pt71a+3YsYNetu836+uN9bV25Ya7Z12/7/JxwPHr1UKvr52O7t6JiBNoBP4dmfnV0nygTNtQvh4s7VPAsqbNTy9t7dolSQPSyd07AdwKPJaZn25atQWYvgNnHfD1pvZ3lLt4zgcOZeZ+4B7gwohYXC7gXljaJEkD0sn0zuuAK4DdEbGrtH0I2AjcGRFXAT8B3l7WbQXWAJPAs8A7ATLzqYj4OPBA6fexzHxqXt6FJKkjc4Z+uSAbbVZf0KJ/Ale3ea3NwOajKVCSNH/8jVxJqoihL0kVMfQlqSKGviRVxNCXpIoY+pJUEUNfkipi6EtSRQx9SaqIoS9JFTH0Jakihr4kVcTQl6SKGPqSVBFDX5IqYuhLUkUMfUmqiKEvSRUx9CWpIoa+JFXE0Jekihj6klQRQ1+SKmLoS1JFDH1JqoihL0kVMfQlqSKGviRVxNCXpIoY+pJUkeOHXYAkDdPyDXfPun7fxksGVMlgeKYvSRUx9CWpInOGfkRsjoiDEfFIU9v1ETEVEbvKY03Tug9GxGRE/CAiLmpqX13aJiNiw/y/FUnSXDo50/8CsLpF+2cy86zy2AoQEWcClwKvLtv8Y0QcFxHHAZ8FLgbOBC4rfSVJAzTnhdzMvC8ilnf4emuBicz8FfDjiJgEzi3rJjPzcYCImCh9Hz3qiiVJXYvMnLtTI/TvyszXlOfXA1cCzwAPAtdm5tMRcTNwf2Z+sfS7FfhGeZnVmfnu0n4FcF5mXtNiX+uB9QCjo6PnTExMdP3mDh8+zMjISNfb95v19cb6Wts9dWjW9SuXngI4ftM6Ha+ZFvL4rVq1amdmjrVa1+0tm7cAHweyfL0ReFeXr/UbMnMTsAlgbGwsx8fHu36tHTt20Mv2/WZ9vbG+1q6c6xbEy8cBx29ap+M100Ifv3a6Cv3MPDC9HBGfA+4qT6eAZU1dTy9tzNIuSRqQrm7ZjIglTU/fAkzf2bMFuDQiXhIRZwArgO8CDwArIuKMiDiRxsXeLd2XLUnqxpxn+hHxJWAcOC0ingCuA8Yj4iwa0zv7gPcAZOaeiLiTxgXaI8DVmfnr8jrXAPcAxwGbM3PPvL8bSdKsOrl757IWzbfO0v8G4IYW7VuBrUdVnSRpXvkbuZJUEUNfkipi6EtSRQx9SaqIoS9JFTH0Jakihr4kVcTQl6SKGPqSVBFDX5IqYuhLUkW6/Tx9SZpXy2d8rv21K488/1n3+zZeMoySXpQ805ekihj6klQRQ1+SKmLoS1JFDH1JqoihL0kVMfQlqSLep6+hm3l/9kzeoy3NH8/0Jakihr4kVcTQl6SKGPqSVBFDX5IqYuhLUkUMfUmqiKEvSRUx9CWpIoa+JFXE0Jekihj6klQRQ1+SKjJn6EfE5og4GBGPNLWdGhHbImJv+bq4tEdE3BQRkxHxcESc3bTNutJ/b0Ss68/bkSTNppMz/S8Aq2e0bQC2Z+YKYHt5DnAxsKI81gO3QOOHBHAdcB5wLnDd9A8KSdLgzBn6mXkf8NSM5rXAbWX5NuDNTe23Z8P9wKKIWAJcBGzLzKcy82lgGy/8QSJJ6rNu5/RHM3N/WX4SGC3LS4GfNvV7orS1a5ckDVBk5tydIpYDd2Xma8rz/87MRU3rn87MxRFxF7AxM79d2rcDHwDGgZdm5idK+0eA5zLzUy32tZ7G1BCjo6PnTExMdP3mDh8+zMjISNfb95v1NeyeOjTr+pVLT2nZ7vi11ul4LrTxm1n36MvgwHON5XbHQD/2O9OxePytWrVqZ2aOtVrX7Z9LPBARSzJzf5m+OVjap4BlTf1OL21TNIK/uX1HqxfOzE3AJoCxsbEcHx9v1a0jO3bsoJft+836Gq6c688lXt66BsevtU7Hc6GN38y6r115hBt3NyKq3THQj/3OdKwef+10O72zBZi+A2cd8PWm9neUu3jOBw6VaaB7gAsjYnG5gHthaZMkDdCcZ/oR8SUaZ+mnRcQTNO7C2QjcGRFXAT8B3l66bwXWAJPAs8A7ATLzqYj4OPBA6fexzJx5cViS1Gdzhn5mXtZm1QUt+iZwdZvX2QxsPqrqJEnzyt/IlaSKGPqSVBFDX5IqYuhLUkUMfUmqiKEvSRUx9CWpIoa+JFXE0Jekihj6klQRQ1+SKmLoS1JFDH1JqoihL0kVMfQlqSKGviRVxNCXpIoY+pJUEUNfkipi6EtSRQx9SaqIoS9JFTH0Jakihr4kVcTQl6SKHD/sAiS9OCzfcPes6/dtvGRAlWg2nulLUkUMfUmqiKEvSRUx9CWpIoa+JFXE0Jekihj6klSRnkI/IvZFxO6I2BURD5a2UyNiW0TsLV8Xl/aIiJsiYjIiHo6Is+fjDUiSOjcfZ/qrMvOszBwrzzcA2zNzBbC9PAe4GFhRHuuBW+Zh35Kko9CP6Z21wG1l+TbgzU3tt2fD/cCiiFjSh/1LktroNfQT+GZE7IyI9aVtNDP3l+UngdGyvBT4adO2T5Q2SdKARGZ2v3HE0syciojfAbYBfwlsycxFTX2ezszFEXEXsDEzv13atwMfyMwHZ7zmehrTP4yOjp4zMTHRdX2HDx9mZGSk6+37zfoadk8dmnX9yqWntGx3/FrrdDznu75uv4/tth99GRx4rrNte/FiPP5WrVq1s2nK/Tf09IFrmTlVvh6MiK8B5wIHImJJZu4v0zcHS/cpYFnT5qeXtpmvuQnYBDA2Npbj4+Nd17djxw562b7frK/hyrk+qOvy1jU4fq11Op7zXV+338d221+78gg37j6+o2178WI9/trpenonIk6KiJOnl4ELgUeALcC60m0d8PWyvAV4R7mL53zgUNM0kCRpAHo50x8FvhYR06/zL5n5HxHxAHBnRFwF/AR4e+m/FVgDTALPAu/sYd+SpC50HfqZ+Tjw2hbtPwcuaNGewNXd7k+S1Dv/iIrUB8s33M21K4+0nS/2D4poWAx9VW22v/ZkMOvFyM/ekaSKeKYvSUMwrL8p7Jm+JFXEM31JzxvW2acGxzN9SaqIoS9JFTH0Jakihr4kVcTQl6SKePeOpGOev1ndOc/0JakinulLC5BnruoXz/QlqSKGviRVxNCXpIoY+pJUEUNfkipi6EtSRbxlU8c0PwpYOjqe6UtSRQx9SaqIoS9JFTH0Jakihr4kVcTQl6SKeMumJHXpWLxl2DN9SaqIoS9JFTH0JakizunreTPnJ69deYQrS9tCnJuUdPQM/T7o5eJOrxeGhvVn9o7FC1pSjZzekaSKDPxMPyJWA38PHAd8PjM39mtfu6cOPT89MZNnnpJqNNDQj4jjgM8CbwSeAB6IiC2Z+egg6+jEsKZJJKmfBj29cy4wmZmPZ+b/ABPA2gHXIEnViswc3M4i3gaszsx3l+dXAOdl5jVNfdYD68vTVwE/6GGXpwE/62H7frO+3lhfb6yvNwu5vt/LzJe3WrHg7t7JzE3Apvl4rYh4MDPH5uO1+sH6emN9vbG+3iz0+toZ9PTOFLCs6fnppU2SNACDDv0HgBURcUZEnAhcCmwZcA2SVK2BTu9k5pGIuAa4h8Ytm5szc08fdzkv00R9ZH29sb7eWF9vFnp9LQ30Qq4kabj8jVxJqoihL0kVOeZDPyJWR8QPImIyIja0WP+SiPhyWf+diFg+wNqWRcS9EfFoROyJiPe16DMeEYciYld5fHRQ9TXVsC8idpf9P9hifUTETWUMH46IswdY26uaxmZXRDwTEe+f0WegYxgRmyPiYEQ80tR2akRsi4i95eviNtuuK332RsS6Adb3dxHx/fL9+1pELGqz7azHQh/ruz4ippq+h2vabDvrv/c+1vflptr2RcSuNtv2ffx6lpnH7IPGxeAfAa8ETgS+B5w5o89fAP9Uli8FvjzA+pYAZ5flk4EftqhvHLhryOO4DzhtlvVrgG8AAZwPfGeI3+8nafziydDGEHgDcDbwSFPb3wIbyvIG4JMttjsVeLx8XVyWFw+ovguB48vyJ1vV18mx0Mf6rgf+uoPv/6z/3vtV34z1NwIfHdb49fo41s/0O/lYh7XAbWX534ALIiIGUVxm7s/Mh8ryL4DHgKWD2Pc8Wwvcng33A4siYskQ6rgA+FFm/mQI+35eZt4HPDWjufk4uw14c4tNLwK2ZeZTmfk0sA1YPYj6MvObmXmkPL2fxu/IDEWb8evEQD7GZbb6Sna8HfjSfO93UI710F8K/LTp+RO8MFSf71MO+kPAbw+kuiZlWukPge+0WP1HEfG9iPhGRLx6oIU1JPDNiNhZPgZjpk7GeRAupf0/tmGP4Whm7i/LTwKjLfoslHF8F43/ubUy17HQT9eU6afNbabHFsL4/QlwIDP3tlk/zPHryLEe+seEiBgBvgK8PzOfmbH6IRrTFa8F/gH490HXB7w+M88GLgaujog3DKGGWZVf5nsT8K8tVi+EMXxeNv6fvyDvhY6IDwNHgDvadBnWsXAL8PvAWcB+GlMoC9FlzH6Wv+D/LR3rod/Jxzo83ycijgdOAX4+kOoa+zyBRuDfkZlfnbk+M5/JzMNleStwQkScNqj6yn6nyteDwNdo/De62UL4+IyLgYcy88DMFQthDIED01Ne5evBFn2GOo4RcSXwZ8Dl5QfTC3RwLPRFZh7IzF9n5v8Bn2uz32GP3/HAW4Evt+szrPE7Gsd66HfysQ5bgOm7JN4G/Ge7A36+lfm/W4HHMvPTbfr87vQ1hog4l8b3ZJA/lE6KiJOnl2lc8HtkRrctwDvKXTznA4eapjIGpe0Z1rDHsGg+ztYBX2/R5x7gwohYXKYvLixtfReNP170N8CbMvPZNn06ORb6VV/zNaK3tNnvsD/G5U+B72fmE61WDnP8jsqwryT3+qBxZ8kPaVzV/3Bp+xiNgxvgpTSmBCaB7wKvHGBtr6fx3/yHgV3lsQZ4L/De0ucaYA+NOxHuB/54wOP3yrLv75U6psewucag8cdvfgTsBsYGXONJNEL8lKa2oY0hjR8++4H/pTGvfBWN60Tbgb3At4BTS98xGn8hbnrbd5VjcRJ45wDrm6QxHz59HE7f0fYKYOtsx8KA6vvncmw9TCPIl8ysrzx/wb/3QdRX2r8wfcw19R34+PX68GMYJKkix/r0jiTpKBj6klQRQ1+SKmLoS1JFDH1JqoihL0kVMfQlqSL/D0Xarz9lHARdAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oqCjibXA0viR"
      },
      "source": [
        "def regress(lista, data):\n",
        "  \"\"\" \n",
        "  Funcion que realiza la regresion OLS\n",
        "  La primera variable de la lista es la dependiente, el resto son las explicativas\n",
        "  data es la base de datos\n",
        "  \"\"\"\n",
        "  datos=data[lista]\n",
        "  # el metodo dropna() me permite eliminar las filas que tienen algun valor missing\n",
        "  datos=datos.dropna()\n",
        "  Y=datos[lista[0]]\n",
        "  X=datos[lista[1:]]\n",
        "  X=sm.add_constant(X)\n",
        "  modelo=sm.OLS(Y,X)\n",
        "  resultados=modelo.fit()\n",
        "  print(resultados.summary())\n",
        "  return resultados"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sg_K-vCB1F_-",
        "outputId": "8c63ecd7-da31-41d2-9bf0-a3c4d2581a4a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        }
      },
      "source": [
        "resultados=regress(['logingreso','aesc'],data=df)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:             logingreso   R-squared:                       0.199\n",
            "Model:                            OLS   Adj. R-squared:                  0.199\n",
            "Method:                 Least Squares   F-statistic:                     2573.\n",
            "Date:                Wed, 14 Oct 2020   Prob (F-statistic):               0.00\n",
            "Time:                        15:46:35   Log-Likelihood:                -11674.\n",
            "No. Observations:               10082   AIC:                         2.335e+04\n",
            "Df Residuals:                   10080   BIC:                         2.337e+04\n",
            "Df Model:                           1                                         \n",
            "Covariance Type:                  HC0                                         \n",
            "==============================================================================\n",
            "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const          8.0118      0.027    298.103      0.000       7.959       8.064\n",
            "aesc           0.1011      0.002     50.723      0.000       0.097       0.105\n",
            "==============================================================================\n",
            "Omnibus:                     1714.578   Durbin-Watson:                   1.798\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             4442.590\n",
            "Skew:                          -0.935   Prob(JB):                         0.00\n",
            "Kurtosis:                       5.660   Cond. No.                         45.7\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors are heteroscedasticity robust (HC0)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<statsmodels.regression.linear_model.RegressionResultsWrapper at 0x7fade4145d30>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4jyDpmY40I8-",
        "outputId": "c5a400f6-7ebe-4ff4-f84d-4c1fdce3f1bb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "resultados.params['aesc']"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.1010826419923894"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mahOOQFj0Thc",
        "outputId": "9e9aca2f-e0d0-4991-fe86-ccc657dd7ade",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "resultados.predict()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([9.22474501, 9.22474501, 9.22474501, ..., 8.41608388, 9.22474501,\n",
              "       9.12366237])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "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": "2d3b9244-a362-40fa-9621-620e667e1ef4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# 10 años de escolaridad\n",
        "8.0118+0.1011*10"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9.022799999999998"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G60RKQOvCrW5",
        "outputId": "2425377a-ba95-411b-bcc7-46b42192d237",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# 10 años de escolaridad levantando los parametros de los resultados del ajuste\n",
        "resultados.params['const']+resultados.params['aesc']*10"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9.022579730303121"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8PzA4iHtDLSw",
        "outputId": "55cc4d44-64bd-4351-cd6a-80cdc2c2e510",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        }
      },
      "source": [
        "# Predicciones para observaciones en la data\n",
        "resultados.params['const']+resultados.params['aesc']*df.aesc"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        9.224745\n",
              "1        9.224745\n",
              "2        9.224745\n",
              "3        9.224745\n",
              "4        9.224745\n",
              "           ...   \n",
              "14507    8.820414\n",
              "14508    9.527993\n",
              "14509    8.416084\n",
              "14510    9.224745\n",
              "14511    9.123662\n",
              "Name: aesc, Length: 10082, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NWU5mDsvDhBf",
        "outputId": "d69031d7-8e3d-4d77-b57b-cbbd8c26082d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "resultados.predict()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([9.22474501, 9.22474501, 9.22474501, ..., 8.41608388, 9.22474501,\n",
              "       9.12366237])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iMdWCqIh1Lm4",
        "outputId": "8076aa8e-64d4-4bce-d816-8c4d396fe760",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        }
      },
      "source": [
        "df.logingreso-resultados.predict()"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       -0.839941\n",
              "1       -0.119766\n",
              "2       -0.742143\n",
              "3        0.391061\n",
              "4        0.391061\n",
              "           ...   \n",
              "14507   -0.072109\n",
              "14508    0.424285\n",
              "14509    0.794257\n",
              "14510   -0.302087\n",
              "14511    0.066476\n",
              "Name: logingreso, Length: 10082, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cw17S2gYDrhp",
        "outputId": "45110c3b-b8d8-445d-801f-e340a5b044b8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "# Ploteamos prediccion con matplolib\n",
        "plt.scatter(df.aesc, resultados.predict())"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fade4459320>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MIFvCC4syPnZ",
        "outputId": "45eff591-3785-427a-d47f-d0f8082af0d1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "plt.scatter(df.aesc, df.logingreso, color='green')\n",
        "\n",
        "plt.plot(df.aesc, resultados.predict(), color= \"red\", marker='.', linestyle=':')\n",
        "\n",
        "plt.ylim(0,15)\n"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0, 15.0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QuoymAZp3_tU",
        "outputId": "bb35e055-aa45-4670-d291-4a78139ef79e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "resultados.predict()"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([9.22474501, 9.22474501, 9.22474501, ..., 8.41608388, 9.22474501,\n",
              "       9.12366237])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QZRn9Kqu4Kyf"
      },
      "source": [
        "# Error Cuadrático Medio\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kF_enYAB1f8Z",
        "outputId": "cafb11a4-e2bb-49ab-98af-37c33b73b6fe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "resultados.mse_resid"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5933965875175048"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "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": "37fafcda-cffe-4648-e325-d5b3dcf0623d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "ypred = resultados.predict()\n",
        "residuos=df.logingreso-ypred\n",
        "rss=np.sum(residuos**2)\n",
        "MSE=(rss/(resultados.nobs-2))\n",
        "MSE"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5933965875175049"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MLzHND1dJzv8",
        "outputId": "f35d0ef8-d26d-4fbf-daf5-c823d1a92960",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "MSE**0.5"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7703223919356784"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7327gR3F4X0e",
        "outputId": "6e2dc426-a199-4e08-ec34-db259c97fe5d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        }
      },
      "source": [
        "residuos2=residuos*residuos\n",
        "residuos2.describe()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    1.008200e+04\n",
              "mean     5.932789e-01\n",
              "std      1.280786e+00\n",
              "min      6.906205e-10\n",
              "25%      3.775540e-02\n",
              "50%      1.961224e-01\n",
              "75%      5.991571e-01\n",
              "max      3.779512e+01\n",
              "Name: logingreso, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BDcE_OXPE3gM",
        "outputId": "a34b90c3-4b8b-4787-83c9-78c5c17c50a7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        }
      },
      "source": [
        "residuos.describe()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    1.008200e+04\n",
              "mean    -2.794740e-15\n",
              "std      7.702842e-01\n",
              "min     -6.147774e+00\n",
              "25%     -3.715081e-01\n",
              "50%      8.090569e-02\n",
              "75%      4.910087e-01\n",
              "max      2.984093e+00\n",
              "Name: logingreso, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "48PRmVlhJxBC"
      },
      "source": [
        "# Propiedades de los estimadores cuando los errores son normales"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7FAwtdmpKN-U"
      },
      "source": [
        "¿Cuál es el beta que espero obtener si los errores de nuestro fueran errores normales?\n",
        "Simulamos las estimaciones de los coeficientes beta en presencia de errores normales  \n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JcwYaNioLTFZ",
        "outputId": "84c87e77-e28e-47ba-b516-a071452f5c30",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 371
        }
      },
      "source": [
        "e_simu= np.random.normal(0,0.6,10082)\n",
        "plt.hist(e_simu)\n"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([  23.,  118.,  592., 1698., 2841., 2641., 1555.,  489.,  107.,\n",
              "          18.]),\n",
              " array([-2.21046439, -1.76359567, -1.31672695, -0.86985823, -0.4229895 ,\n",
              "         0.02387922,  0.47074794,  0.91761666,  1.36448538,  1.8113541 ,\n",
              "         2.25822282]),\n",
              " <a list of 10 Patch objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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gL1pmHEly09BNXcjHMEhSRzzSl6SOGPqS1BFDX5I6YuhLUkcMfUnqiKEvSR0x9CWpI/8PGdiMn/mPxCIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xuqh7kr2FGNr",
        "outputId": "1e928745-e028-42cb-8744-588974ccef41",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "def simu():\n",
        "  e_simu = np.random.normal(0,0.6,10082)\n",
        "  X=df.aesc\n",
        "  y_simu=resultados.params['const']+resultados.params['aesc']*X+e_simu\n",
        "  model_simu=sm.OLS(y_simu,X)\n",
        "  resultados_simu=model_simu.fit()\n",
        "  #print(resultados_simu.summary())\n",
        "  #print(resultados_simu.params['bedrooms'])\n",
        "  return resultados_simu.params['aesc']\n",
        "simu()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6847614484114757"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xc0V-YE2G5xO",
        "outputId": "0cd0d1f1-8260-4fb1-bbe1-fcc18a25ca42",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "source": [
        "betacoefs=[]\n",
        "for i in range(0,2000):\n",
        "  beta=simu()\n",
        "  betacoefs.append(beta)\n",
        "print(betacoefs)\n"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.6845311014290651, 0.6855405528383517, 0.6845970250803701, 0.6844391392156366, 0.6842467897692812, 0.6851939008565336, 0.6853752531052342, 0.684900618112041, 0.6848315931309378, 0.6853539455401021, 0.6843676702684827, 0.6854842966280463, 0.6845485465801637, 0.6854369433626647, 0.6851062132342142, 0.6844969620177919, 0.6844028736805352, 0.6845028881146284, 0.685366812103382, 0.6844168358124, 0.6852452130422002, 0.6843943740569427, 0.6846537756094584, 0.684631999176926, 0.6848220902526749, 0.68458353315933, 0.685225041215229, 0.6848309778830793, 0.685778686450049, 0.685294656722488, 0.6846995485871579, 0.6846710725904506, 0.6847587479711597, 0.6840280766377818, 0.6852345265660149, 0.6850244244847072, 0.6847247542004546, 0.6847945880919954, 0.684531708843608, 0.6848281855779765, 0.6852998206850436, 0.6852039533280967, 0.684263177596024, 0.6848415048397445, 0.6845134623733775, 0.684516432915451, 0.6844058459046483, 0.6845029481226936, 0.684263834723366, 0.6846241576936247, 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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MN6lNag1I1uv",
        "outputId": "3dee12e0-d82f-465a-958c-bd73b4a15405",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        }
      },
      "source": [
        "pd.DataFrame(betacoefs).hist()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fade439d0f0>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LZcQ70HqJTIu",
        "outputId": "7ec8a4df-db10-4863-9277-f01f3c237f9c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.hist(betacoefs)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([  2.,  13.,  83., 326., 589., 569., 286., 111.,  19.,   2.]),\n",
              " array([0.68286477, 0.68321942, 0.68357406, 0.68392871, 0.68428336,\n",
              "        0.684638  , 0.68499265, 0.6853473 , 0.68570195, 0.68605659,\n",
              "        0.68641124]),\n",
              " <a list of 10 Patch objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l8MelTxsLrzq"
      },
      "source": [
        "# Cómo son los residuos (errores estimados) en mis datos?\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n4sZ7Av3LnnM",
        "outputId": "5c988d9f-efd8-43b7-977f-bfc6864bc4e5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "residuos.hist()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fade41c3390>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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dr25vAfZGxGtzld67ushYfITuqZ0SjjUWmXlLq/NRul/xvzjIvunkEhEvB74KfCgzfzbs/vTTqgz9zPyDhZZFxAeAr7WQvysi/pfuy5X+e1D9G6SFxiIiNgNnAQ9EBHRPZ9wbEedk5g8H2MWBOdZ+ARAR24G3Axes1oOABfh6lB4R8WK6gf/FzPzasPvTbxVP7/wD8DaAiHg9cCqj/Ta945KZD2Xmr2fmeGaO0/1K/+bVGviLiYgtdK9tvCMznx12fwbM16M00T0Cugk4kJmfGnZ/VkLF0N8NvDYivkv3gtW2Ykd1mt/fAK8A9kfE/RHxt8Pu0KC0C9hHX49yANg74NejnEzeCrwXOL/tB/dHxCXD7lQ/+RoGSSqk4pG+JJVl6EtSIYa+JBVi6EtSIYa+JBVi6EtSIYa+JBXyf7mULQdeZ8vXAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RpIU-4Iq4ciC"
      },
      "source": [
        "def regress(lista, data):\n",
        "  \"\"\" \n",
        "  Funcion que realiza la regresion OLS\n",
        "  La primera variable de la lista es la dependiente, el resto son las explicativas\n",
        "  data es la base de datos\n",
        "  \"\"\"\n",
        "  datos=data[lista]\n",
        "  # el metodo dropna() me permite eliminar las filas que tienen algun valor missing\n",
        "  datos=datos.dropna()\n",
        "  Y=datos[lista[0]]\n",
        "  X=datos[lista[1:]]\n",
        "  X=sm.add_constant(X)\n",
        "  modelo=sm.OLS(Y,X)\n",
        "  resultados=modelo.fit(cov_type='HC0')\n",
        "  print(resultados.summary())\n",
        "  return resultados"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6OmqjpVW4miA",
        "outputId": "290d327a-a95f-4c69-ba12-69b1e7d28022",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        }
      },
      "source": [
        "regress(['logingreso','aesc'],data=df)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:             logingreso   R-squared:                       0.199\n",
            "Model:                            OLS   Adj. R-squared:                  0.199\n",
            "Method:                 Least Squares   F-statistic:                     2573.\n",
            "Date:                Wed, 14 Oct 2020   Prob (F-statistic):               0.00\n",
            "Time:                        15:37:30   Log-Likelihood:                -11674.\n",
            "No. Observations:               10082   AIC:                         2.335e+04\n",
            "Df Residuals:                   10080   BIC:                         2.337e+04\n",
            "Df Model:                           1                                         \n",
            "Covariance Type:                  HC0                                         \n",
            "==============================================================================\n",
            "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const          8.0118      0.027    298.103      0.000       7.959       8.064\n",
            "aesc           0.1011      0.002     50.723      0.000       0.097       0.105\n",
            "==============================================================================\n",
            "Omnibus:                     1714.578   Durbin-Watson:                   1.798\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             4442.590\n",
            "Skew:                          -0.935   Prob(JB):                         0.00\n",
            "Kurtosis:                       5.660   Cond. No.                         45.7\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors are heteroscedasticity robust (HC0)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<statsmodels.regression.linear_model.RegressionResultsWrapper at 0x7fade4194710>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    }
  ]
}