Aprendizaje_supervisado/Cuadernos/su_0060_Regresion_Binomial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<figure>\n",
    "<img src=\"../Imagenes/logo-final-ap.png\"  width=\"80\" height=\"80\" align=\"left\"/> \n",
    "</figure>\n",
    "\n",
    "# <span style=\"color:blue\"><left>Aprendizaje Profundo</left></span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# <span style=\"color:red\"><center>Diplomado en Ciencia de Datos</center></span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <span style=\"color:green\"><center>Modelos de Regresión Logístico y Binomial</center></span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<figure>\n",
    "<center>\n",
    "<img src=\"../Imagenes/linea-produccion.jpg\" width=\"500\" height=\"500\" align=\"center\" /> \n",
    "</center>   \n",
    "</figure>\n",
    "\n",
    "<a href=\"https://commons.wikimedia.org/wiki/File:The_Arms_Production_in_Britain_in_the_First_World_War_Q27861.jpg\">Lewis, George P. (Photographer)</a>, Public domain, via Wikimedia Commons"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">Profesores</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Alvaro  Montenegro, PhD, <ammontenegrod@unal.edu.co>\n",
    "* Campo Elías Pardo, PhD, <cepardot@unal.edu.co>\n",
    "* Daniel  Montenegro, Msc, <dextronomo@gmail.com>\n",
    "* Camilo José Torres Jiménez, Msc, <cjtorresj@unal.edu.co>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">Asesora Medios y Marketing digital</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Maria del Pilar Montenegro, pmontenegro88@gmail.com\n",
    "* Jessica López Mejía, jelopezme@unal.edu.co"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">Jefe Jurídica</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Paula Andrea Guzmán, guzmancruz.paula@gmail.com"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">Coordinador Jurídico</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* David Fuentes, fuentesd065@gmail.com"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">Desarrolladores Principales</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Dairo Moreno, damoralesj@unal.edu.co\n",
    "* Joan Castro, jocastroc@unal.edu.co\n",
    "* Bryan Riveros, briveros@unal.edu.co\n",
    "* Rosmer Vargas, rovargasc@unal.edu.co\n",
    "* Venus Puertas, vpuertasg@unal.edu.co"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">Expertos en Bases de Datos</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Giovvani Barrera, udgiovanni@gmail.com\n",
    "* Camilo Chitivo, cchitivo@unal.edu.co"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">Referencias</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. [Dobson y Baernett, An introduction to Generalizer Linear Model](http://library.lol/main/472B57FA461867F6CFB4334BFED60010)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">Introducción</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En esta lección introducimos el modelo logístico y el modelo binomial.\n",
    "\n",
    "La librería `statsmodels` es un paquete de Python orientado a la exploración de datos con métodos estadísticos. Proporciona una amplia gama de herramientas estadísticas, se integra con Pandas y NumPy, y puede utilizar las cadenas de fórmulas de estilo R para definir modelos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">Modelo Binomial</span> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <span style=\"color:#4CC9F0\">Distribución de probabilidad Binomial</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La distribución Binomial surge en problemas con ensayos binarios que pueden resultar en éxito (1) o fallo (0). Si se realizan $n$ ensayos (de Bernoulli) de forma independiente y se define la variable aleatoria $y$ como el  número de éxitos, entonces se dice que $Y$ tiene distribución Binomial con parámetro $\\theta$, cuya densidad es dada por:\n",
    "\n",
    "$$\n",
    "p(y|\\theta) = \\binom{n}{y}\\theta^y(1-\\theta)^{n-y}, \\hspace{1mm} y=1,\\ldots,n,\n",
    "$$\n",
    "\n",
    "en donde \n",
    "\n",
    "$$\n",
    "\\binom{n}{y} =\\frac{n!}{y!(n-y)!}\n",
    "$$\n",
    "es el número de grupos diferentes de tamaño $y$, que se pueden organizar si se tienen en total $n$ elementos. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import binom\n",
    "\n",
    "mu = 0.25\n",
    "n = 4\n",
    "binomial = binom(n, mu)\n",
    "x = np.arange(5)\n",
    "prob_p = binomial.pmf(x)\n",
    "data = pd.DataFrame(zip(x, prob_p))\n",
    "data.columns=['x','p']\n",
    "ax = sns.barplot(data=data, x='x', y='p')\n",
    "ax.set(xlabel='x', ylabel='p(x|$\\theta$)', title='Función de probabilidad de la distribución Binomial({},{})'.format(n,mu))\n",
    "ax.set_xticklabels(x, rotation=0, size=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distribución Binomial(4, 0.25); media = 1.0, varianza 0.75\n",
      "Distribución Binomial(4, 0.25); probabilidades=[0.25 0.5  0.75]; cuantiles=[0. 1. 2.]\n"
     ]
    }
   ],
   "source": [
    "# media y varianza de la distribución Binomial(4, 0.25)\n",
    "import numpy as np\n",
    "from scipy.stats import binom\n",
    "mu = 0.25\n",
    "n = 4\n",
    "binomial = binom(n, mu)\n",
    "media, var = binomial.stats(moments=\"mv\")\n",
    "print('Distribución Binomial(4, 0.25); media = {}, varianza = {}'.format(media, var))\n",
    "# calcula algunos cuantiles de la distribución\n",
    "prob = np.array([0.25, 0.5, 0.75])\n",
    "cuantiles = binomial.ppf(prob)\n",
    "print('Distribución Binomial(4, 0.25); probabilidades={}; cuantiles={}'.format(prob, cuantiles))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">Regresión Binomial </span> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Supongamos que se hacen $n$ observaciones independientes de $ Y_i \\sim\\text{Binom}(n_i,\\pi_i)$. El modelo estadístico usando la función de enlace natural es determinado por\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "\\text{Probabilidad conjunta: } \\hspace{1mm} p(\\mathbf{y}|\\boldsymbol{\\pi}) &= \\prod_{i=1}^n Binom(y_i|\\pi_i) = \\prod_{i=1}^n \\binom{n_i}{y_i}\\pi_i^{y_i}(1-\\pi_i)^{n_i-y_i},\\\\\n",
    "\\text{Modelo de regresión: } \\hspace{1mm}  \\log \\frac{\\pi_i}{1-\\pi_i} &= \\mathbf{x}_i^t \\boldsymbol{\\beta} = \\beta_1x_{1i} + \\ldots +\\beta_px_{pi}\n",
    "\\end{align}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <span style=\"color:#4CC9F0\">Log verosimilitud</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La función de log-verosimilitud es dada por\n",
    "\n",
    "\n",
    "$$\n",
    "\\mathcal{L}(\\mu) = \\sum_{i=1}^n  x_i\\log \\pi_i +  (n_i-x_i)\\log(1-\\pi_i).\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">Regresión Logística </span> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La regresión logística corresponde al caso en el cual tenemos como variable de respuesta 0 o 1. Es decir, la distribución de la variables de respuesta es Bernoulli($\\pi$).  El modelo estadístico usando la función de enlace natural es determinado por\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "\\text{Probabilidad conjunta: } \\hspace{1mm} p(\\mathbf{y}|\\boldsymbol{\\pi}) &= \\prod_{i=1}^n Bernoulli(y_i|\\pi_i) = \\prod_{i=1}^n \\pi_i^{y_i}(1-\\pi_i)^{n_i-y_i},\\\\\n",
    "\\text{Modelo de regresión: } \\hspace{1mm}  \\log \\frac{\\pi_i}{1-\\pi_i} &= \\mathbf{x}_i^t \\boldsymbol{\\beta} = \\beta_1x_{1i} + \\ldots +\\beta_px_{pi}\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "Además, directamente se puede despejar $\\pi_i $ para obtener las predicciones. Si escribimos $z_i = \\mathbf{x}_i^t \\boldsymbol{\\beta}$\n",
    "\n",
    "$$\n",
    "\\hat{y}_i = \\pi_i = \\frac{z_i}{1+z_i}.\n",
    "$$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <span style=\"color:#4CC9F0\">Log verosimilitud</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La función de log-verosimilitud es dada por\n",
    "\n",
    "\n",
    "$$\n",
    "\\mathcal{L}(\\mu) = \\sum_{i=1}^n  x_i\\log \\pi_i +  (1-x_i)\\log(1-\\pi_i) \n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <span style=\"color:#4CC9F0\">Deviance del modelo Binomial</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El modelo saturado o con el número máximo de parámetros es aquel en el cual $\\hat{y_i}= y_i$. Entonces un cálculo directo muestra que la deviance en este caso es\n",
    "\n",
    "$$\n",
    "\\text{Deviance} = 2\\left[\\sum_i y_i \\log\\left(\\frac{y_i}{\\hat{y}_i}\\right) +  (n_i-y_i)\\log \\left(\\frac{n_i-y_i}{n_i-\\hat{y}_i} \\right)  \\right]\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">Ejemplos de Regresión Logística y Binomial</span> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <span style=\"color:#4CC9F0\">Sobre la API de Statmodels</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* `statsmodels.api`: La API estándar. Los datos se separan en variables explicativas (exog) y una variable de respuesta (endog). La especificación de un modelo se realiza a través de clases.\n",
    "* `statsmodels.formula.api`: La fórmula API. Utiliza la sintaxis de la fórmula de estilo R y los marcos de datos. La API de fórmula es una forma más conveniente de crear modelos que abstraen el texto estándar requerido por la API estándar. Debajo del capó, las API estándar y de fórmula utilizan los mismos modelos subyacentes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <span style=\"color:#4CC9F0\">Ejemplo de regresión logística : Titanic</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Los datos corresponden al histórico naufragio. Los datos puede descargarlos de [seaborn-data](https://raw.githubusercontent.com/mwaskom/seaborn-data/master/titanic.csv). Este famoso conjunto de datos ha sido utilizado en competencias de maquinas de aprendizaje. Inicialmente son 891 datos y 15 variables. La variable de respuesta (endógena) es binaria. *survived = 1*, si sobrevivió y *survived  = 0*, si no sobrevivió. Tratamos de predecir *survived* a partir de variables explicativas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Survived (Sobrevivió):** Esta columna indica si un pasajero sobrevivió o no al naufragio del Titanic. Suele tener dos valores posibles: 0 para no sobrevivir y 1 para sobrevivir.\n",
    "- **Pclass (Clase del boleto):** Esta columna representa la clase del boleto que tenía el pasajero. Puede tomar uno de los tres valores posibles: 1 para primera clase, 2 para segunda clase y 3 para tercera clase. La clase se utiliza a menudo como una indicación del estatus socioeconómico del pasajero.\n",
    "- **Sex (Género):** El género del pasajero, que puede ser \"male\" (masculino) o \"female\" (femenino).\n",
    "- **Age (Edad):** La edad del pasajero en años. Esta columna puede contener valores numéricos.\n",
    "- **SibSp (Hermanos/cónyuges a bordo):** El número de hermanos o cónyuges que el pasajero tenía a bordo del Titanic.\n",
    "- **Parch (Padres/hijos a bordo):** El número de padres o hijos que el pasajero tenía a bordo del Titanic.\n",
    "- **Fare (Tarifa):** El costo del boleto del pasajero.\n",
    "- **Embarked (Puerto de embarque):** El puerto de embarque del pasajero. Puede tener tres valores posibles: \"C\" para Cherbourg, \"Q\" para Queenstown y \"S\" para Southampton.\n",
    "- **Class (Clase):** La clase a la que pertenece el pasajero, similar a \"Pclass\" pero con una representación diferente.\n",
    "- **Who (Quién):** Una categorización que describe si el pasajero es un niño, hombre o mujer.\n",
    "- **Adult_male (Adulto masculino):** Indica si el pasajero es un adulto masculino (Verdadero/Falso).\n",
    "- **Deck (Cubierta):** La cubierta donde se encontraba la cabina del pasajero.\n",
    "- **Embark_town (Ciudad de embarque):** El nombre de la ciudad donde el pasajero embarcó.\n",
    "- **Alive (Vivo):** Indica si el pasajero sobrevivió o no en un formato más legible, como \"yes\" (sí) o \"no\" (no).\n",
    "- **Alone (Solo):** Indica si el pasajero viajaba solo (Verdadero/Falso)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seleccionamos algunas variables y eliminamos los registros con datos faltantes. Al final nos quedamos con 715 registros y 5 variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# librerías\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.formula.api as smf # smf perimte escribir la fórmula como se hace en la librería gl de R."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "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>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>embark_town</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Southampton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>Cherbourg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>Southampton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>Southampton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>Southampton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>Queenstown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>Southampton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>Southampton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>Cherbourg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>Queenstown</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>712 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  pclass     sex   age  embark_town\n",
       "0           0       3    male  22.0  Southampton\n",
       "1           1       1  female  38.0    Cherbourg\n",
       "2           1       3  female  26.0  Southampton\n",
       "3           1       1  female  35.0  Southampton\n",
       "4           0       3    male  35.0  Southampton\n",
       "..        ...     ...     ...   ...          ...\n",
       "885         0       3  female  39.0   Queenstown\n",
       "886         0       2    male  27.0  Southampton\n",
       "887         1       1  female  19.0  Southampton\n",
       "889         1       1    male  26.0    Cherbourg\n",
       "890         0       3    male  32.0   Queenstown\n",
       "\n",
       "[712 rows x 5 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# datos\n",
    "titanic = pd.read_csv('../Datos/titanic.csv')\n",
    "titanic # son 891 datos y 15 columnas\n",
    "# selecciona variables de interés\n",
    "titanic = titanic[[\"survived\", \"pclass\", \"sex\", \"age\", \"embark_town\"]]\n",
    "# omite registros con faltantes\n",
    "titanic = titanic.dropna()\n",
    "titanic # quedan 712 registros y 5 variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.509889\n",
      "         Iterations 6\n"
     ]
    }
   ],
   "source": [
    "# modelación con statmodels. Ajuste del modelo\n",
    "import statsmodels.formula.api as smf\n",
    "#fit logistic regression model\n",
    "# \"survived ~ sex + age + embark_town\", \n",
    "log_reg = smf.logit(\"survived ~ sex + age + embark_town\", data=titanic).fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <span style=\"color:#4CC9F0\">Revisión de los resultados</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Usaremos el método summary para revisar los resultados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                           Logit Regression Results                           \n",
      "==============================================================================\n",
      "Dep. Variable:               survived   No. Observations:                  712\n",
      "Model:                          Logit   Df Residuals:                      707\n",
      "Method:                           MLE   Df Model:                            4\n",
      "Date:                Sat, 02 Sep 2023   Pseudo R-squ.:                  0.2444\n",
      "Time:                        09:15:12   Log-Likelihood:                -363.04\n",
      "converged:                       True   LL-Null:                       -480.45\n",
      "Covariance Type:            nonrobust   LLR p-value:                 1.209e-49\n",
      "==============================================================================================\n",
      "                                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------------------\n",
      "Intercept                      2.2046      0.322      6.851      0.000       1.574       2.835\n",
      "sex[T.male]                   -2.4760      0.191    -12.976      0.000      -2.850      -2.102\n",
      "embark_town[T.Queenstown]     -1.8156      0.535     -3.393      0.001      -2.864      -0.767\n",
      "embark_town[T.Southampton]    -1.0069      0.237     -4.251      0.000      -1.471      -0.543\n",
      "age                           -0.0081      0.007     -1.233      0.217      -0.021       0.005\n",
      "==============================================================================================\n"
     ]
    }
   ],
   "source": [
    "# impirme el summary de los resultados\n",
    "print(log_reg.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <span style=\"color:#4CC9F0\">Acceso a los parámetros y demás objetos  de la salida del modelo</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En statsmodels, el método fit() devuelve un objeto *Result*. Los coeficientes del modelo, los errores estándar, los valores p, etc., están todos disponibles en este objeto Result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<statsmodels.discrete.discrete_model.BinaryResultsWrapper object at 0x7feec25eaaa0>\n"
     ]
    }
   ],
   "source": [
    "print(log_reg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convenientemente, los resultados se  almacenan como dataframes de Pandas con el nombre del parámetro como índice del marco de datos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept                     2.204585\n",
      "sex[T.male]                  -2.475962\n",
      "embark_town[T.Queenstown]    -1.815592\n",
      "embark_town[T.Southampton]   -1.006949\n",
      "age                          -0.008079\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Inspecciona los parámetros\n",
    "print(log_reg.params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estos son algunos de los valores relevantes para una regresión logística.\n",
    "\n",
    "|Atributo/función |Descripción|\n",
    "|---|---|\n",
    "|params |Parámetros estimados del modelo. Aparece como coef cuando se llama a summary() en un modelo ajustado|\n",
    "|bse| Error estándar|\n",
    "|tvalues|columna z al llamar a summary() en un modelo ajustado|\n",
    "|pvalues|Valores p del modelo|\n",
    "|conf_int(alfa)| Método que calcula el intervalo de confianza para los parámetros estimados. Para llamar: model.conf_int(0.05)|"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por ejemplo el intervalos de confianza se recupera con"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Intercept</th>\n",
       "      <td>1.573877</td>\n",
       "      <td>2.835293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex[T.male]</th>\n",
       "      <td>-2.849937</td>\n",
       "      <td>-2.101986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>embark_town[T.Queenstown]</th>\n",
       "      <td>-2.864233</td>\n",
       "      <td>-0.766950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>embark_town[T.Southampton]</th>\n",
       "      <td>-1.471180</td>\n",
       "      <td>-0.542717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>-0.020917</td>\n",
       "      <td>0.004760</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   0         1\n",
       "Intercept                   1.573877  2.835293\n",
       "sex[T.male]                -2.849937 -2.101986\n",
       "embark_town[T.Queenstown]  -2.864233 -0.766950\n",
       "embark_town[T.Southampton] -1.471180 -0.542717\n",
       "age                        -0.020917  0.004760"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg.conf_int(0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para ver la lista completa de atributos y métodos disponibles, use la función dir() integrada de Python en el modelo ajustado."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getstate__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', '_cache', '_data_attr', '_data_in_cache', '_get_endog_name', '_get_robustcov_results', '_get_wald_nonlinear', '_transform_predict_exog', '_use_t', 'aic', 'bic', 'bse', 'conf_int', 'converged', 'cov_kwds', 'cov_params', 'cov_type', 'df_model', 'df_resid', 'f_test', 'fittedvalues', 'get_distribution', 'get_influence', 'get_margeff', 'get_prediction', 'im_ratio', 'info_criteria', 'initialize', 'k_constant', 'llf', 'llnull', 'llr', 'llr_pvalue', 'load', 'method', 'mle_retvals', 'mle_settings', 'model', 'nobs', 'normalized_cov_params', 'params', 'pred_table', 'predict', 'prsquared', 'pvalues', 'remove_data', 'resid_dev', 'resid_generalized', 'resid_pearson', 'resid_response', 'save', 'scale', 'score_test', 'set_null_options', 'summary', 'summary2', 't_test', 't_test_pairwise', 'tvalues', 'use_t', 'wald_test', 'wald_test_terms']\n"
     ]
    }
   ],
   "source": [
    "print(dir(log_reg))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <span style=\"color:#4CC9F0\">Evaluación el rendimiento del modelo</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para evaluar la calidad del modelo de regresión logística, podemos observar dos métricas en el resultado:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <span style=\"color:#4CC9F0\">Pseudo R-cuadrado</span> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Este valor se puede considerar como el sustituto del valor de *R* cuadrado ($R^2$) para un modelo de regresión lineal.\n",
    "\n",
    "* Se calcula como el cociente entre la función logaritmo de verosimilitud evaluada en el modelo nulo y el modelo completo.\n",
    "\n",
    " * Este valor puede oscilar entre 0 y 1; los valores más altos indican un mejor ajuste del modelo.\n",
    "\n",
    "* En este ejemplo, el valor pseudo R-cuadrado es  0.2444, que es bastante bajo. Esto nos dice que las variables predictoras en el modelo no son suficientes para estimar el valor de la variable de respuesta."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <span style=\"color:#4CC9F0\">LLR p-valor</span> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LLR = log likelihood residual, o log verosimilitud marginal.\n",
    "\n",
    "Este valor se puede considerar como el sustituto del valor p para el valor F general de un modelo de regresión lineal.\n",
    "\n",
    "* Si este valor está por debajo de cierto umbral (por ejemplo, α = .05), entonces podemos concluir que el modelo en general es \"útil\" y es mejor para predecir los valores de la variable de respuesta en comparación con un modelo sin variables predictoras.\n",
    "\n",
    "* En este ejemplo, el valor p de LLR es 1.209e-49. Dependiendo del nivel de significación que elijamos (p. ej., .01, .05, .1), podemos o no concluir que el modelo en su conjunto es útil. En este caso, es claro que las variables predictores si están aportando a la predicción de la variable observada o en endógena.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <span style=\"color:#4CC9F0\">Conclusión provisional</span> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "De acuerdo con las salidas explicadas para este problema podemos concluir que \n",
    "\n",
    "* Las variables *sex* y *embark_town* y el intercepto son necesarias en el modelo. La variable edad no alcanza a tener un aporte significativo significativo, por lo que en la explicación de del problema no aporta demasiado.\n",
    "* El valor *LLR p-valor* indica que en realidad las variables explicativas si aportan a la predicción de la variable observada y son necesarias además del intercepto.\n",
    "* Finalmente el valor de la estadística *Pseudo R-cuadrado* indica que las variables explicativas no son suficientes para tener una explicación satisfactoria de la variable respuesta. \n",
    "\n",
    "En conclusión, las variables *sex* y *embark_town* hacen un buen aporte a la explicación de la variable  *survived*, pero no son suficiente para tener una adecuada explicación del problema.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <span style=\"color:#4CC9F0\">Problema de la dosis</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "from scipy import stats\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Problema de la dosis. Tomado de \n",
    "[Dobson y Baernett, An introduction oto Generalizer Linear Model](http://library.lol/main/472B57FA461867F6CFB4334BFED60010). Los datos corresponde al número de escarabajos que mueren después de cinco hora de estar expuestos a un veneno en diferentes concentraciones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "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>Dosis</th>\n",
       "      <th>Expuestos</th>\n",
       "      <th>Muertos</th>\n",
       "      <th>Proporción</th>\n",
       "      <th>Vivos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.6907</td>\n",
       "      <td>59</td>\n",
       "      <td>6</td>\n",
       "      <td>0.101695</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.7242</td>\n",
       "      <td>60</td>\n",
       "      <td>13</td>\n",
       "      <td>0.216667</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.7552</td>\n",
       "      <td>62</td>\n",
       "      <td>18</td>\n",
       "      <td>0.290323</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.7842</td>\n",
       "      <td>56</td>\n",
       "      <td>28</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.8113</td>\n",
       "      <td>63</td>\n",
       "      <td>52</td>\n",
       "      <td>0.825397</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.8369</td>\n",
       "      <td>59</td>\n",
       "      <td>53</td>\n",
       "      <td>0.898305</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.8610</td>\n",
       "      <td>62</td>\n",
       "      <td>61</td>\n",
       "      <td>0.983871</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.8839</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Dosis  Expuestos  Muertos  Proporción  Vivos\n",
       "0  1.6907         59        6    0.101695     53\n",
       "1  1.7242         60       13    0.216667     47\n",
       "2  1.7552         62       18    0.290323     44\n",
       "3  1.7842         56       28    0.500000     28\n",
       "4  1.8113         63       52    0.825397     11\n",
       "5  1.8369         59       53    0.898305      6\n",
       "6  1.8610         62       61    0.983871      1\n",
       "7  1.8839         60       60    1.000000      0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "escarabajos = {'Dosis':[1.6907, 1.7242, 1.7552, 1.7842, 1.8113, 1.8369, 1.8610, 1.8839],\n",
    "             'Expuestos': [59, 60, 62, 56, 63, 59, 62, 60],\n",
    "             'Muertos':[6, 13, 18, 28, 52, 53, 61, 60],\n",
    "             'Proporción': [6.0/59, 13./60, 18./62, 28./56, 52./63, 53./59 ,61./62, 60./60]}\n",
    "escarabajos = pd.DataFrame(escarabajos)\n",
    "escarabajos['Vivos'] = escarabajos.Expuestos - escarabajos.Muertos \n",
    "escarabajos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <span style=\"color:#4CC9F0\">Gráfica dosis v.s. proporción</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.scatterplot(data=escarabajos, x='Dosis', y='Proporción')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observe la forma de S de los datos. Además se tiene que esta es un relación entre números reales y números entre 0 y 1. Funciones candidatas para modelar este tipo de datos son las funciones de distribución."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <span style=\"color:#4CC9F0\">Modelación con GLM de Statsmodels</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "endog = escarabajos[['Muertos', 'Vivos']]\n",
    "exog =  escarabajos['Dosis']\n",
    "exog =  sm.add_constant(exog, prepend=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "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>Dosis</th>\n",
       "      <th>const</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.6907</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.7242</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.7552</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.7842</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.8113</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.8369</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.8610</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.8839</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Dosis  const\n",
       "0  1.6907    1.0\n",
       "1  1.7242    1.0\n",
       "2  1.7552    1.0\n",
       "3  1.7842    1.0\n",
       "4  1.8113    1.0\n",
       "5  1.8369    1.0\n",
       "6  1.8610    1.0\n",
       "7  1.8839    1.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "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>Muertos</th>\n",
       "      <th>Vivos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>52</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>53</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Muertos  Vivos\n",
       "0        6     53\n",
       "1       13     47\n",
       "2       18     44\n",
       "3       28     28\n",
       "4       52     11\n",
       "5       53      6\n",
       "6       61      1\n",
       "7       60      0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "endog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  Generalized Linear Model Regression Results                   \n",
      "================================================================================\n",
      "Dep. Variable:     ['Muertos', 'Vivos']   No. Observations:                    8\n",
      "Model:                              GLM   Df Residuals:                        6\n",
      "Model Family:                  Binomial   Df Model:                            1\n",
      "Link Function:                    Logit   Scale:                          1.0000\n",
      "Method:                            IRLS   Log-Likelihood:                -18.715\n",
      "Date:                  Sat, 02 Sep 2023   Deviance:                       11.232\n",
      "Time:                          09:15:12   Pearson chi2:                     10.0\n",
      "No. Iterations:                       6   Pseudo R-squ. (CS):              1.000\n",
      "Covariance Type:              nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Dosis         34.2703      2.912     11.768      0.000      28.563      39.978\n",
      "const        -60.7175      5.181    -11.720      0.000     -70.871     -50.563\n",
      "==============================================================================\n"
     ]
    }
   ],
   "source": [
    "glm_binom = sm.GLM(endog, exog, family=sm.families.Binomial())\n",
    "res = glm_binom.fit()\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <span style=\"color:#4CC9F0\">Curva ajustada</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "nobs = res.nobs\n",
    "y = endog.iloc[:,0]/endog.sum(1)\n",
    "yhat = res.mu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "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>y</th>\n",
       "      <th>yhat</th>\n",
       "      <th>indice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.101695</td>\n",
       "      <td>0.058601</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.216667</td>\n",
       "      <td>0.164028</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.290323</td>\n",
       "      <td>0.362119</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.605315</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.825397</td>\n",
       "      <td>0.795172</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.898305</td>\n",
       "      <td>0.903236</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.983871</td>\n",
       "      <td>0.955196</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.979049</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          y      yhat  indice\n",
       "0  0.101695  0.058601       0\n",
       "1  0.216667  0.164028       1\n",
       "2  0.290323  0.362119       2\n",
       "3  0.500000  0.605315       3\n",
       "4  0.825397  0.795172       4\n",
       "5  0.898305  0.903236       5\n",
       "6  0.983871  0.955196       6\n",
       "7  1.000000  0.979049       7"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fit_data = pd.DataFrame({'y':y, 'yhat':yhat})\n",
    "fit_data = fit_data.sort_values(by= 'yhat')\n",
    "fit_data['indice'] = np.array(np.arange(fit_data.shape[0]))\n",
    "fit_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEICAYAAABPgw/pAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAAvT0lEQVR4nO3deXwU9f3H8deHEEg4BARULgEVOQTKkeKBCniBxw/wFsUDq1Zbz1o8+qtWrVYt/aH1wmK1VFtAqkDxpCqo4MktAqIIAZJQBCRBIFzh+/vjO4mbsEkW2DC7m/fz8ciD3ZnJzGcnu2+++535zphzDhERSX41wi5ARETiQ4EuIpIiFOgiIilCgS4ikiIU6CIiKUKBLiKSIhToVcjM3jSzG8pMO9fMVpvZZjPrbmaLzKxvFdcxxswerMptRNnmVWY280Buc2+YWRszc2ZWM+xa9oaZZZvZaWHXUVWCv8lRweNnzeyeGH7nfTO7puqrS3xJ9WY+0MzsEuA2oDOwBVgB/B0Y5So5gd/MrgC+d86NKjPrT8CNzrl/B8+PiW/VIqnBOXd92DUkG7XQy2FmtwN/BkYAhwGHAtcDvYFa5fxOWsTTesDPoyzWGlgU12JTiHkp8b5MpdeyL5Lt208qqLZvtoqYWQPgAeAXzrlXnHM/OG+ec+4y59z2YLkxZjYq6FrZAvQzs7PNbB7wCPCVmd0XLFvbzDYDacACM/s2mF7yFdrM0szsN2b2rZn9YGZzzKxVMO8EM5tlZgXBvydUUH93M5sbrONlIKPM/HPMbL6Z5ZvZx2bWtZz1jDKzP5WZ9m8z+1Xw+K6IWheb2bkV1FRu/cFX5ofM7CNgK3CEmXUws3fM7HszW2pmF0Usf1awvR/MLNfMfl3ONmuY2W/NbKWZfWdmLwZ/20hXm1mema2JXI+Z9TKz2Wa2yczWmtnIiHnHBfst38wWRHaZRXktw81sdpm6bjOzKcHjs81sXrCd1cXvl4hlLw/q32Bm/1tmXm0zezyoPy94XDuY18TMXg9q/N7MZkT7z8XMnjaz/yszbYqZ3VbOPnVmdrOZLTez9WY2oni95rvZPjKzx8xsA3BfUOOfzGxVsB+fNbPMiPUND/Z9npldXWZbpboKzWxQ8L7dFLzvBkQs3jrY9g9m9h8zaxLxewPNd23mB3+fjhHz7gzeQz8E77NTo73upOGc00+ZH2AAsAuoWclyY4ACfKu9Bj44TwG6BM+7At8BgyN+xwFHRTzPBk4LHg8HFgLtAQN+AjQGDgY2Apfju8mGBM8bR6mpFrAS31WUDlwA7AQeDOZ3D2o6Fv+fy5VBDbWjrOtkYDVgwfNGQCHQPHh+IdA8eK0X47ulmgXzrgJmBo8rrB94H1iF736qCTQItjsseN4dWA90CpZfA5wUUVOPcv4+VwPLgCPw35gmAi8F89oEf4txQN3gb7Yu4m/xCXB58LgecFzwuAWwATgreN2nB8+bVvBafgDaRdQ1C7gkeNyX0u+XtQTvF6ATsDn4O9QGRuLfl8U1PgB8ChwCNAU+Bn4fzHsYeDZ4D6QDJxX/Hcvso15AHlAjeN4E/x/RoeXsUwdMD/6mhwNfA9dE/M13ATcFrz0TeAyYEixfH3gNeDjic7YW36VZFxhLxOcD//l6MKLOgmB/1wj+Dh0i9vm3wNHBNt8HHgnmHY1/X54e7Ic78O+JWvjP2Wp+fD+3AY4MO3/2K7vCLiARf4ChwH/LTPsYyMcH2skRb7gXK1nX48BjEc8rCvSlwKAo67gc+LzMtE+Aq6Ise3LwAbUytRd/MEYVf+gj5i8F+kRZl+HDqfj1XgtMq+C1zi+un9KBXmH9wQfwgYh5FwMzyiz/F+B3weNV+O6sgyrZ9+/hv2UVP2+P/8+tJj8GeoeI+X8Eng8efwjcDzQps847Cf5TiJg2Fbgy2msJpv0DuDd43A4f8HUqe78A9wLjI+bVBXZEvF++Bc6KmN8fyA4ePwD8O/K9VsF+WgKcHjy+EXizgmUdMCDi+S+A9yL+5qvKvH+2EBGSwPHAiuDxCwTBGzw/mvID/S9EfI7K1PQ+8NsyNb0dPL4HmBAxrwaQi/+P9Ch84+Y0IL2y/ZQMP+pyiW4D0MQi+gCdcyc45xoG8yL32+rIXzSzHmb2lvmulJX4N3kTYtMK/yEtqzm+1R1pJb6VEm3ZXBe8eyOWLdYauD34+plvZvnBdpuXXVGwjvH4FjXApcA/i+eb2RX2Y9dNPr6lFe21xlJ/5H5sDRxbpsbL8McyAM7Ht5BXmtkHZnZ8lG1G2+5KfJgfWs52V/LjfvgZPmC+Mt9FdE5EbReWqe1EoFk56wTf8ozch5Odc1sBzOxYM5tuZuvMrAB/nKZ4HzaPXJdzbgv+/VfR6yuufwS+JfqfoHvkLsr3d3wjhuDflypYtuzri9xm2XlNgTrAnIh99XYwvbj+susqT3mfjWL/jXi8Ff+tqngbJet1zu0OttnCObcMuBW4D/jOzMab2R6fg2SiQI/uE2A7MCiGZcue7fIy8Dq+ldEa/2GxGLe7GjgyyvQ8fJBEOhzf0ihrDdDCzKzMspHbeMg51zDip45zblw5NY0DLjCz1vhumlcBgufP4Vt0jYP/7L4k+muNpf7I/bga+KBMjfWcczcAOOdmOecG4bsaJgMTyqm97HYPx3cJrI2Y1qrM/LxgG98454YE23gUeMXM6ga1vVSmtrrOuUfKeS0A7wBNzawbPtjHRswbi++SaOWca4DvJineh2si6zOzOvguuIpeX3H9PzjnbnfOHQEMBH5VQf/wP4BBZvYToCN+n1Yk6j4LRL729fhvtMdE7KsGzrnisC31+ij9Pi2rvM9GZUrto+Bz0YrgveecG+ucOzFYxuH/1klLgR6Fcy4f/3X7GTO7wMzqmz/A1g3/tbciDYFC59wuM+vFjy2zWPwV+L2ZtTOvq5k1Bt4EjjazS82sppldjO9ffT3KOj7Bh9bNZpZuZufh+x+LPQdcH7QMzczqmj8wVz9aQc65efgP5l+BqcG+Ab8fHL7fGTMbhm+hR7M39RNMP9r8AcH04OenZtbRzGqZ2WVm1sA5txPYBOwuZz3jgNvMrK2Z1QP+ALzsnNsVscw9ZlbHzI7B99m/HLyeoWbWNGjRFb/m3fjw+x8z62/+IHaGmfU1s5bl1EBQ57/wreaD8QFfrD7+9NZtwfvl0oh5rwDnmNmJZlYL340S+ZkdB/zWzJoGBwHvDeorPvB9VBBgBUBRefvJOZeD79d/CXjVOVdY3msJDDezRuYP2N9CsM+irHc3/v32mJkdEtTVwsz6B4tMAK4ys07Bf1a/q2CbzwPDzOzU4LPYwsw6VFJn8TbODn4vHbgd31j72Mzam9kp5g8kb8P/51Peeyk5hN3nk8g/+K/5n+O/wq0DPgOuA2oF88cQ9PFF/M4F+K94P+CD6SngHxHzK+pDTwN+iz/f/Qf8h6xlMO9EYA7+wzkHOLGCurOAecE6Xg5+HoyYPyBYdz6+lfQvoH4F67snqPvCMtMfAr7HB/5I4ANKHyCbGbFsufXj+0CvKbPu9sAbwX7fAEwDuuEPZr2NP6i6KXgdUfcFPvzuxbfu1uHDrlEwr03wmq7Dt+L+C9wR8bv/wPevbsafZjo4Yt6xwWv9PljvG8Dh5b2WYPpJwfae3sv3y5X4YwYbgP8t837JAJ4I/oZrgscZwbzbgmW3ADnAPZW814cG9fWrZDkH3AwsD2r6PyAt2t88osY/BMtvwvfX3xwx/65g3+fhD2JH7UMPnp8LfBHsq2VA/2j7vGwdwe8txr/3PsB/YwB/EPrzYH3fB/u/edi5sz8/xWcviEg1ZmYn4/8Ta+0qCAUzc/gzdpYdsOIkZupyEanmgq6IW4C/VhTmkvgU6CLVmPlBNvn4s3QeD7UY2W/qchERSRFqoYuIpIjQLp7TpEkT16ZNm7A2LyKSlObMmbPeOdc02rzQAr1NmzbMnj278gVFRKREMAI9KnW5iIikCAW6iEiKUKCLiKSIhLqjyM6dO8nJyWHbtm1hl1ItZGRk0LJlS9LT08MuRUTiIKECPScnh/r169OmTRtKXyxQ4s05x4YNG8jJyaFt27ZhlyMicVBpl4uZvWD+9l1fljPfzOwJM1tmZl+YWY99LWbbtm00btxYYX4AmBmNGzfWtyGRFBJLH/oY/NX5ynMm/i4s7fBXrit7l/u9ojA/cLSvRVJLpV0uzrkPzaxNBYsMwt+GzQGfmllDM2vmnFsTryJFRJJO0TbYvv7Hn20Rj1ucA42z4r7JePSht6D0baRygml7BLqZXYdvxXP44RXdnCQ1vP/++/zpT3/i9ddfZ8qUKSxevJi77op+J7Ds7GzOOeccvvwyas+WiISpaAfs2FA6oLevh+3RpgU/u7aUv77MwxI20GPmnBsNjAbIyspK2quCFRUVkZaWtle/M3DgQAYOHFhFFYlIzHbvgh0byw/iaD87N5W/vvSDoHYT/5NxKDQ45sfne/w0hloHQ42qid54rDWX0vcFbEn0e13G3eR5uYyYupS8/EKaN8xkeP/2DO4e7b7JscvOzmbAgAH07NmTuXPncswxx/Diiy/SqVMnLr74Yt555x3uuOMODj74YH73u9+xfft2jjzySP72t79Rr1493n77bW699Vbq1KnDiSeeWLLeMWPGMHv2bJ566inWrl3L9ddfz/LlywEYNWoUzZs3p6ioiGuvvZaPP/6YFi1a8O9//5vMzEzmz5/P9ddfz9atWznyyCN54YUXaNSoEU888QTPPvssNWvWpFOnTowfP36/XrtIUtq9CzYvjz2cd2wsf10165YO4Prtyg/m2k2gVmNIqxVzqT6zPoxrZpUqPw7rmALcaGbj8bfmKjgQ/eeT5+Vy98SFFO4sAiA3v5C7Jy4E2O8dtHTpUp5//nl69+7N1VdfzTPPPANA48aNmTt3LuvXr+e8887j3XffpW7dujz66KOMHDmSO+64g2uvvZZp06Zx1FFHcfHFF0dd/80330yfPn2YNGkSRUVFbN68mY0bN/LNN98wbtw4nnvuOS666CJeffVVhg4dyhVXXMGTTz5Jnz59uPfee7n//vt5/PHHeeSRR1ixYgW1a9cmPz9/v16zSNIpXAPLnoNlo6EwShuyRm3IaPpjCNdtXX4wF4dzzcwqK7cqM6tYpYFuZuOAvkATM8vB38g1HcA59yz+BsBn4e/xtxV/o90qN2Lq0pIdU6xwZxEjpi7d753TqlUrevfuDcDQoUN54oknAEoC+tNPP2Xx4sUly+zYsYPjjz+er776irZt29KuXbuS3x09evQe6582bRovvvgiAGlpaTRo0ICNGzfStm1bunXrBkDPnj3Jzs6moKCA/Px8+vTpA8CVV17JhRdeCEDXrl257LLLGDx4MIMHD96v1yySFJyD7z6Ab56B1ZPA7YJm/aHr7yGzOWREhHVaHUigM7mqMrOKxXKWS4V3rQ/ObvllXKrZC3n50W9MXt70vVH2dL7i53Xr1gX8oJzTTz+dcePGlVpu/vz5+7Xd2rVrlzxOS0ujsLDi1/LGG2/w4Ycf8tprr/HQQw+xcOFCatZMqLFiIvGxowBWvAjfjIJNS3w/dIdb4aifQ/2jwq4uJlWZWcWS9louzRtG/2pU3vS9sWrVKj755BMAxo4dW6ovHOC4447jo48+Ytkyf5/cLVu28PXXX9OhQweys7P59ttvAfYI/GKnnnoqo0b50/WLioooKCgot5YGDRrQqFEjZsyYAcBLL71Enz592L17N6tXr6Zfv348+uijFBQUsHnz5v174SKJZuN8+PznMKk5zLkZ0uvDcWNgcA50H5E0YQ5Vm1nFkjbQh/dvT2Z66TNNMtPTGN6//X6vu3379jz99NN07NiRjRs3csMNN5Sa37RpU8aMGcOQIUPo2rVrSXdLRkYGo0eP5uyzz6ZHjx4ccsghUdf/5z//menTp9OlSxd69uzJ4sWLK6zn73//O8OHD6dr167Mnz+fe++9l6KiIoYOHUqXLl3o3r07N998Mw0bNtzv1y4SuqJtsOIf8J/e8FZ3WPEStBkCA2ZD/8/giCtL+ronz8ul9yPTaHvXG/R+ZBqT5x2Q8zH2SVVmVrHQ7imalZXlyt7gYsmSJXTs2DHmdVTVWS7V6Xzwvd3nIlVm8wpY9hf49nl/Nkr9o6HdDT7AazXaY/GyBxnBB+TD53WJ65kj8RSPzDKzOc65qCexJ3WH6+DuLRL2DyciMdhdBGve9n3jeW+C1YCWg6DdL+DQUyo8qHkgDjLGW1VnVlIHelVo06ZNtWmdi4Rm2zpY/gJ88yxsyYbMZtD5HjjqWqjTMqZVHIiDjMlGgS4iB4ZzsP4Tf8rhqn/B7h1waD9/cLPlIKixd9flb94wk9wo4R3Pg4zJRoEuIlVr52ZYORa+fgbyF/ih8kf93PePN9j34zfD+7eP2ocez4OMyUaBLiJVo2Cx7xtf8aK/FkqjbtBrNLQeAun19nv1xX3R8T4xIpkp0EUkfnbvhJzJvjX+3ftQoxYcfpE/yNnkuLiP3NSJEaUl7XnoB1J2djadO3cOu4w99O3bl7KnfoqEYmsOfHEvTD4cZl4EW1ZCt0f9AKATXoKmxyfUMPxUpRZ6SHbt2qVh+pLc3G5YO823xnOn+OfNz/Z94836Q429u8S07D+10KMYOXIknTt3pnPnzjz++OOAD+DLLruMjh07csEFF7B161YA7rrrLjp16kTXrl359a9/DcC6des4//zz+elPf8pPf/pTPvroIwDuu+8+Lr/8cnr37s3ll1/Occcdx6JFi0q2W9zi/vzzzzn++OPp3r07J5xwAkuXLgWgsLCQSy65hI4dO3LuueeWutbLuHHj6NKlC507d+bOO+8E/GUFrrrqKjp37kyXLl147LHHqnzfSTWwYyN89Ri83gGmnQ7rZkDH4TDwW+j7GrQ4S2EeksRtIs651V/HIZ4adYOej1e82Tlz+Nvf/sZnn32Gc45jjz2WPn36RL2k7rBhw5g0aRJfffUVZlZyCdtbbrmF2267jRNPPJFVq1bRv39/lixZAsDixYuZOXMmmZmZPPbYY0yYMIH777+fNWvWsGbNGrKysti0aRMzZsygZs2avPvuu/zmN7/h1VdfZdSoUdSpU4clS5bwxRdf0KOHvx93Xl4ed955J3PmzKFRo0acccYZTJ48mVatWpGbm1tyXr0usSuxKHc04/dzfGt85TgoKoSmvaHz7+DwCyCtduUrliqnFnoZM2fO5Nxzz6Vu3brUq1eP8847jxkzZuxxSd2ZM2fSoEEDMjIy+NnPfsbEiROpU6cOAO+++y433ngj3bp1Y+DAgWzatKnkwlkDBw4kM9OfJ3vRRRfxyiuvADBhwgQuuOACAAoKCrjwwgvp3Lkzt912W0kr/sMPP2To0KGAv3Ru165dAZg1axZ9+/aladOm1KxZk8suu4wPP/yQI444guXLl3PTTTfx9ttvc9BBBx2gvSjJqng4fW5+IQ5YX5DPZ++NZOPEbvB2Fqx6GdpeAWfOh9NnQtvLFOYJJHFb6JW0pA+0aJfUrVmzJp9//jnvvfcer7zyCk899RTTpk1j9+7dfPrpp2RkZOyxnuJL8AK0aNGCxo0b88UXX/Dyyy/z7LPPAnDPPffQr18/Jk2aRHZ2Nn379t2nmhs1asSCBQuYOnUqzz77LBMmTOCFF17Yp3VJ9VA8nL51rTwua/wWFzV6h4Y1N7NiU2saHf8ktL0cajUIu0wph1roZZx00klMnjyZrVu3smXLFiZNmsRJJ50U9ZK6mzdvpqCggLPOOovHHnuMBQsWAHDGGWfw5JNPlqyzouukX3zxxfzxj3+koKCgpMVdUFBAixb+VKwxY8aULHvyySczduxYAL788ku++OILAHr16sUHH3zA+vXrKSoqYty4cfTp04f169eze/duzj//fB588EHmzp0bt/0kKahwLaenTWDikbfzQYfrGNZkCjM3d+fibx/mlK+egvY3KswTXOK20EPSo0cPrrrqKnr16gXANddcQ6NGjUouqXv11VfTqVMnbrjhBgoKChg0aBDbtm3DOcfIkSMBeOKJJ/jlL39J165d2bVrFyeffHJJ67usCy64gFtuuYV77rmnZNodd9zBlVdeyYMPPsjZZ59dMv2GG25g2LBhdOzYkY4dO9KzZ08AmjVrxiOPPEK/fv1wznH22WczaNAgFixYwLBhw9i9ezcADz/8cJXsM0liOzf5O/9kj4W173Jfi90sKjyCh9dcxcSNp7Bu18EAtKjGw+mTSVJfPlf2n/Z5NVS0HfLe8sPxc1/z1x+v2xbaXMp7hady4xvbk+qStNVNyl4+V0RitLvI34tz5VhY9QrsLICMQ+DIa6HNpdD4WDDjVODh2vG/z4AcGAp0kVTlHGyc67tTVo6HwjyoWR9anedD/NBToMaeEaDh9Mkr4QLdObfHGSVSNcLqbpMqtulrf6549lj44Wt/PZXmZ/kQb35Oye3bJPUkVKBnZGSwYcMGGjdurFCvYs45NmzYEPXUSklCW/P8OeLZY+H72YD5a413usO3yKPcwk1ST0IFesuWLcnJyWHdunVhl1ItZGRk0LJlbHeHkQS0Ix9WT4Tsf8La6YCDg3tC9/+D1hdDHXWbVDcJFejp6em0bds27DJEEteuQsh7w7fE897wd/2pdxR0vhfaDIGDqu/NHSTBAl1Eoti9y1/VMHusb5Hv+gEyDvPXGG9zmW+Vq4tSUKCLJCbnYMNnPsRXvQzbvoP0BnD4hf7g5iF9dUVD2YMCXSSRFCwJTjMcC5uXQ43a0OJ/gjNUzoQ0HcSW8inQRcK2ZbU/T3zlWH/JaKsBh57m+8VbDtb1UyRmCnSRMGz/Hla/4lvj330IOD9as+ef/T04Mw8Lu0JJQgp0kQNl1xbIec23xNe87W+ofFAH6PoAtB4C9Y8Mu0JJcgp0kQNhxT9h1s99qGe2gPa3QOtL/V20dIaKxIkCXaSqbc2BWTdAgy7Q7RE45CTfTy4SZwp0karkHMz6Jbhd0PufUO+IsCuSFBZTM8HMBpjZUjNbZmZ3RZl/uJlNN7N5ZvaFmZ0V/1JFktDqVyF3iu8nV5hLFas00M0sDXgaOBPoBAwxs05lFvstMME51x24BHgm3oWKJJ0dG2H2TeRndOHkf/+Etne9Qe9HpjF5Xm7YlUmKiqWF3gtY5pxb7pzbAYwHBpVZxgHFt5RvAOTFr0SRJDXvTnZvW8fPllzHqvwdOCA3v5C7Jy5UqEuViCXQWwCrI57nBNMi3QcMNbMc4E3gpmgrMrPrzGy2mc3WFRUlpa39AL59jvGbzmfOD6UvOFe4s4gRU5eGVJiksngdah8CjHHOtQTOAl4y2/MwvnNutHMuyzmX1bRp0zhtWiTBFG2Dz6+Dum35/cqLoi6Sl194gIuS6iCWQM8FWkU8bxlMi/QzYAKAc+4TIANoEo8CRZLOlw/5OwX1+gsHN4h+Y4nmDXXXIIm/WAJ9FtDOzNqaWS38Qc8pZZZZBZwKYGYd8YGuPhWpfvK/hMWPQJvLodnpDO/fnsz00ldFzExPY3h/Xbdc4q/S89Cdc7vM7EZgKpAGvOCcW2RmDwCznXNTgNuB58zsNvwB0qucblgp1Y3bDZ9dC7UaQo+RACU3Wx4xdSl5+YU0b5jJ8P7tdRNmqRIWVu5mZWW52bNnh7JtkSrx9dMw+0Y4/iVoOzTsaiRFmdkc51xWtHkafywSD1tzYP7dcNjp/i5CIiFQoIvsr8jh/b2e1cW2JDS6lovI/ioe3t99hIb3S6jUQhfZH8Hwfhr1gPa3hl2NVHNqoYvsj3l3wvZ10PcNqKGPk4RLLXSRfRUM76fDbXBwj7CrEVGgi+yTiOH9dLk/7GpEAHW5iOyb4uH9/f4DNeuEXY0IoBa6yN4rM7xfJFEo0EX2Rsnw/gYlw/tFEoW6XET2xjejYMOncPyLkKELikpiUQtdJFalhvfrWi2SeBToIrHQ8H5JAupyEYmFhvdLElALXaQyGt4vSUItdJHKaHi/JAm10EUqouH9kkQU6CLl0fB+STL6/ihSHg3vlySjFrpINBreL0lIgS5SVqnh/f8XdjUiMVOXi0hZpYb3Nw27GpGYqYUuEknD+yWJKdBFiml4vyQ5dbmIFNPwfklyaqGLgIb3S0pQC10ENLxfUoJa6CIa3i8pQoEu1ZuG90sK0XdLqd5KhvdP1fB+SXpqoUv1VTK8fyg0OyPsakT2mwJdqqfdRRHD+0eGXY1IXKjLRZLS5Hm5jJi6lLz8Qpo3zGR4//YM7t4i9hUse1bD+yXlxNRCN7MBZrbUzJaZ2V3lLHORmS02s0VmNja+ZYr8aPK8XO6euJDc/EIckJtfyN0TFzJ5Xm5sK9DwfklRlQa6maUBTwNnAp2AIWbWqcwy7YC7gd7OuWOAW+Nfqog3YupSCncWlZpWuLOIEVOXVv7LGt4vKSyWFnovYJlzbrlzbgcwHhhUZplrgaedcxsBnHPfxbdMkR/l5Rfu1fRSiof3d31Aw/sl5cQS6C2A1RHPc4JpkY4Gjjazj8zsUzMbEG1FZnadmc02s9nr1q3bt4ql2mveMHOvppfQ8H5JcfE6y6Um0A7oCwwBnjOzhmUXcs6Nds5lOeeymjbVgSjZN8P7tyczPa3UtMz0NIb3b1/xLxYP7z/2OQ3vl5QUy7s6F2gV8bxlMC1SDvCZc24nsMLMvsYH/Ky4VCkSofhslr06y6V4eH/HX2t4v6SsWAJ9FtDOzNrig/wS4NIyy0zGt8z/ZmZN8F0wy+NYp0gpg7u3iP00xVLD+++r0rpEwlRpoDvndpnZjcBUIA14wTm3yMweAGY756YE884ws8VAETDcObehKgsXiVmp4f11w65GpMqYcy6UDWdlZbnZs2eHsm2pRvK/hLe6Q+tL4ISXwq5GZL+Z2RznXFa0eRr6L6lLw/ulmtGhfkldGt4v1Yxa6JKaNLxfqiEFuqQeDe+XakpdLpJ6iof3dx+h4f1SraiFLqlFw/ulGlMLXVJL8fD+vm9oeL9UO2qhS+ooHt7f4TYN75dqSYEuqaFkeH8bDe+XakvfSSU1aHi/iFrokgLyv4TFj/jzzZudEXY1IqFRoEty0/B+kRLqcpHkpuH9IiXUQpfkpeH9IqUo0CU5ud0w6xca3i8SQV0uknycg9k3Qu5r0OMxDe8XCaiFLsnFOZhzM3wzCjreAe1vCbsikYShQJfk4RzMvQ2+fgo63A7dHlFXi0gEBbokB+dg3q9h6Z99q7z7CIW5SBkKdEl8zsH8O+GrkXD0Tb7fXGEusgcFuiQ252DBb2DJCGj3C+j5Z4W5SDkU6JK4nIMv7vHD+o/6OWQ9qTAXqYACXRLXwvth0UNw5DXw02fA9HYVqYg+IZKYFv4evrwfjhgGvf6iMBeJgT4lkngW/QEW3gttr4BezynMRWKkT4oklsWPwoL/9ddmOfYFqJEWdkUiSUOBLoljyZ9g/l3QeggcN0ZhLrKXFOiSGL56DOYNh8Mv9pfCVZiL7DUFuoRv6RMw91fQ6gI44R9QQ9eME9kXCnQJ19dPw5xboOW50HuswlxkPyjQJTzfPOsvg9tyEPQeDzXSw65IJKkp0CUcy0bDrBug+TnQewKk1Qq7IpGkp0CXA+/b5+Hzn0Pzs+CkVxTmInESU6Cb2QAzW2pmy8zsrgqWO9/MnJllxa9ESSnLx8Bn10Kz/nDSq5BWO+yKRFJGpYFuZmnA08CZQCdgiJl1irJcfeAW4LN4FykpYsVL8OnVcNhpcNIkSMsIuyKRlBJLC70XsMw5t9w5twMYDwyKstzvgUeBbXGsT1JF9lj49Co4tB+cPBlqZoZdkUjKiSXQWwCrI57nBNNKmFkPoJVz7o2KVmRm15nZbDObvW7dur0uVpJU9nj45HJoejL0eQ1q1gm7IpGUtN8HRc2sBjASuL2yZZ1zo51zWc65rKZNm+7vpiUZrPoXfDIUmp4IfV9XmItUoVgCPRdoFfG8ZTCtWH2gM/C+mWUDxwFTdGBUWPUqfDQEmhwPfd6AmnXDrkgkpcUS6LOAdmbW1sxqAZcAU4pnOucKnHNNnHNtnHNtgE+Bgc652VVSsSSH1ZPho0ugcS/o+yak1wu7IpGUV2mgO+d2ATcCU4ElwATn3CIze8DMBlZ1gZKEcl6Djy6Cg3tCv7chvX7YFYlUCzFdOMM59ybwZplp95azbN/9L0sOtMnzchkxdSl5+YU0b5jJ8P7tGdy9ReW/WFbuGzDzfGjYDfpNhfSD4l6riESnKyEJk+flcvfEhRTuLAIgN7+QuycuBNi7UM97C2acBw27win/gVoNqqJcESmHhv4LI6YuLQnzYoU7ixgxdWnsK8mbCh+eCw2OgX7/gVoN41ukiFRKgS7k5Rfu1fQ9/PddmDEYDuoAp7wDtQ+OX3EiEjMFutC8YfRRm+VNL+W/0+CD/4H67eCUd6F24zhXJyKxUqALw/u3JzO99C3fMtPTGN6/fcW/uPYD+OAcqHcknPIeZDSpwipFpDI6KColBz736iyX72bA+2dB3TZBmGvkr0jYFOgC+FCP+YyWdR/B+2dC3cPh1GmQeWjVFiciMVGXi+yddZ/A9AGQ2SII88PCrkhEAgp0id36z2B6f8g4LAjzZmFXJCIRFOgSmw2zYPoZvq/8tOlQZx9GkYpIlVKgS+W+nwvTzoBajeHU6VCnZdgViUgUCnSp2PfzYNppfhj/adP9gVARSUgKdCnfxgU+zGvW9y3zuq3DrkhEKqBAl+jyF8K0U/0dhk6bDvXahl2RiFRCgS57yl8E750KNWr7lnm9I8KuSERioECX0gqWwLRToEZNH+b1jwq7IhGJkQJdvN27IO9teO8UwOCUaXDQ0WFXJSJ7QUP/qzPn4PvZkP1PWDketq31g4VOeQ8adAi7OhHZSwr06uiHZT7Es8fCD19DjVrQ4hxocxk0PwvSMsKuUET2gQK9uihcC6te9kG+4XPA4NC+0OkOaHW+7jAkkgIU6Kls52bImexD/L/vgCuChj+Bbn+ENkM04lMkxSjQU83unbDmPz7Ec/4NRVv9gKCOd/gulYbHhF2hiFQRBXoqcA7Wf+JDfNUE2L4eah0Mba/wId70BDCd0CSS6hToyaxgyY8HN7es8AczWwzyId6sP6TVCrtCETmAFOjJZmserBzng3zjPN/yPvQ06HIftDoX0uuHXaGIhESBngx2FMDqiT7E104DHBycBT0eh9YX665BIgIo0BNX0XbIe8uHeO5rsHs71DsSOt/ju1Q0ilNEylCgJxK3G76b4UN89SuwYyNkHAJHXedDvHEvMAu7ShFJUAr0RLDxi2D4/TjYuhpq1oWW5/oQP+w0f6EsEZFKKCnCsmWVD/AV/4CCL8Fq+jNTuj0KLQf6UBcR2QsK9ANp+/e+KyX7n/Ddh35akxMg62k4/EJ/A2YRkX2kQK9quwoh73Uf4nlv+pGcB3WErg9Cm0t1JyARiRsFelUp2gHL/gJf3g/bN/jL0h59E7QZCo266eCmiMRdTIFuZgOAPwNpwF+dc4+Umf8r4BpgF7AOuNo5tzLOtSYH5/wFsebfCT98A4eeCsfcDYf0hRppYVcnIims0kA3szTgaeB0IAeYZWZTnHOLIxabB2Q557aa2Q3AH4GLq6LghLb+c5h3O6ybCQ06Qd83odkAtcZF5ICIpYXeC1jmnFsOYGbjgUFASaA756ZHLP8pMDSeRSa8zStgwW/8XX8yDoVeo+GIYTrdUEQOqFgSpwWwOuJ5DnBsBcv/DHgr2gwzuw64DuDwww+PscQEtmMjLPoDLH0CLM2P4uw4XNdTEZFQxLUJaWZDgSygT7T5zrnRwGiArKwsF89tH1BFO+CbUfDlAz7Uj7gKuv4e6rQIuzIRqcZiCfRcoFXE85bBtFLM7DTgf4E+zrnt8SkvwTgHOZNg3p2weZkfxdn9T9DoJ2FXJiISU6DPAtqZWVt8kF8CXBq5gJl1B/4CDHDOfRf3KhPB+s+CA54fQYNjoO9bfmSnDniKSIKoNNCdc7vM7EZgKv60xRecc4vM7AFgtnNuCjACqAf8y3zArXLODazCug+czStg/t3+Bss64CkiCSymVHLOvQm8WWbavRGPT4tzXeHb44DnvcEBz3oxr2LyvFxGTF1KXn4hzRtmMrx/ewZ3Vz+7iFQNNTPL2uOA5zDo+sBeH/CcPC+XuycupHBnEQC5+YXcPXEhgEJdRKqE7hxczDlY9Sq80Qnm3goH94Qz58Fxz+/T2Ssjpi4tCfNihTuLGDF1aZwKFhEpTS10gPWfwtzbYf3HcTvgmZdfuFfTRUT2V/UO9M3LgwOeEyDjMOj1nD+nPA4HPJs3zCQ3Sng3b5i53+sWEYmmena57NjoW+Svd4Dc16Hz7+B/voGjronb2SvD+7cnM730xbgy09MY3r99XNYvIlJW9WqhF+2Ab54JDnjm7/MBz1gUH/jUWS4icqBUj0B3Dla/CvPvgs3fwmGnByM8u1bpZgd3b6EAF5EDJvUDfd0nfoTn+k+gQWfo+zY07x92VSIicZe6gR71gOcw3WRCRFJW6gX69u9h0UPw9ZNg6f6AZ8df79UITxGRZJQ6gV60PTjg+Xt/wPPIq6HLA1CnediViYgcEMkf6M7B6leCA57L/YCgbn+s8gOeIiKJJrkDfd3HMO/X/oBnwy464Cki1VpyBvoP3/oW+epXILMZHPs8tL1SBzxFpFpLvkD/ZhTMucUf8OxyH3S4XQc8RURIxkBv1B3aXuHv4ZnZLOxqREQSRvIFepPj/I+IiJRSPS/OJSKSghToIiIpQoEuIpIiFOgiIikiqQ6KTp6Xq+uLi4iUI2kCffK8XO6euLDkxsu5+YXcPXEhgEJdRIQk6nIZMXVpSZgXK9xZxIipS0OqSEQksSRNoOdFueFyRdNFRKqbpAn05g0z92q6iEh1kzSBPrx/ezLTS198KzM9jeH924dUkYhIYkmag6LFBz51louISHRJE+jgQ10BLiISXdJ0uYiISMUU6CIiKUKBLiKSIhToIiIpQoEuIpIizDkXzobN1gEr9/HXmwDr41hOVUumepOpVkiuepOpVkiuepOpVti/els755pGmxFaoO8PM5vtnMsKu45YJVO9yVQrJFe9yVQrJFe9yVQrVF296nIREUkRCnQRkRSRrIE+OuwC9lIy1ZtMtUJy1ZtMtUJy1ZtMtUIV1ZuUfegiIrKnZG2hi4hIGQp0EZEUkXSBbmYDzGypmS0zs7vCrqciZvaCmX1nZl+GXUtlzKyVmU03s8VmtsjMbgm7pvKYWYaZfW5mC4Ja7w+7pliYWZqZzTOz18OupSJmlm1mC81svpnNDrueyphZQzN7xcy+MrMlZnZ82DVFY2btg31a/LPJzG6N6zaSqQ/dzNKAr4HTgRxgFjDEObc41MLKYWYnA5uBF51zncOupyJm1gxo5pyba2b1gTnA4ETct2ZmQF3n3GYzSwdmArc45z4NubQKmdmvgCzgIOfcOWHXUx4zywaynHNJMVDHzP4OzHDO/dXMagF1nHP5IZdVoSDLcoFjnXP7OsByD8nWQu8FLHPOLXfO7QDGA4NCrqlczrkPge/DriMWzrk1zrm5weMfgCVAQl583nmbg6fpwU9Ct0zMrCVwNvDXsGtJJWbWADgZeB7AObcj0cM8cCrwbTzDHJIv0FsAqyOe55CgoZPMzKwN0B34LORSyhV0X8wHvgPecc4lbK2Bx4E7gN0h1xELB/zHzOaY2XVhF1OJtsA64G9Bd9Zfzaxu2EXF4BJgXLxXmmyBLlXMzOoBrwK3Ouc2hV1PeZxzRc65bkBLoJeZJWyXlpmdA3znnJsTdi0xOtE51wM4E/hl0HWYqGoCPYBRzrnuwBYg0Y+t1QIGAv+K97qTLdBzgVYRz1sG0yQOgv7oV4F/Oucmhl1PLIKv19OBASGXUpHewMCgb3o8cIqZ/SPcksrnnMsN/v0OmITv6kxUOUBOxDe0V/ABn8jOBOY659bGe8XJFuizgHZm1jb4X+4SYErINaWE4EDj88AS59zIsOupiJk1NbOGweNM/EHyr0ItqgLOubudcy2dc23w79lpzrmhIZcVlZnVDQ6KE3RdnAEk7Flazrn/AqvNrH0w6VQg4Q7klzGEKuhugSS7SbRzbpeZ3QhMBdKAF5xzi0Iuq1xmNg7oCzQxsxzgd86558Otqly9gcuBhUHfNMBvnHNvhldSuZoBfw/OFKgBTHDOJfSpgEnkUGCS//+dmsBY59zb4ZZUqZuAfwaNvOXAsJDrKVfwn+TpwM+rZP3JdNqiiIiUL9m6XEREpBwKdBGRFKFAFxFJEQp0EZEUoUAXEUkRCnQRkRShQBcRSRH/D46B+Z3mPPcgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(fit_data['indice'], fit_data['yhat'], label='predichos')\n",
    "plt.plot(fit_data['indice'], fit_data['y'], label='observados', color=\"orange\")\n",
    "plt.legend()\n",
    "plt.title('Gráfico de valores observados y predichos')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <span style=\"color:#4CC9F0\">Curva ajustados v.s. predichos</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.graphics.api import abline_plot\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(yhat, y)\n",
    "line_fit = sm.OLS(y, sm.add_constant(yhat, prepend=True)).fit()\n",
    "abline_plot(model_results=line_fit, ax=ax)\n",
    "\n",
    "ax.set_title('Gráfico de ajuste del modelo')\n",
    "ax.set_ylabel('Valores observados')\n",
    "ax.set_xlabel('Valores ajustados');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <span style=\"color:#4CC9F0\">Residuales de Pearson</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.scatter(yhat, res.resid_pearson)\n",
    "ax.hlines(0, 0, 1)\n",
    "ax.set_xlim(0, 1)\n",
    "ax.set_title('Gráfico de Dependencia de Residuales')\n",
    "ax.set_ylabel(' Residuales de Pearson')\n",
    "ax.set_xlabel('Valores Ajustados')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <span style=\"color:#4CC9F0\">Histograma de residuales deviance estandarizados</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "resid = res.resid_deviance.copy()\n",
    "resid_std = stats.zscore(resid)\n",
    "ax.hist(resid_std, bins=3)\n",
    "ax.set_title('Histograma de Residuales Deviance Estandarizados');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <span style=\"color:#4CC9F0\">qq plot de residuales deviance</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels import graphics\n",
    "graphics.gofplots.qqplot(resid, line='r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <span style=\"color:#4CC9F0\">Dosis Letal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La dosis letal se define como la dosis que téoricamente mata al 50% de los expuestos.\n",
    "\n",
    "De la relación\n",
    "\n",
    "$$\n",
    "\\log \\frac{\\pi}{1-\\pi} = intercept +\\beta*dosis\n",
    "$$\n",
    "\n",
    "se obtiene \n",
    "\n",
    "$$\n",
    "\\log \\frac{0.5}{0.5} = 0 =  -60.7175 +34.2703*dosis\n",
    "$$\n",
    "\n",
    "de donde la dosis letal es \n",
    "\n",
    "$$\n",
    "\\text{dosis  letal} = \\frac{60.7175}{34.2703}  = 1.77\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <span style=\"color:#4CC9F0\">Ejemplo de regresión Binomial: Star9</span> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En este ejemplo, usamos el conjunto de datos Star9 de los ejemplos de statmodels, el cual que se tomó con permiso de Jeff Gill (2000) Modelos lineales generalizados: un enfoque unificado. \n",
    "\n",
    "La variable de respuesta (endog) es una típica variable $\\text{Binomial}(n_i,x_i)$. Son datos de personas que presentan una prueba estatal. La variable de respuesta está divida en dos partes: *NABOVE* que corresponde al número de personas en cada estado  con puntaje por encima de la mediana nacional para la parte de matemáticas de la prueba y *NBLOW* el número de personas en cada estado  con puntaje por debajo de la mediana nacional para la parte de matemáticas de la prueba."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " La documentación de los datos se puede obtener escribiendo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "::\n",
      "\n",
      "    Number of Observations - 303 (counties in California).\n",
      "\n",
      "    Number of Variables - 13 and 8 interaction terms.\n",
      "\n",
      "    Definition of variables names::\n",
      "\n",
      "        NABOVE   - Total number of students above the national median for the\n",
      "                   math section.\n",
      "        NBELOW   - Total number of students below the national median for the\n",
      "                   math section.\n",
      "        LOWINC   - Percentage of low income students\n",
      "        PERASIAN - Percentage of Asian student\n",
      "        PERBLACK - Percentage of black students\n",
      "        PERHISP  - Percentage of Hispanic students\n",
      "        PERMINTE - Percentage of minority teachers\n",
      "        AVYRSEXP - Sum of teachers' years in educational service divided by the\n",
      "                number of teachers.\n",
      "        AVSALK   - Total salary budget including benefits divided by the number\n",
      "                   of full-time teachers (in thousands)\n",
      "        PERSPENK - Per-pupil spending (in thousands)\n",
      "        PTRATIO  - Pupil-teacher ratio.\n",
      "        PCTAF    - Percentage of students taking UC/CSU prep courses\n",
      "        PCTCHRT  - Percentage of charter schools\n",
      "        PCTYRRND - Percentage of year-round schools\n",
      "\n",
      "        The below variables are interaction terms of the variables defined\n",
      "        above.\n",
      "\n",
      "        PERMINTE_AVYRSEXP\n",
      "        PEMINTE_AVSAL\n",
      "        AVYRSEXP_AVSAL\n",
      "        PERSPEN_PTRATIO\n",
      "        PERSPEN_PCTAF\n",
      "        PTRATIO_PCTAF\n",
      "        PERMINTE_AVTRSEXP_AVSAL\n",
      "        PERSPEN_PTRATIO_PCTAF\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(sm.datasets.star98.NOTE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Carga los datos y agrega una constante a las variables exógenas (independientes):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = sm.datasets.star98.load()\n",
    "\n",
    "data.exog = sm.add_constant(data.exog, prepend=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['LOWINC', 'PERASIAN', 'PERBLACK', 'PERHISP', 'PERMINTE', 'AVYRSEXP',\n",
       "       'AVSALK', 'PERSPENK', 'PTRATIO', 'PCTAF', 'PCTCHRT', 'PCTYRRND',\n",
       "       'PERMINTE_AVYRSEXP', 'PERMINTE_AVSAL', 'AVYRSEXP_AVSAL',\n",
       "       'PERSPEN_PTRATIO', 'PERSPEN_PCTAF', 'PTRATIO_PCTAF',\n",
       "       'PERMINTE_AVYRSEXP_AVSAL', 'PERSPEN_PTRATIO_PCTAF', 'const'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.exog.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "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>LOWINC</th>\n",
       "      <th>PERASIAN</th>\n",
       "      <th>PERBLACK</th>\n",
       "      <th>PERHISP</th>\n",
       "      <th>PERMINTE</th>\n",
       "      <th>AVYRSEXP</th>\n",
       "      <th>AVSALK</th>\n",
       "      <th>PERSPENK</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>PCTAF</th>\n",
       "      <th>...</th>\n",
       "      <th>PCTYRRND</th>\n",
       "      <th>PERMINTE_AVYRSEXP</th>\n",
       "      <th>PERMINTE_AVSAL</th>\n",
       "      <th>AVYRSEXP_AVSAL</th>\n",
       "      <th>PERSPEN_PTRATIO</th>\n",
       "      <th>PERSPEN_PCTAF</th>\n",
       "      <th>PTRATIO_PCTAF</th>\n",
       "      <th>PERMINTE_AVYRSEXP_AVSAL</th>\n",
       "      <th>PERSPEN_PTRATIO_PCTAF</th>\n",
       "      <th>const</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>34.39730</td>\n",
       "      <td>23.299300</td>\n",
       "      <td>14.235280</td>\n",
       "      <td>11.411120</td>\n",
       "      <td>15.918370</td>\n",
       "      <td>14.70646</td>\n",
       "      <td>59.15732</td>\n",
       "      <td>4.445207</td>\n",
       "      <td>21.71025</td>\n",
       "      <td>57.03276</td>\n",
       "      <td>...</td>\n",
       "      <td>22.222220</td>\n",
       "      <td>234.102872</td>\n",
       "      <td>941.68811</td>\n",
       "      <td>869.9948</td>\n",
       "      <td>96.50656</td>\n",
       "      <td>253.52242</td>\n",
       "      <td>1238.1955</td>\n",
       "      <td>13848.8985</td>\n",
       "      <td>5504.0352</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17.36507</td>\n",
       "      <td>29.328380</td>\n",
       "      <td>8.234897</td>\n",
       "      <td>9.314884</td>\n",
       "      <td>13.636360</td>\n",
       "      <td>16.08324</td>\n",
       "      <td>59.50397</td>\n",
       "      <td>5.267598</td>\n",
       "      <td>20.44278</td>\n",
       "      <td>64.62264</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>219.316851</td>\n",
       "      <td>811.41756</td>\n",
       "      <td>957.0166</td>\n",
       "      <td>107.68435</td>\n",
       "      <td>340.40609</td>\n",
       "      <td>1321.0664</td>\n",
       "      <td>13050.2233</td>\n",
       "      <td>6958.8468</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>32.64324</td>\n",
       "      <td>9.226386</td>\n",
       "      <td>42.406310</td>\n",
       "      <td>13.543720</td>\n",
       "      <td>28.834360</td>\n",
       "      <td>14.59559</td>\n",
       "      <td>60.56992</td>\n",
       "      <td>5.482922</td>\n",
       "      <td>18.95419</td>\n",
       "      <td>53.94191</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>420.854496</td>\n",
       "      <td>1746.49488</td>\n",
       "      <td>884.0537</td>\n",
       "      <td>103.92435</td>\n",
       "      <td>295.75929</td>\n",
       "      <td>1022.4252</td>\n",
       "      <td>25491.1232</td>\n",
       "      <td>5605.8777</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.90953</td>\n",
       "      <td>13.883090</td>\n",
       "      <td>3.796973</td>\n",
       "      <td>11.443110</td>\n",
       "      <td>11.111110</td>\n",
       "      <td>14.38939</td>\n",
       "      <td>58.33411</td>\n",
       "      <td>4.165093</td>\n",
       "      <td>21.63539</td>\n",
       "      <td>49.06103</td>\n",
       "      <td>...</td>\n",
       "      <td>7.142857</td>\n",
       "      <td>159.882095</td>\n",
       "      <td>648.15671</td>\n",
       "      <td>839.3923</td>\n",
       "      <td>90.11341</td>\n",
       "      <td>204.34375</td>\n",
       "      <td>1061.4545</td>\n",
       "      <td>9326.5797</td>\n",
       "      <td>4421.0568</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36.88889</td>\n",
       "      <td>12.187500</td>\n",
       "      <td>76.875000</td>\n",
       "      <td>7.604167</td>\n",
       "      <td>43.589740</td>\n",
       "      <td>13.90568</td>\n",
       "      <td>63.15364</td>\n",
       "      <td>4.324902</td>\n",
       "      <td>18.77984</td>\n",
       "      <td>52.38095</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>606.144976</td>\n",
       "      <td>2752.85075</td>\n",
       "      <td>878.1943</td>\n",
       "      <td>81.22097</td>\n",
       "      <td>226.54248</td>\n",
       "      <td>983.7059</td>\n",
       "      <td>38280.2616</td>\n",
       "      <td>4254.4314</td>\n",
       "      <td>1.0</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>298</th>\n",
       "      <td>62.23968</td>\n",
       "      <td>13.820860</td>\n",
       "      <td>3.956157</td>\n",
       "      <td>32.128790</td>\n",
       "      <td>15.884480</td>\n",
       "      <td>13.27089</td>\n",
       "      <td>52.88133</td>\n",
       "      <td>4.324174</td>\n",
       "      <td>20.98711</td>\n",
       "      <td>8.00000</td>\n",
       "      <td>...</td>\n",
       "      <td>18.181820</td>\n",
       "      <td>210.801187</td>\n",
       "      <td>839.99243</td>\n",
       "      <td>701.7823</td>\n",
       "      <td>90.75192</td>\n",
       "      <td>34.59339</td>\n",
       "      <td>167.8969</td>\n",
       "      <td>11147.4471</td>\n",
       "      <td>726.0153</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>46.16188</td>\n",
       "      <td>1.150575</td>\n",
       "      <td>0.800400</td>\n",
       "      <td>45.622810</td>\n",
       "      <td>13.333330</td>\n",
       "      <td>11.80714</td>\n",
       "      <td>48.42615</td>\n",
       "      <td>3.945069</td>\n",
       "      <td>19.85830</td>\n",
       "      <td>53.77358</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>157.428494</td>\n",
       "      <td>645.68184</td>\n",
       "      <td>571.7743</td>\n",
       "      <td>78.34236</td>\n",
       "      <td>212.14048</td>\n",
       "      <td>1067.8519</td>\n",
       "      <td>7623.6559</td>\n",
       "      <td>4212.7494</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>43.41431</td>\n",
       "      <td>3.648564</td>\n",
       "      <td>1.541204</td>\n",
       "      <td>46.319980</td>\n",
       "      <td>21.377670</td>\n",
       "      <td>14.40468</td>\n",
       "      <td>53.79616</td>\n",
       "      <td>3.845481</td>\n",
       "      <td>22.70884</td>\n",
       "      <td>29.89247</td>\n",
       "      <td>...</td>\n",
       "      <td>6.666667</td>\n",
       "      <td>307.938495</td>\n",
       "      <td>1150.03656</td>\n",
       "      <td>774.9165</td>\n",
       "      <td>87.32641</td>\n",
       "      <td>114.95093</td>\n",
       "      <td>678.8233</td>\n",
       "      <td>16565.9086</td>\n",
       "      <td>2610.4022</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>70.62104</td>\n",
       "      <td>20.461720</td>\n",
       "      <td>2.989345</td>\n",
       "      <td>17.423050</td>\n",
       "      <td>11.032860</td>\n",
       "      <td>16.56420</td>\n",
       "      <td>61.70566</td>\n",
       "      <td>3.970984</td>\n",
       "      <td>24.88647</td>\n",
       "      <td>15.14286</td>\n",
       "      <td>...</td>\n",
       "      <td>13.636360</td>\n",
       "      <td>182.750500</td>\n",
       "      <td>680.78991</td>\n",
       "      <td>1022.1049</td>\n",
       "      <td>98.82377</td>\n",
       "      <td>60.13205</td>\n",
       "      <td>376.8523</td>\n",
       "      <td>11276.7402</td>\n",
       "      <td>1496.4746</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>77.74827</td>\n",
       "      <td>5.998899</td>\n",
       "      <td>9.576225</td>\n",
       "      <td>11.117230</td>\n",
       "      <td>7.874016</td>\n",
       "      <td>16.91871</td>\n",
       "      <td>65.09957</td>\n",
       "      <td>6.913397</td>\n",
       "      <td>20.03252</td>\n",
       "      <td>23.52941</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>133.218193</td>\n",
       "      <td>512.59506</td>\n",
       "      <td>1101.4007</td>\n",
       "      <td>138.49276</td>\n",
       "      <td>162.66815</td>\n",
       "      <td>471.3534</td>\n",
       "      <td>8672.4471</td>\n",
       "      <td>3258.6530</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>303 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       LOWINC   PERASIAN   PERBLACK    PERHISP   PERMINTE  AVYRSEXP    AVSALK  \\\n",
       "0    34.39730  23.299300  14.235280  11.411120  15.918370  14.70646  59.15732   \n",
       "1    17.36507  29.328380   8.234897   9.314884  13.636360  16.08324  59.50397   \n",
       "2    32.64324   9.226386  42.406310  13.543720  28.834360  14.59559  60.56992   \n",
       "3    11.90953  13.883090   3.796973  11.443110  11.111110  14.38939  58.33411   \n",
       "4    36.88889  12.187500  76.875000   7.604167  43.589740  13.90568  63.15364   \n",
       "..        ...        ...        ...        ...        ...       ...       ...   \n",
       "298  62.23968  13.820860   3.956157  32.128790  15.884480  13.27089  52.88133   \n",
       "299  46.16188   1.150575   0.800400  45.622810  13.333330  11.80714  48.42615   \n",
       "300  43.41431   3.648564   1.541204  46.319980  21.377670  14.40468  53.79616   \n",
       "301  70.62104  20.461720   2.989345  17.423050  11.032860  16.56420  61.70566   \n",
       "302  77.74827   5.998899   9.576225  11.117230   7.874016  16.91871  65.09957   \n",
       "\n",
       "     PERSPENK   PTRATIO     PCTAF  ...   PCTYRRND  PERMINTE_AVYRSEXP  \\\n",
       "0    4.445207  21.71025  57.03276  ...  22.222220         234.102872   \n",
       "1    5.267598  20.44278  64.62264  ...   0.000000         219.316851   \n",
       "2    5.482922  18.95419  53.94191  ...   0.000000         420.854496   \n",
       "3    4.165093  21.63539  49.06103  ...   7.142857         159.882095   \n",
       "4    4.324902  18.77984  52.38095  ...   0.000000         606.144976   \n",
       "..        ...       ...       ...  ...        ...                ...   \n",
       "298  4.324174  20.98711   8.00000  ...  18.181820         210.801187   \n",
       "299  3.945069  19.85830  53.77358  ...   0.000000         157.428494   \n",
       "300  3.845481  22.70884  29.89247  ...   6.666667         307.938495   \n",
       "301  3.970984  24.88647  15.14286  ...  13.636360         182.750500   \n",
       "302  6.913397  20.03252  23.52941  ...   0.000000         133.218193   \n",
       "\n",
       "     PERMINTE_AVSAL  AVYRSEXP_AVSAL  PERSPEN_PTRATIO  PERSPEN_PCTAF  \\\n",
       "0         941.68811        869.9948         96.50656      253.52242   \n",
       "1         811.41756        957.0166        107.68435      340.40609   \n",
       "2        1746.49488        884.0537        103.92435      295.75929   \n",
       "3         648.15671        839.3923         90.11341      204.34375   \n",
       "4        2752.85075        878.1943         81.22097      226.54248   \n",
       "..              ...             ...              ...            ...   \n",
       "298       839.99243        701.7823         90.75192       34.59339   \n",
       "299       645.68184        571.7743         78.34236      212.14048   \n",
       "300      1150.03656        774.9165         87.32641      114.95093   \n",
       "301       680.78991       1022.1049         98.82377       60.13205   \n",
       "302       512.59506       1101.4007        138.49276      162.66815   \n",
       "\n",
       "     PTRATIO_PCTAF  PERMINTE_AVYRSEXP_AVSAL  PERSPEN_PTRATIO_PCTAF  const  \n",
       "0        1238.1955               13848.8985              5504.0352    1.0  \n",
       "1        1321.0664               13050.2233              6958.8468    1.0  \n",
       "2        1022.4252               25491.1232              5605.8777    1.0  \n",
       "3        1061.4545                9326.5797              4421.0568    1.0  \n",
       "4         983.7059               38280.2616              4254.4314    1.0  \n",
       "..             ...                      ...                    ...    ...  \n",
       "298       167.8969               11147.4471               726.0153    1.0  \n",
       "299      1067.8519                7623.6559              4212.7494    1.0  \n",
       "300       678.8233               16565.9086              2610.4022    1.0  \n",
       "301       376.8523               11276.7402              1496.4746    1.0  \n",
       "302       471.3534                8672.4471              3258.6530    1.0  \n",
       "\n",
       "[303 rows x 21 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.exog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#La variable dependiente (Éxito: NABOVE, Fracaso: NBELOW):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "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>NABOVE</th>\n",
       "      <th>NBELOW</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>452.0</td>\n",
       "      <td>355.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>144.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>337.0</td>\n",
       "      <td>234.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>395.0</td>\n",
       "      <td>178.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.0</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>96.0</td>\n",
       "      <td>246.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>37.0</td>\n",
       "      <td>117.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>208.0</td>\n",
       "      <td>387.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>199.0</td>\n",
       "      <td>510.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>72.0</td>\n",
       "      <td>84.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>303 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     NABOVE  NBELOW\n",
       "0     452.0   355.0\n",
       "1     144.0    40.0\n",
       "2     337.0   234.0\n",
       "3     395.0   178.0\n",
       "4       8.0    57.0\n",
       "..      ...     ...\n",
       "298    96.0   246.0\n",
       "299    37.0   117.0\n",
       "300   208.0   387.0\n",
       "301   199.0   510.0\n",
       "302    72.0    84.0\n",
       "\n",
       "[303 rows x 2 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.endog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0.560099\n",
       "1      0.782609\n",
       "2      0.590193\n",
       "3      0.689354\n",
       "4      0.123077\n",
       "         ...   \n",
       "298    0.280702\n",
       "299    0.240260\n",
       "300    0.349580\n",
       "301    0.280677\n",
       "302    0.461538\n",
       "Length: 303, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.endog.iloc[:,0]/data.endog.sum(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <span style=\"color:#4CC9F0\">Ajuste del modelo</span> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  Generalized Linear Model Regression Results                   \n",
      "================================================================================\n",
      "Dep. Variable:     ['NABOVE', 'NBELOW']   No. Observations:                  303\n",
      "Model:                              GLM   Df Residuals:                      282\n",
      "Model Family:                  Binomial   Df Model:                           20\n",
      "Link Function:                    Logit   Scale:                          1.0000\n",
      "Method:                            IRLS   Log-Likelihood:                -2998.6\n",
      "Date:                  Sat, 02 Sep 2023   Deviance:                       4078.8\n",
      "Time:                          09:15:14   Pearson chi2:                 4.05e+03\n",
      "No. Iterations:                       5   Pseudo R-squ. (CS):              1.000\n",
      "Covariance Type:              nonrobust                                         \n",
      "===========================================================================================\n",
      "                              coef    std err          z      P>|z|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------------\n",
      "LOWINC                     -0.0168      0.000    -38.749      0.000      -0.018      -0.016\n",
      "PERASIAN                    0.0099      0.001     16.505      0.000       0.009       0.011\n",
      "PERBLACK                   -0.0187      0.001    -25.182      0.000      -0.020      -0.017\n",
      "PERHISP                    -0.0142      0.000    -32.818      0.000      -0.015      -0.013\n",
      "PERMINTE                    0.2545      0.030      8.498      0.000       0.196       0.313\n",
      "AVYRSEXP                    0.2407      0.057      4.212      0.000       0.129       0.353\n",
      "AVSALK                      0.0804      0.014      5.775      0.000       0.053       0.108\n",
      "PERSPENK                   -1.9522      0.317     -6.162      0.000      -2.573      -1.331\n",
      "PTRATIO                    -0.3341      0.061     -5.453      0.000      -0.454      -0.214\n",
      "PCTAF                      -0.1690      0.033     -5.169      0.000      -0.233      -0.105\n",
      "PCTCHRT                     0.0049      0.001      3.921      0.000       0.002       0.007\n",
      "PCTYRRND                   -0.0036      0.000    -15.878      0.000      -0.004      -0.003\n",
      "PERMINTE_AVYRSEXP          -0.0141      0.002     -7.391      0.000      -0.018      -0.010\n",
      "PERMINTE_AVSAL             -0.0040      0.000     -8.450      0.000      -0.005      -0.003\n",
      "AVYRSEXP_AVSAL             -0.0039      0.001     -4.059      0.000      -0.006      -0.002\n",
      "PERSPEN_PTRATIO             0.0917      0.015      6.321      0.000       0.063       0.120\n",
      "PERSPEN_PCTAF               0.0490      0.007      6.574      0.000       0.034       0.064\n",
      "PTRATIO_PCTAF               0.0080      0.001      5.362      0.000       0.005       0.011\n",
      "PERMINTE_AVYRSEXP_AVSAL     0.0002   2.99e-05      7.428      0.000       0.000       0.000\n",
      "PERSPEN_PTRATIO_PCTAF      -0.0022      0.000     -6.445      0.000      -0.003      -0.002\n",
      "const                       2.9589      1.547      1.913      0.056      -0.073       5.990\n",
      "===========================================================================================\n"
     ]
    }
   ],
   "source": [
    "glm_binom = sm.GLM(data.endog, data.exog, family=sm.families.Binomial())\n",
    "res = glm_binom.fit()\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <span style=\"color:#4CC9F0\">Cantidades de interés</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of trials: 108418.0\n"
     ]
    }
   ],
   "source": [
    "print('Total number of trials:',  data.endog.iloc[:, 0].sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters:  LOWINC                    -0.016815\n",
      "PERASIAN                   0.009925\n",
      "PERBLACK                  -0.018724\n",
      "PERHISP                   -0.014239\n",
      "PERMINTE                   0.254487\n",
      "AVYRSEXP                   0.240694\n",
      "AVSALK                     0.080409\n",
      "PERSPENK                  -1.952161\n",
      "PTRATIO                   -0.334086\n",
      "PCTAF                     -0.169022\n",
      "PCTCHRT                    0.004917\n",
      "PCTYRRND                  -0.003580\n",
      "PERMINTE_AVYRSEXP         -0.014077\n",
      "PERMINTE_AVSAL            -0.004005\n",
      "AVYRSEXP_AVSAL            -0.003906\n",
      "PERSPEN_PTRATIO            0.091714\n",
      "PERSPEN_PCTAF              0.048990\n",
      "PTRATIO_PCTAF              0.008041\n",
      "PERMINTE_AVYRSEXP_AVSAL    0.000222\n",
      "PERSPEN_PTRATIO_PCTAF     -0.002249\n",
      "const                      2.958878\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print('Parameters: ', res.params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T-values:  LOWINC                    -38.749083\n",
      "PERASIAN                   16.504736\n",
      "PERBLACK                  -25.182189\n",
      "PERHISP                   -32.817913\n",
      "PERMINTE                    8.498271\n",
      "AVYRSEXP                    4.212479\n",
      "AVSALK                      5.774998\n",
      "PERSPENK                   -6.161911\n",
      "PTRATIO                    -5.453217\n",
      "PCTAF                      -5.168654\n",
      "PCTCHRT                     3.921200\n",
      "PCTYRRND                  -15.878260\n",
      "PERMINTE_AVYRSEXP          -7.390931\n",
      "PERMINTE_AVSAL             -8.449639\n",
      "AVYRSEXP_AVSAL             -4.059162\n",
      "PERSPEN_PTRATIO             6.321099\n",
      "PERSPEN_PCTAF               6.574347\n",
      "PTRATIO_PCTAF               5.362290\n",
      "PERMINTE_AVYRSEXP_AVSAL     7.428064\n",
      "PERSPEN_PTRATIO_PCTAF      -6.445137\n",
      "const                       1.913012\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print('T-values: ', res.tvalues)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <span style=\"color:#4CC9F0\">Gráfico curva ajustada</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "nobs = res.nobs\n",
    "y = data.endog.iloc[:,0]/data.endog.sum(1)\n",
    "yhat = res.mu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "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>y</th>\n",
       "      <th>yhat</th>\n",
       "      <th>indice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>0.171642</td>\n",
       "      <td>0.097120</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>0.093750</td>\n",
       "      <td>0.117765</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>0.199820</td>\n",
       "      <td>0.134021</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>0.351351</td>\n",
       "      <td>0.141042</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>0.086957</td>\n",
       "      <td>0.148461</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>0.829736</td>\n",
       "      <td>0.834301</td>\n",
       "      <td>298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>0.928302</td>\n",
       "      <td>0.858354</td>\n",
       "      <td>299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.858500</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>0.889256</td>\n",
       "      <td>0.863858</td>\n",
       "      <td>301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.889362</td>\n",
       "      <td>0.866315</td>\n",
       "      <td>302</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>303 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            y      yhat  indice\n",
       "287  0.171642  0.097120       0\n",
       "42   0.093750  0.117765       1\n",
       "103  0.199820  0.134021       2\n",
       "282  0.351351  0.141042       3\n",
       "77   0.086957  0.148461       4\n",
       "..        ...       ...     ...\n",
       "90   0.829736  0.834301     298\n",
       "116  0.928302  0.858354     299\n",
       "148  0.600000  0.858500     300\n",
       "252  0.889256  0.863858     301\n",
       "11   0.889362  0.866315     302\n",
       "\n",
       "[303 rows x 3 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fit_data = pd.DataFrame({'y':y, 'yhat':yhat})\n",
    "fit_data = fit_data.sort_values(by= 'yhat')\n",
    "fit_data['indice'] = np.array(np.arange(fit_data.shape[0]))\n",
    "fit_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(fit_data['indice'], fit_data['yhat'], label='predichos')\n",
    "plt.scatter(fit_data['indice'], fit_data['y'], label='observados')\n",
    "plt.legend()\n",
    "plt.title('Gráfico de valores observados y predichos')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <span style=\"color:#4CC9F0\">Gráfico de valores ajustados v.s. valores observados</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.graphics.api import abline_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.scatter(yhat, y)\n",
    "line_fit = sm.OLS(y, sm.add_constant(yhat, prepend=True)).fit()\n",
    "abline_plot(model_results=line_fit, ax=ax)\n",
    "\n",
    "ax.set_title('Gráfico de ajuste del modelo')\n",
    "ax.set_ylabel('Valores observados')\n",
    "ax.set_xlabel('Valores ajustados');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <span style=\"color:#4CC9F0\">Gráfico de Residuales de Person</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.scatter(yhat, res.resid_pearson)\n",
    "ax.hlines(0, 0, 1)\n",
    "ax.set_xlim(0, 1)\n",
    "ax.set_title('Gráfico de Dependencia de Residuales')\n",
    "ax.set_ylabel(' Residuales de Pearson')\n",
    "ax.set_xlabel('Valores Ajustados')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <span style=\"color:#4CC9F0\">Histograma de residuales deviance estandarizados</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAEICAYAAABGaK+TAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAAaJklEQVR4nO3dfbwcVZ3n8c93QnhMeL5iSMDLIsoiK2E28rAwDhthJhJGwFGBVRaUNTDqDrioBHQ0jE/xNQr4Gnd1giCsPIsgLDhqxCCDAnqDAROCw1OQhEAuQoZEHDTht3+cc2Ol0327773dt+9Jvu/Xq1+3qk511a+rTv3uqVPVXYoIzMysPH/S7QDMzGx4nMDNzArlBG5mVigncDOzQjmBm5kVygnczKxQRSdwSUskHdXtOMYSSVdI+ky34xgg6QJJXx+kfJmko9uwntMl3T3S5YwmSV+T9HfdjmOsG+m+bVYHh7nMXkkhaat2LneoxmwCr3dg1+7IiHhDRNzZZDljYkOXIG+n30paK2mFpIskjRvJMiPicxHxP9oV42jK9W193h5rJT0h6RuSXteO5UfEWRHx6XYsqx3qfN6B155N3jdH0lWjFedQlVwHmxmzCbwUm+E/hoMiYgLw58BJwPu6HE+33ZO3x07A0cDvgIWSDuxuWB1zT0RMqHk93e2ghmszPD43UnQCr7bSJR0iqU/Si5KelXRRnu2u/Hd1bk0cLulPJH1C0pOSVkn6v5J2qiz3v+ey30j6u5r1zJF0o6SrJL0InJ7XfY+k1ZJWSvqKpK0rywtJH5D0iKQ1kj4taV9JP83x3jAwv6RdJN0mqV/SC3l4yiDb4GBJ9+flXg9sW1N+nKRFObafSnpjK9s2Ih4FfgJMbWVZks7LrfY1kn4l6S2V7XVVZb5TK9v24zWxbtT9I+koScsr47MlPZbX8ZCkEwfZLvtLmi/p+RzPuyplx+b3r8kxf6SF7bE+Ih6LiA8APwbmVJZ3WN4eqyU9oNytJ+kkSX01cX1Y0q21n7fZfpd0Z643P8lx/0DS7pXyIysxPCXp9Dx9G0lflPTrfFx8TdJ2zT5vg226yT6WNAO4ADhJ6fh6IM/7XklL87yPSzqzspyjJC2XdK7S8bdS0nsr5btJujUfGz8D9q2J48v5M74oaaGkP6uU1Ts+N9RBpWOzenaxTtKcXNawfkkal7fjc5IeB2bWxLRnjvl5SY9Ken+lrFFuGrmIGJMvYBlwdM2004G7680D3AOcmocnAIfl4V4ggK0q73sf8CjwH/K8NwHfzGUHAGuBI4GtgS8Cf6isZ04eP4H0D3A74D8DhwFb5fUtBc6prC+AW4AdgTcALwN35PXvBDwEnJbn3Q34a2B7YCLwLeA7DbbR1sCTwIeB8cA7cmyfyeUHA6uAQ4FxwGl5m23TYHkBvDYP7w+sBD7cbFnA64GngD0r23zfyva6qmbbvjm/7yJgXWXbXjEQex4/ClheGX8nsGfe7icBvwUm1dYNYIccz3vzPjkYeA44IJevBP4sD+8C/GmD7bFhmTXT3wc8m4cnA78Bjs1xHZPHe/I+XAPsV3nvz4GTaz9vs/0O3Ak8BryOVOfuBObmstfk9ZyS68FuwNRcdjFwK7BrXu7/Az4/lM+by1rax5X5Z5ISr0hncy8NbOe8X9cBf5/jPTaX75LLrwNuyPvxQGAFGx/378mfcSvgXOAZYNtBjs9N4svzTgX6gYNbqF9nAQ8De+VtuYBKXiE1FP8PqQE1sNzpg+WmtuTJTiXgEQeWksNaYHXl9RKNE/hdwIXA7jXL6WXTBH4H8IGayvmHXCE+CVxbKdse+D0bJ/C7msR+DnBzZTyAIyrjC4HzKuNfAi5psKypwAsNyt4MPA2oMu2n/DEpfBX4dM17fgX8eYPlBfBirrgBXEtO9oMtC3gtKbkfDYyvmWfDwZO37XWVsh1qtu0VDJLA68S7CDg+D5/OHxP4ScC/1Mz7T8Cn8vCvgTOBHZvsxw3LrJk+A/hDHj6P/M+/Uv59/vgP+Srgk3l4P1Ki3b7e5x1sv5MS9icq4x8AvpeHz6/Wt8o8yvty38q0w4EnBvm869j4mHssl7W0jwfZlt8Bzq7s19+x8TG5itQIGkc6FvevlH2u3n6olL9A6vobiOWuZvGR/sEuI/8zbaF+/Qg4q1L2F+S8Qkrq64GJlfLPA1fk4bq5qR2vsd6FckJE7DzwIlXaRs4gtU4elvRzSccNMu+epJbrgCdJO2KPXPbUQEFEvERqUVU9VR2R9Lp8yvtMPm37HLB7zXuerQz/rs74hLys7SX9k1I3w4uknb+z6l9M3BNYEbmWVD7LgNcA5+bT6tWSVpMq22AXpf40x3ISqbW9Q7NlRepuOYd0oKySdJ3qX/iq3ba/ZdNt25BS19aiyvoPZNPtPBDroTWxvht4dS7/a1Kr70lJP5Z0eKsxZJOB5yvremfNuo4EJuXya0gtY4D/RmpVv1Tns7Wy35+pDL9ErjOk/fBYnTgHzgIWVmL7Xp7eyL3VYy4i9oUNXWrn0HwfD3yet0q6N3cprCZt7+q++k1ErKvzeXpIx2L1GKvWaSR9JHfP/Fte9k41y97o+KwT23jgRuCaiLiuMn2w+rVR3a2JaU/g+YhYU1M+OQ8PJTcNyVhP4C2LiEci4hTgVcAXgBsl7UD6L1nradKBN2BvUsvjWdLpdbXvcTvS6dpGq6sZ/yrp9Gq/iNiR1CeoYX6Uc0lnBIfmZb15IJQ6864EJkuqlu1dGX4K+GzNAbl9RFw7WACR3EA69ftkK8uKiGsi4kjSdg3SPqgX714DI5K2Z+Nt+1tSwhnw6sq8rwEuBT4E7Jb/oS+m/nZ5CvhxTawTIuJvcqw/j4jjSXXlO6TT9aE4EfiXyrq+WbOuHSJibi6fD/RImkpK5Nc0WOZQ9nutp6jpJ86eIzUO3lCJbadIF2WHbJB9vNHxIGkb4Nuk7sc98r76boufpZ90LO5VmbahTuf+7o8B7yJ1uewM/FvNsusd81X/SDrT/ERluc3q10Z1l42Ps6eBXSVNrClfAYPmphHbbBK4pPdI6omIV0infgCvkCrEK6T+5gHXAh+WtI+kCaQW8/W5RXAj8FeS/ovShcU5NK94E0kVYq2k/YG/GcFHmUg66FZL2hX41CDz3kOq7H8rabyktwOHVMovBc6SdKiSHSTNrKlog5kLvF/SqwdblqTXS5qeD9x/z/G/Umd5NwLHKV1w25rUB1qtg4uAYyXtmtd5TqVs4J9xP6SLZKQWUj23Aa9TumA6Pr/eJOk/Stpa0rsl7RQRfyDtt3qxbkTpItY+kv6R1AVwYS66ilRf/jLPs63SRbopAHkd3wL+gdR3Or/BKoay32tdDRwt6V2StlK6CDg1HwuXAhdLelX+HJMl/eUQlk1+32D7+FmgV9LAvtyadI2jH1gn6a2kLoemImI96ZrUnHxWcgDpesuAiaQ63w9sJemTpGtLrX6OM0ndfu/O22dAs/p1A+k4myJpF2B2JeanSF2Xn8/7/42kVvfAhdNGuWnENpsETuqXXCJpLfBlUt/W7/Lp6meBn+RTo8OAy4Fvkk5TnyBVyP8JEBFL8vB1pP+6a0n9cy8Psu6PkE6P15AOmOtH8DkuIV14eQ64l3TKW1dE/B54O6nv8nlSt8dNlfI+4P3AV0j9hI/meVsSEb8kbaOPNlnWNqRk/xzpNP9VpH7Z2uUtAT5IaoWuzMtZXpnlm8ADpL7JH1DZjhHxEOlawT2khPGfSHfJ1It7DSlhnExqHT1Davlsk2c5FViWuyrOInWvNHJ4rlMvkvqhdwTelLfNwMF7POmsq5/UGv4oGx9b15D6jr9V021QdQkt7vc6n/fXpC6Kc0n1YBFwUC4+j7Sv7s2f94eklv6gn7fm9SYG38ffyn9/I+n+vP3/lpT0XiAdG7e2+nlIreAJeT1XAN+olH2ftG3+ldRN8e806TKpcQqpMfd05fNd0EL9ujSv+wHgfirHWWW5vaT6djPpessPc1nd3DSEmBvSxt2nViu30FeTukee6HI4ZmYbbE4t8LaR9Ff59G0HUj/eL0mtQjOzMcMJvL7jSadCT5Nu/To5fKpiZmOMu1DMzArlFriZWaFG9Ydedt999+jt7R3NVZqZFW/hwoXPRcQmX8Aa1QTe29tLX19f8xnNzGwDSU/Wm+4uFDOzQjmBm5kVygnczKxQTuBmZoVyAjczK5QTuJlZoZzAzcwK5QRuZlYoJ3Azs0KN6jcxbcvTO/v2Ic2/bO7MDkVitvlxC9zMrFAtJ/D8vL9fSLotj+8j6T5Jj0q6Pj/j0MzMRslQWuBnA0sr418ALo6I15Kee3dGOwMzM7PBtZTA8xO2ZwJfz+MCppOeMg5wJXBCB+IzM7MGWm2BXwJ8DHglj+8GrK48YXs5MLneGyXNktQnqa+/v38ksZqZWUXTBC7pOGBVRCwczgoiYl5ETIuIaT09m/weuZmZDVMrtxEeAbxN0rHAtsCOwJeBnSVtlVvhU4AVnQvTzMxqNW2BR8T5ETElInqBk4EfRcS7gQXAO/JspwG3dCxKMzPbxEjuAz8P+F+SHiX1iV/WnpDMzKwVQ/omZkTcCdyZhx8HDml/SGZm1gp/E9PMrFBO4GZmhXICNzMrlBO4mVmhnMDNzArlBG5mVigncDOzQjmBm5kVygnczKxQTuBmZoVyAjczK5QTuJlZoZzAzcwK5QRuZlYoJ3Azs0I5gZuZFaqVhxpvK+lnkh6QtETShXn6FZKekLQov6Z2PFozM9uglSfyvAxMj4i1ksYDd0v651z20Yi4sXPhmZlZI00TeEQEsDaPjs+v6GRQZmbWXEt94JLGSVoErALmR8R9ueizkh6UdLGkbRq8d5akPkl9/f397YnazMxaS+ARsT4ipgJTgEMkHQicD+wPvAnYlfSU+nrvnRcR0yJiWk9PT3uiNjOzod2FEhGrgQXAjIhYGcnLwDfwE+rNzEZVK3eh9EjaOQ9vBxwDPCxpUp4m4ARgcefCNDOzWq3chTIJuFLSOFLCvyEibpP0I0k9gIBFwFmdC9PMzGq1chfKg8DBdaZP70hEZmbWEn8T08ysUE7gZmaFcgI3MyuUE7iZWaFauQvFbIPe2bd3OwQzy9wCNzMrlBO4mVmhnMDNzArlBG5mVigncDOzQjmBm5kVygnczKxQTuBmZoVyAjczK5S/ibkF87cqzcrmFriZWaFaeaTatpJ+JukBSUskXZin7yPpPkmPSrpe0tadD9fMzAa00gJ/GZgeEQcBU4EZkg4DvgBcHBGvBV4AzuhYlGZmtommCTw/eX5tHh2fXwFMB27M068kPdjYzMxGSUt94JLGSVoErALmA48BqyNiXZ5lOTC5wXtnSeqT1Nff39+GkM3MDFpM4BGxPiKmAlOAQ4D9W11BRMyLiGkRMa2np2d4UZqZ2SaGdBdKRKwGFgCHAztLGrgNcQqwor2hmZnZYFq5C6VH0s55eDvgGGApKZG/I892GnBLh2I0M7M6WvkizyTgSknjSAn/hoi4TdJDwHWSPgP8Arisg3GamVmNpgk8Ih4EDq4z/XFSf7iZmXWBv4lpZlYoJ3Azs0I5gZuZFcoJ3MysUP45WRtThvoTt8vmzuxQJGZjn1vgZmaFcgI3MyuUE7iZWaGcwM3MCuWLmJsRP+PSbMviFriZWaGcwM3MCuUEbmZWKCdwM7NC+SKmFc3f3LQtmVvgZmaFauWRantJWiDpIUlLJJ2dp8+RtELSovw6tvPhmpnZgFa6UNYB50bE/ZImAgslzc9lF0fEFzsXnpmZNdLKI9VWAivz8BpJS4HJnQ7MzMwGN6Q+cEm9pOdj3pcnfUjSg5Iul7RLu4MzM7PGWk7gkiYA3wbOiYgXga8C+wJTSS30LzV43yxJfZL6+vv7Rx6xmZkBLSZwSeNJyfvqiLgJICKejYj1EfEKcCkNnlAfEfMiYlpETOvp6WlX3GZmW7xW7kIRcBmwNCIuqkyfVJntRGBx+8MzM7NGWrkL5QjgVOCXkhblaRcAp0iaCgSwDDizA/GZmVkDrdyFcjegOkXfbX84ZmbWKn8T08ysUE7gZmaFcgI3MyuUE7iZWaGcwM3MCuXfA7ctin8/3DYnboGbmRXKCdzMrFBO4GZmhXICNzMrlBO4mVmhnMDNzArlBG5mVigncDOzQjmBm5kVygnczKxQTuBmZoVq5ZmYe0laIOkhSUsknZ2n7yppvqRH8t9dOh+umZkNaKUFvg44NyIOAA4DPijpAGA2cEdE7AfckcfNzGyUNE3gEbEyIu7Pw2uApcBk4HjgyjzblcAJHYrRzMzqGNLPyUrqBQ4G7gP2iIiVuegZYI8G75kFzALYe++9hx3olmioP31qZluWli9iSpoAfBs4JyJerJZFRABR730RMS8ipkXEtJ6enhEFa2Zmf9RSApc0npS8r46Im/LkZyVNyuWTgFWdCdHMzOpp5S4UAZcBSyPiokrRrcBpefg04Jb2h2dmZo200gd+BHAq8EtJi/K0C4C5wA2SzgCeBN7VkQjNzKyupgk8Iu4G1KD4Le0Nx8zMWuVvYpqZFcoJ3MysUE7gZmaFcgI3MyuUE7iZWaGcwM3MCuUEbmZWKCdwM7NCOYGbmRVqSD8na7alGc5P+i6bO7MDkZhtyi1wM7NCOYGbmRXKCdzMrFBO4GZmhXICNzMrlBO4mVmhWnmk2uWSVklaXJk2R9IKSYvy69jOhmlmZrVaaYFfAcyoM/3iiJiaX99tb1hmZtZM0wQeEXcBz49CLGZmNgQj6QP/kKQHcxfLLo1mkjRLUp+kvv7+/hGszszMqoabwL8K7AtMBVYCX2o0Y0TMi4hpETGtp6dnmKszM7Naw0rgEfFsRKyPiFeAS4FD2huWmZk1M6wELmlSZfREYHGjec3MrDOa/hqhpGuBo4DdJS0HPgUcJWkqEMAy4MzOhWhmZvU0TeARcUqdyZd1IBYzMxsCfxPTzKxQTuBmZoVyAjczK5QTuJlZoZzAzcwK5QRuZlYoJ3Azs0I5gZuZFcoJ3MysUE7gZmaFcgI3MyuUE7iZWaGcwM3MCuUEbmZWqKY/J2uN9c6+vdsh2Bg01HqxbO7MDkVimzu3wM3MCtU0geenzq+StLgybVdJ8yU9kv82fCq9mZl1Rist8CuAGTXTZgN3RMR+wB153MzMRlHTBB4RdwHP10w+HrgyD18JnNDesMzMrJnh9oHvEREr8/AzwB6NZpQ0S1KfpL7+/v5hrs7MzGqN+CJmRATp6fSNyudFxLSImNbT0zPS1ZmZWTbcBP6spEkA+e+q9oVkZmatGG4CvxU4LQ+fBtzSnnDMzKxVrdxGeC1wD/B6ScslnQHMBY6R9AhwdB43M7NR1PSbmBFxSoOit7Q5FjMzGwJ/E9PMrFBO4GZmhXICNzMrlBO4mVmhnMDNzArlBG5mVigncDOzQjmBm5kVygnczKxQTuBmZoVyAjczK5QTuJlZoZzAzcwK5QRuZlYoJ3Azs0I5gZuZFarpAx0GI2kZsAZYD6yLiGntCMrMzJobUQLP/mtEPNeG5ZiZ2RC4C8XMrFAjTeAB/EDSQkmz2hGQmZm1ZqRdKEdGxApJrwLmS3o4Iu6qzpAT+yyAvffee4SrMzOzASNqgUfEivx3FXAzcEideeZFxLSImNbT0zOS1ZmZWcWwE7ikHSRNHBgG/gJY3K7AzMxscCPpQtkDuFnSwHKuiYjvtSUqMzNratgJPCIeBw5qYyxmZjYE7bgPfLPRO/v2bodgW6Ch1rtlc2d2KBIrje8DNzMrlBO4mVmhnMDNzArlBG5mVqhiLmL6Qo/Z8PjY2Xy5BW5mVigncDOzQjmBm5kVygnczKxQxVzEHCp/q9I2V2Otbg8nHl8obQ+3wM3MCuUEbmZWKCdwM7NCOYGbmRVqs72IaWbDMxoXSTv97dCxtvzhrKMVboGbmRVqRAlc0gxJv5L0qKTZ7QrKzMyaG8lDjccB/xt4K3AAcIqkA9oVmJmZDW4kLfBDgEcj4vGI+D1wHXB8e8IyM7NmRnIRczLwVGV8OXBo7UySZgGz8uhaSb8awTo7aXfguW4HMUSOeXSUFnNp8UKTmPWFzq58mMsf0nYe4Wd4Tb2JHb8LJSLmAfM6vZ6RktQXEdO6HcdQOObRUVrMpcULjnm4RtKFsgLYqzI+JU8zM7NRMJIE/nNgP0n7SNoaOBm4tT1hmZlZM8PuQomIdZI+BHwfGAdcHhFL2hbZ6Bvz3Tx1OObRUVrMpcULjnlYFBHdjsHMzIbB38Q0MyuUE7iZWaGcwCsk/YOkhyU9KOlmSTt3O6ZmJL1T0hJJr0gas7dhlfazC5Iul7RK0uJux9IqSXtJWiDpoVwnzu52TM1I2lbSzyQ9kGO+sNsxtULSOEm/kHRbN+NwAt/YfODAiHgj8K/A+V2OpxWLgbcDd3U7kEYK/dmFK4AZ3Q5iiNYB50bEAcBhwAcL2M4vA9Mj4iBgKjBD0mHdDaklZwNLux2EE3hFRPwgItbl0XtJ97aPaRGxNCLG6rdbBxT3swsRcRfwfLfjGIqIWBkR9+fhNaQEM7m7UQ0ukrV5dHx+jek7KyRNAWYCX+92LE7gjb0P+OduB7GZqPezC2M6sZROUi9wMHBfl0NpKndHLAJWAfMjYqzHfAnwMeCVLsex5T3QQdIPgVfXKfp4RNyS5/k46XT06tGMrZFWYjYbIGkC8G3gnIh4sdvxNBMR64Gp+ZrTzZIOjIgxee1B0nHAqohYKOmoLoez5SXwiDh6sHJJpwPHAW+JMXKTfLOYC+CfXRglksaTkvfVEXFTt+MZiohYLWkB6drDmEzgwBHA2yQdC2wL7Cjpqoh4TzeCcRdKhaQZpFOjt0XES92OZzPin10YBZIEXAYsjYiLuh1PKyT1DNztJWk74Bjg4a4GNYiIOD8ipkREL6ke/6hbyRucwGt9BZgIzJe0SNLXuh1QM5JOlLQcOBy4XdL3ux1TrXxheOBnF5YCN4z1n12QdC1wD/B6ScslndHtmFpwBHAqMD3X30W5pTiWTQIWSHqQ9I9+fkR09da8kvir9GZmhXIL3MysUE7gZmaFcgI3MyuUE7iZWaGcwM3MCuUEbmZWKCdwM7NC/X8ILe9igwcZAwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "resid = res.resid_deviance.copy()\n",
    "resid_std = stats.zscore(resid)\n",
    "ax.hist(resid_std, bins=25)\n",
    "ax.set_title('Histograma de Residuales Deviance Estandarizados');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <span style=\"color:#4CC9F0\">qq plot de residuales deviance</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels import graphics\n",
    "fig = graphics.gofplots.qqplot(resid, line='r')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}