{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Regresión lineal" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Abordemos las primeras ideas de regresión lineal a través de un ejemplo práctico:\n", "\n", "- Creamos dos variables, Ingreso y Consumo Esperado" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Y X\n", "0 55 80\n", "1 60 80\n", "2 65 80\n", "3 70 80\n", "4 75 80" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import random\n", "import statsmodels.formula.api as smf\n", "from statsmodels.graphics.regressionplots import abline_plot\n", "\n", "\n", "df = pd.DataFrame({\n", " 'Col1': [1,2,3],\n", " 'Col2': [4,5,6]\n", " })\n", "\n", "\n", "familia = pd.DataFrame({'Y':[55,60,65,70,75,\n", " 65,70,74,80,85,88,\n", " 79,84,90,94,98,\n", " 80,93,95,103,108,113,115,\n", " 102,107,110,116,118,125,\n", " 110,115,120,130,135,140,\n", " 120,136,140,144,145,\n", " 135,137,140,152,157,160,162,\n", " 137,145,155,165,175,189,\n", " 150,152,175,178,180,185,191\n", " ],'X':[80,80,80,80,80,\n", " 100,100,100,100,100,100,\n", " 120,120,120,120,120,\n", " 140,140,140,140,140,140,140,\n", " 160,160,160,160,160,160,\n", " 180,180,180,180,180,180,\n", " 200,200,200,200,200,\n", " 220,220,220,220,220,220,220,\n", " 240,240,240,240,240,240,\n", " 260,260,260,260,260,260,260\n", " ]})\n", "familia.head()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['Y', 'X'], dtype='object')" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ingresos = np.arange(80,261,20)\n", "ingresos\n", "consumoEsperado = [65,77,89,101,113,125,137,149,161,173]\n", "consumoEsperado\n", "\n", "\n", "familia.columns" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure() # llama al dispositivo grafico\n", "plt.plot(ingresos,consumoEsperado)\n", "plt.scatter(familia['X'],familia['Y'])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "¿Qué hemos hecho?\n", "\n", "$$ E(Y|X_i) = f(X_i)$$\n", "\n", "$$E(Y|X_i) = \\beta_1+\\beta_2X_i$$\n", "\n", "$$ u_i = Y_i - E(Y|X_i) $$\n", "\n", "$$ Y_i = E(Y|X_i) + u_i$$\n", "\n", "\n", "¿Qué significa que sea lineal?\n", "\n", "El término regresión lineal siempre significará una regresión lineal en los parámetros; los $\\beta$ (es decir, los parámetros) se elevan sólo a la primera potencia. Puede o no ser lineal en las variables explicativas $X$.\n", "\n", "Para evidenciar la factibilidad del uso de RL en este caso, vamos a obtener una muestra de la población:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,\n", " 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n", " 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n", " 51, 52, 53, 54, 55, 56, 57, 58, 59])" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nS = familia.shape\n", "type(nS)\n", "indice = np.arange(0,nS[0])\n", "indice # Creamos una variable indicadora para obtener una muestra" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[54, 59, 33, 25, 32, 39, 47, 51, 18, 3, 34, 12, 29, 7, 26, 5, 56, 50, 44, 13]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "random.seed(8519)\n", "muestra = random.sample(list(indice),k = 20) # cambio de array a lista\n", "muestra # samos sample para obtener una muestra sin reemplazo del tamaño indicado" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "ingreso_muestra = familia.loc[muestra,'X']\n", "consumo_muestra = familia.loc[muestra,'Y']" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: consumo_muestra R-squared: 0.909\n", "Model: OLS Adj. R-squared: 0.904\n", "Method: Least Squares F-statistic: 179.4\n", "Date: Wed, 18 Sep 2024 Prob (F-statistic): 8.44e-11\n", "Time: 05:32:16 Log-Likelihood: -76.677\n", "No. Observations: 20 AIC: 157.4\n", "Df Residuals: 18 BIC: 159.3\n", "Df Model: 1 \n", "Covariance Type: nonrobust \n", "===================================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "-----------------------------------------------------------------------------------\n", "Intercept 13.2650 8.817 1.504 0.150 -5.259 31.789\n", "ingreso_muestra 0.6226 0.046 13.394 0.000 0.525 0.720\n", "==============================================================================\n", "Omnibus: 3.437 Durbin-Watson: 2.369\n", "Prob(Omnibus): 0.179 Jarque-Bera (JB): 2.288\n", "Skew: -0.828 Prob(JB): 0.319\n", "Kurtosis: 2.997 Cond. No. 634.\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "df = pd.DataFrame(list(zip(consumo_muestra,ingreso_muestra)),columns = ['consumo_muestra','ingreso_muestra'])\n", "ajuste_1 = smf.ols('consumo_muestra~ingreso_muestra',data =df).fit()\n", "\n", "print(ajuste_1.summary())" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Cellphone')" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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di/bt22PgwIFuOisiInK1jJw8TE/LQs26Sb62DNPTspA6sbNzA09FBWBpeER+PsArDW7n1lvPd+7ciX79+pm1T548GWvWrAEArFq1CosWLcKlS5fQtm1bpKSkYMSIEcZ9y8rKMG/ePHzyyScoLS3FgAED8O6779bpDitbb10jIiL50ekFei3eblLRqU4CoFYFYu/8/vD1ccJkfY88Aqxfb9rWqROQleX49yITtv79ls08O+7EsENE5Ln2n7uGcSsO1Lrfp1MfRI9WoY574+JiwNLfjOJi4K67HPc+ZJXHz7NDRERki4JiyxUde/ezSYcO5kHn0UcNA5AZdGRHtgOUiYiIbBEeHFj7TnXY747y84FIC2N/KioAf//6H5+cgpUdIiLyaN1iQhCpCoS10TgSDHdldYsJqd8bBQSYB5158wzVHAYdWWPYISIij+brIyE5wTAnW83AU/U8OSHW/sHJp04Z5s2pqDBt1+sBTnPiERh2iIjI48XHRSJ1YmeoVaaXqtSqwPrddi5JwL33mra9885vEweSR+CYHSIiUoT4uEgMilXjYO51FBSXITzYcOnKrorO/v1Az57m7byB2SMx7BARkWL4+kj1v73cUsXmiy8M8+mQR2LYISIiAoBNm4Bqk9YasZrj8Rh2iIiILFVzdu0Cevd2fV8URKcXjrmsWE8MO0RE5L3efx94+mnzdlZz6k0WC7P+D+/GIiIi71N1N1XNoHP8OIOOA1QtzFpzvbKqhVkzcvJc2h+GHSIi8i4vvQT4WPjzJwQQG+v6/iiMTi+Qkn7CbAV6AMa2lPQT0OldFyp5GYuIiLzD7duWZzq+dAlo3tz1/VGog7nXra5ADxgCT562DAdzrzt2YdY7YGWHiIiUb9Ik86DTpo2hmsOg41BuWZi1FqzsEBGRct28aXkV8sJCoEkTl3fHG7h0YVYbsbJDRETK1LOnedAZOtRQzWHQcRqXLcxaBww7RESkLFevGu602r/ftL2sDNi82T198iJOX5jVDgw7RESkHKGhQHi4aduzzxqqOQEB7umTF3Lawqx24pgdIiLyfD/9BLRqZd6u01m+zZyczqELs9YTww4REXk2S0s9LFkCzJvn+r6QCYcszOoADDtEROSZMjOBrl3N2zkDMtXA2h4REXkeSTIPOmlpDDpkESs7RETkOb75BoiPN29nyKE7YNghIiLPYGlsztatwMCBru8LeRRexiIiInn78EPLQUcIBh2yCSs7REQkT0JYvm38yBHg/vtd3h3yXKzsEBGR/CxcaDnoCMGgQ3XGyg4REUGnF7KY/A06HeBn4U9Tbi5w990u7w4pA8MOEZGXy8jJQ0r6CeRpy4xtkapAJCfEunZa/6efBt5/37QtMhK4csV1fSBFYtghIvJiGTl5mJ6WhZo3budryzA9Lcs16xiVlQFBQebtv/5qWOuKqJ44ZoeIyEvp9AIp6SfMgg4AY1tK+gno9E6cw2bQIPOg06ePYWwOgw45CCs7RERe6mDudZNLVzUJAHnaMhzMve749Y0KC4GQEPP2W7csV3mI6oGVHSIiL1VQbD3o2LOfzaKjzYPOE08YqjkMOuQErOwQEXmp8OBAh+5Xq4sXgZYtzdtv3wZ8fR3zHkQWsLJDROSlusWEIFIVCGs3mEsw3JXVLcbC5aa6kiTzoPPKK4ZqDoMOORnDDhGRl/L1kZCcEAsAZoGn6nlyQmz95ts5dszyUg96PZCcbP9xieqAYYeIyIvFx0UidWJnqFWml6rUqsD633YuSUCHDqZt//qXoZpjKQAROQnH7BARebn4uEgMilU7bgblHTuA/v3N24UTb2EnugOGHSIigq+P5Jjbyy1VbL76Cnj44fofm8hOvIxFRET19+9/Ww46QjDokNuxskNEVA+yWUDTnSyFnB9+ALp1c31fiCxwa2Vn9+7dSEhIgEajgSRJ2Lhxo9V9p02bBkmS8NZbb5m0l5eXY+bMmQgLC0OjRo0wfPhwXLp0ybkdJyKCYV2pXou3Y9yKA3huXTbGrTiAXou3IyMnz91dc40337RezWHQIRlxa9i5efMmOnbsiOXLl99xv40bN+KHH36ARqMx25aYmIgNGzZg3bp12Lt3L0pKSjBs2DDodDpndZuIyLiAZs3lFqoW0FR04NHrDSFn9mzT9jNnOAiZZMmtYWfIkCF49dVXMWrUKKv7XL58GTNmzMDHH38Mf39/k21arRYrV67EG2+8gYEDB6JTp05IS0vDsWPHsG3bNmd3n4i8lCwW0HSX5583nwTwrrsMIeeee9zTJ6JayHqAsl6vx2OPPYZ58+ahXbt2ZtszMzNRWVmJwYMHG9s0Gg3i4uKwb98+q8ctLy9HUVGRyYOIyFZ1WUBTMSoqDNWcGkMJ8MsvQHGxW7pEZCtZh53FixfDz88Ps2bNsrg9Pz8fDRo0QNOmTU3aIyIikJ+fb/W4ixYtgkqlMj6ioqIc2m8iUja3LaDpLn/8IxAQYNrWtauhmhMe7p4+EdWBbO/GyszMxNtvv42srCxIdZxpUwhxx9ckJSVhdrVrzUVFRQw8RGQzly+g6S5FRYBKZd5eXGy4dEXkIeyu7Jw7dw4vvfQSxo0bh4KCAgBARkYGjh8/7pCO7dmzBwUFBWjZsiX8/Pzg5+eH8+fPY86cObj77rsBAGq1GhUVFSgsLDR5bUFBASIiIqweOyAgAI0bNzZ5EBHZyqULaLpLXJx50BkzxlDNYdAhD2NX2Nm1axfat2+PH374AevXr0dJSQkA4OjRo0h20MJujz32GI4ePYrs7GzjQ6PRYN68efjmm28AAF26dIG/vz+2bt1qfF1eXh5ycnLQs2dPh/SDiKgmlyyg6S55eYaxOTX/4VpRAaxb554+eSGdXmD/uWv4Mvsy9p+7pszB7i5k12WsF198Ea+++ipmz56N4OBgY3u/fv3w9ttv23yckpISnD171vg8NzcX2dnZCAkJQcuWLREaajp1ub+/P9RqNdq2bQsAUKlUmDJlCubMmYPQ0FCEhIRg7ty5aN++PQYOHGjPqRER2aRqAc2U9BMmg5XVqkAkJ8TWbwFNd/HzA2pO2zF/PvD66+7pj5fKyMkz+7mK9OSfKxmwK+wcO3YMn3zyiVl7s2bNcO3aNZuPc/jwYfTr18/4vGoczeTJk7FmzRqbjvHmm2/Cz88Po0ePRmlpKQYMGIA1a9bAt+atkUREDubwBTTd5b//Be67z7y9aj4dcpmq+Ztq1nGq5m+q90r0XsqusNOkSRPk5eUhJibGpP3IkSNo3ry5zcfp27cvRB0moPr555/N2gIDA7Fs2TIsW7bM5uMQETmKwxbQdBdLYWb5cuDZZ13fFy9X2/xNEgzzNw2KVXteoHYzu8bsjB8/HvPnz0d+fj4kSYJer8f333+PuXPnYtKkSY7uIxEROdr331tf6oFBxy28cv4mF7Er7Lz22mto2bIlmjdvjpKSEsTGxqJ3797o2bMnXnrpJUf3kYhItjxyIKkkAb16mbb95z9c6sHNvG7+Jhey6zKWv78/Pv74Y/z1r3/FkSNHoNfr0alTJ7Ru3drR/SMiki2PG0i6caNhgsCaGHJkwWvmb3KDek0q2KpVK7Rq1cpRfSEi8hgeN5DU0iWrPXvMKzzkNlXzN+VryyyO25FguNvPo+dvchO7wo5Op8OaNWvw3XffoaCgAHq93mT79u3bHdI5IiI58qiBpKmpwDPPmLezmiM7VfM3TU/LggSY/Hx5/PxNbmZX2HnuueewZs0aPPzww4iLi6vzcg5ERJ6sLgNJ3XanlhCAj4VhmcePA7Gxru8P2USR8zfJgF1hZ926dfjss88wdOhQR/eHiEj2ZD+Q9M9/BhYtMm9nNccjKGb+JhmxK+w0aNAA99xzj6P7QkTkEWQ7kPT2bcDf37z98mVAo3FtX6hePH7+Jpmx69bzOXPm4O23367ThIBEREohy4VAJ0wwDzpt2xqqOQw65OXsquzs3bsXO3bswNdff4127drBv8YXbP369Q7pHBGRHMlqIOnNm5ZXIb9xw3zVciIvZVdlp0mTJvjjH/+IPn36ICwsDCqVyuRBRKR0VQNJ1SrTS1VqVaDrbjt/8EHzoDNsmKGaw9/FREaS4LUoFBUVQaVSQavVonHjxu7uDhF5EJ1euH4gaUEBEBFh3l5WBgQEOPe9iWTE1r/f9ZpU8OrVqzh16hQkSUKbNm3QrFmz+hyOiMjjuHwgaZMmgFZr2jZzJvDOO67rA5GHsSvs3Lx5EzNnzsRHH31knFDQ19cXkyZNwrJly9CwYUOHdpKIyOudOwdYugtWp7M8nw4RGdn1DZk9ezZ27dqF9PR03LhxAzdu3MCXX36JXbt2Yc6cOY7uIxGRbLlkIVBJMg86f/+79YkDiciEXWN2wsLC8MUXX6Bv374m7Tt27MDo0aNx9epVR/XPJThmh4js4fSFQA8fBh54wLydQy2JANj+99uufxLcunULERYGx4WHh+PWrVv2HJKIyKNULQRac9mIqoVAM3Ly6vcGkmQedD75hEGHyA52hZ0ePXogOTkZZWW/fclLS0uRkpKCHj16OKxzRERyVNtCoIBhIVC7Lml9/bXlFcqFAMaNq/vxiMi+Acpvv/024uPj0aJFC3Ts2BGSJCE7OxuBgYH45ptvHN1HIiJZcdpCoJZCznffAf37172TRGRkV9iJi4vDmTNnkJaWhv/+978QQmDs2LGYMGECgoKCHN1HIiJZcfhCoKtXA08+ad7OS1ZEDmH3PDtBQUGYOnWqI/tCROQRHLYQqLW7qbKzgY4d694xIrLI7rBz+vRp7Ny5EwUFBca5dqq8/PLL9e4YEZFcVS0Emq8tszhuR4Jh2Yg7LgT66qvAggXm7azmEDmcXWFnxYoVmD59OsLCwqBWqyFVu84sSRLDDhEpWr0WAtXpAD8Lv3p//hmIjnZ8Z4nIvnl2oqOj8cwzz2D+/PnO6JPLcZ4dIrJHnefZmToV+Ne/TNtatAAuXnRyT4mUyalrYxUWFuLRRx+1u3NEREoQHxeJQbHq2hcCLS0FLC2jc+0aEHKHS11E5BB2zbPz6KOP4ttvv3V0X4iIPE7VQqAj7m+OHq1CzYPOgAHmQad/f8PYHAYdIpewubLzTrUVde+55x4sWLAABw4cQPv27eHv72+y76xZsxzXQyIiT3T9OhBqYY6dW7cATtFB5FI2j9mJiYmx7YCShJ9++qlenXI1jtkhIodq2dJ8HM5TTwErVrinP0QK5fAxO7m5uQ7pGBGRYl24YPmOqtu3AV9f1/eHiADYOWanOiEE7Lihi4hIWSTJPOj87W+GsTkMOkRuZXfYWblyJeLi4hAYGIjAwEDExcXhXzVvqSQiUrovvrC8ppVeD7z0kuv7Q0Rm7Lr1fMGCBXjzzTcxc+ZM4yrn+/fvx/PPP4+ff/4Zr776qkM7SUQkS5ZCzqpVwBNPuL4vRGSVXZMKhoWFYdmyZRg3bpxJ+6effoqZM2fi119/dVgHXYEDlImoTpYsASxNqspL+kQuZevfb7suY+l0OnTt2tWsvUuXLrh9+7Y9hyQi8gySZB50/vIXBh0iGbMr7EycOBGpqalm7R988AEmTJhQ704REcnO449bvmwlhGFRTyKSLbtXPV+5ciW+/fZbPPjggwCAAwcO4OLFi5g0aRJmz55t3G/p0qX17yURkTtZCjlr1gCTJ7u8K0RUd3aFnZycHHTu3BkAcO7cOQBAs2bN0KxZM+Tk5Bj3kyz9giAi8hSdOgHZ2ebtvGRF5FHsCjs7duxwdD+IiORDpwP8LPx63LkT6NPH5d0hovqx+zIWEZEiWatIs5pD5LFsDjujRo2y+aDr16+3qzNERG5z8yZw113m7adOAW3auL4/ROQwNocdlUrlzH4QEbkPqzlEimZz2Fm9erUz+0FE5HpXrgDNm5u3X70KhIW5vj9E5BT1Xgi0Pnbv3o2EhARoNBpIkoSNGzcat1VWVmL+/Plo3749GjVqBI1Gg0mTJuHKlSsmxygvL8fMmTMRFhaGRo0aYfjw4bh06ZKLz4SIPI4kWQ46QjDoECmMzZWdTp062XwreVZWlk373bx5Ex07dsQTTzyBRx55xGTbrVu3kJWVhQULFqBjx44oLCxEYmIihg8fjsOHDxv3S0xMRHp6OtatW4fQ0FDMmTMHw4YNQ2ZmJny50jAR1fTjj8D995u3l5YCgYEu7w4ROZ/NYWfkyJEOf/MhQ4ZgyJAhFrepVCps3brVpG3ZsmXo1q0bLly4gJYtW0Kr1WLlypVYu3YtBg4cCABIS0tDVFQUtm3bhoceesjiscvLy1FeXm58XlRU5KAzIiJZs/QPNpUKuHHD5V0hItexOewkJyc7sx820Wq1kCQJTZo0AQBkZmaisrISgwcPNu6j0WgQFxeHffv2WQ07ixYtQkpKiiu6TERysGUL8PDD5u16vfXByUSkGHaP2blx4wb+9a9/ISkpCdevXwdguHx1+fJlh3WuurKyMrz44osYP368cWXT/Px8NGjQAE2bNjXZNyIiAvn5+VaPlZSUBK1Wa3xcvHjRKX0mIhmQJPOg07evYWwOgw6RV7BrUsGjR49i4MCBUKlU+PnnnzF16lSEhIRgw4YNOH/+PD766COHdrKyshJjx46FXq/Hu+++W+v+Qog7ji8KCAhAQECAI7tIRHLzz38CM2aYt/N2ciKvY1dlZ/bs2Xj88cdx5swZBFYb0DdkyBDs3r3bYZ0DDEFn9OjRyM3NxdatW41VHQBQq9WoqKhAYWGhyWsKCgoQERHh0H4QkQeRJPOgM2sWgw6Rl7Ir7Bw6dAjTpk0za2/evPkdLx/VVVXQOXPmDLZt24bQ0FCT7V26dIG/v7/JQOa8vDzk5OSgZ8+eDusHEXmIWbMsX5oSAnj7bdf3h4hkwa7LWIGBgRbvYDp16hSaNWtm83FKSkpw9uxZ4/Pc3FxkZ2cjJCQEGo0Gf/rTn5CVlYWvvvoKOp3OGKRCQkLQoEEDqFQqTJkyBXPmzEFoaChCQkIwd+5ctG/f3nh3FhF5CUsh5913genTXd8XIpIVSYi613X/7//+D1evXsVnn32GkJAQHD16FL6+vhg5ciR69+6Nt956y6bj7Ny5E/369TNrnzx5Ml555RXExMRYfN2OHTvQt29fAIaBy/PmzcMnn3yC0tJSDBgwAO+++y6ioqJsPp+ioiKoVCpotVqTy2RE5Dw6vcDB3OsoKC5DeHAgusWEwNfHjgHDffsCu3aZt/OSFZHi2fr3266wU1RUhKFDh+L48eMoLi6GRqNBfn4+HnzwQXz99ddo1KhRvTrvagw7RK6VkZOHlPQTyNOWGdsiVYFITohFfFykbQcRAvCxcCV+yxbAyvxdRKQsTg07VbZv346srCzo9Xp07tzZYy8dMewQuU5GTh6mp2Wh5i+eqppO6sTOtQee4GCgpMS8ndUcIq9i69/vOg1Q3r59O2JjY43jdfr374+5c+fihRdewAMPPIB27dphz5499es5ESmWTi+Qkn7CLOgAMLalpJ+ATm8ltJSVGcbm1Aw6P/7IoENEVtUp7Lz11luYOnWqxfSkUqkwbdo0LF261GGdIyJlOZh73eTSVU0CQJ62DAdzr5tvlCQgKMjCiwTQoYPjOklEilOnsPPjjz8iPj7e6vbBgwcjMzOz3p0iImUqKLYedKzud/Wq5TutrlxhNYeIbFKnW89/+eUX+Pv7Wz+Ynx+uXr1a704RkTKFB9u2qrhxP2szoTPkEFEd1Kmy07x5cxw7dszq9qNHjyIy0sY7KYjI63SLCUGkKhDWbjCXYLgrq1uFlWpOSQmDDhHVWZ3CztChQ/Hyyy+jrMy8FF1aWork5GQMGzbMYZ0jImXx9ZGQnBALAGaBp+r5/j8PhG/sfeYvFgLwsGktiEge6nTr+S+//ILOnTvD19cXM2bMQNu2bSFJEk6ePIl//vOf0Ol0yMrK8rh1qXjrOZFrWZpnZ+i1/+Ldf8013/n2bcDX14W9IyJP4bR5ds6fP4/p06fjm2++QdVLJUnCQw89hHfffRd33313vTruDgw7RK5XfQblEZ1amO/QuTPAGx6I6A5s/ftd57WxoqOjsWXLFhQWFuLs2bMQQqB169Zo2rRpvTpMRN7F10dCjz3pwBNPmG/kuBwiciC7FgIFgKZNm+KBBx5wZF+IyJtYGoD8xBPAqlWu7wsRKVqdBigTEdXbSy9ZDjpCMOgQkVPYXdkhIqozSyFn4UIgKcn1fSEir8GwQ0TON3Ik8OWX5u0cm0NELsCwQ0TOIwTgY+Fq+X/+A93IP+LguWsoKC5DeHAgusWEwNfH2nSD8lX9rjJPPg8iJWPYISLnaNkSuHjRvF0Iwzw7i7ebzLMTqQpEckIs4uM8ZxZ2S/MFeeJ5ECkdBygTkWNVVhrG5tQMOgcPGoPO9LQss9XP87VlmJ6WhYycPBd21n5KOQ8ib8CwQ0SOI0lAgwbm7UIADzwAnV4gJf0ELI3UqWpLST8BnV7eY3mUch5E3oJhh4jq78YNy3da/fyzySDkg7nXzSoh1QkAedoyHMy97vAuOpJSzoPIW3DMDhHVj6WQA1i806qg2HpAsGc/d1HKeRB5C1Z2iMg+ubmWg86NG1ZvKQ8PDrTp0Lbu5y5KOQ8ib8HKDhHVXR2qOdV1iwlBpCoQ+doyi+NdJABqleH2bTlTynkQeQtWdojIdgcOWA46lZU2TRDo6yMhOSEWgCEQVFf1PDkhVvbz1CjlPIi8BcMOEdlGkoAePUzbWrUyhBw/24vE8XGRSJ3YGWqV6SUetSoQqRM7e8z8NEo5DyJvIAnB+dqLioqgUqmg1WrRuHFjd3eHSF4++wwYM8a8Xa+3fjnLBkqZeVgp50HkiWz9+80xO0RknaUw88gjwBdf1PvQvj4SerQKrfdx3E0p50GkZLyMRUTmXn/dctARwiFBh4jIlRh2iMiUJAFJSaZtL7/MFcqJyGPxMhYRGTz2GJCWZt7OkENEHo5hh4gsX7JauxaYONH1fSEicjCGHSJv1qEDcOyYeTurOUSkIByzQ+SNdDpDNadm0Nm9m0GHiBSHlR0ib2PnUg9ERJ6KlR0ib1FSYjnonD7NoENEisbKDpE3YDWHiLwYKztESnb5suWg8+uvDDpE5DVY2SFSKlZziIgAsLJDpDzZ2ZaDTlkZgw4ReSVWdoiUxFLICQkBrl1zfV+IiGSClR0iJdi82XLQ0esZdIjI67GyQ+TpLIWcwYOBb75xfV+IiGTIrZWd3bt3IyEhARqNBpIkYePGjSbbhRB45ZVXoNFoEBQUhL59++L48eMm+5SXl2PmzJkICwtDo0aNMHz4cFy6dMmFZ0HkJsuWWQ46QjDoEBFV49awc/PmTXTs2BHLly+3uH3JkiVYunQpli9fjkOHDkGtVmPQoEEoLi427pOYmIgNGzZg3bp12Lt3L0pKSjBs2DDodDpXnQaR60kSMGuWadvs2RyATERkgSSEPH47SpKEDRs2YOTIkQAMVR2NRoPExETMnz8fgKGKExERgcWLF2PatGnQarVo1qwZ1q5dizFjxgAArly5gqioKGzZsgUPPfSQTe9dVFQElUoFrVaLxo0bO+X8iBxi5kzA0j8O5PE1JiJyKVv/fst2gHJubi7y8/MxePBgY1tAQAD69OmDffv2AQAyMzNRWVlpso9Go0FcXJxxH0vKy8tRVFRk8iCSPUkyDzrvvcegQ0RUC9mGnfz8fABARESESXtERIRxW35+Pho0aICmTZta3ceSRYsWQaVSGR9RUVEO7j2RA/XubX1szrRpru8PEZGHkW3YqSLV+CUvhDBrq6m2fZKSkqDVao2PixcvOqSvRA6l1xtCzp49pu3ffMNqDhFRHcj21nO1Wg3AUL2JjIw0thcUFBirPWq1GhUVFSgsLDSp7hQUFKBnz55Wjx0QEICAgAAn9ZzIARo2BEpLzdsZcoiI6ky2lZ2YmBio1Wps3brV2FZRUYFdu3YZg0yXLl3g7+9vsk9eXh5ycnLuGHaIZKuszFDNqRl0jh1j0CEispNbKzslJSU4e/as8Xlubi6ys7MREhKCli1bIjExEQsXLkTr1q3RunVrLFy4EA0bNsT48eMBACqVClOmTMGcOXMQGhqKkJAQzJ07F+3bt8fAgQPddVpE9uHCnURETuHWsHP48GH069fP+Hz27NkAgMmTJ2PNmjV44YUXUFpaimeeeQaFhYXo3r07vv32WwQHBxtf8+abb8LPzw+jR49GaWkpBgwYgDVr1sDX19fl50Nkl4ICoMZAfABAXh7wv8u5RERkP9nMs+NOnGeH3IbVHCIiu3n8PDtEinbypOWgc/Mmgw4RkYPJ9m4sIsWyFHIkyXCruQ10eoGDuddRUFyG8OBAdIsJga/PnadjICLyZgw7RK6yYwfQv795u04H+NhWZM3IyUNK+gnkacuMbZGqQCQnxCI+LvIOryQi8l68jEXkCpJkHnS6dzdcsqpD0JmelmUSdAAgX1uG6WlZyMjJc1RviYgUhWGHyJlWrbK+1MOBAzYfRqcXSEk/AUujearaUtJPQKfneB8iopoYdoicRZKAKVNM26ZOtWsA8sHc62YVneoEgDxtGQ7mXq/zsYmIlI5hh8jRkpKsV3M++MCuQxYUWw869uxHRORNOECZyJEshZylS4Hnn6/XYcODAx26HxGRN2HYIXKEESOATZvM2x00Z063mBBEqgKRry2zOG5HAqBWGW5DJyIiU7yMRVQfQhiqOTWDzsaNDp0c0NdHQnJCLABDsKmu6nlyQizn2yEisoBhh8heGo3l28aFMFR6HCw+LhKpEztDrTK9VKVWBSJ1YmfOs0NEZAUvYxHVVUUFEBBg3n74MNCli1PfOj4uEoNi1ZxBmYioDhh2iOpCBgt3+vpI6NEq1GXvR0Tk6XgZi8gWhYWWg86FC1y4k4hI5ljZIa9h9wKaMqjmEBGR/Rh2yCvYtYDmTz8BrVqZt2u1QOPGTuopERE5Gi9jkeLZtYCmJFkOOkIw6BAReRiGHVK0Oi+guX+/5ctWlZW8bEVE5KEYdkjR6rSApiQBPXua7tCmjSHk+PGKLxGRp+JvcFI0WxbGHHZyN3rcM8x8g15vfXAyERF5DIYdUrTaFsb8ebGFkDNmDLBunZN6RERErsawQ4pmbQHNGfvWYe6eNPMXcFwOEZHicMwOKZqlBTR/XjzMPOj87W8MOkRECsXKDile1QKaV599Ho/t/rf5Dgw5RESKxrBDXiG+vcasTZ/2MXwmjHdDb4iIyJV4GYuUbfZsy3dUCcGgQ0TkJVjZIWXS6SzPjfPf/wJt27q+P0RE5Das7JDyJCRYDjpCMOgQEXkhVnZIOcrKgKAg8/b8fCAiwvX9ISIiWWBlh5ShbVvzoNOsmaGaw6BDROTVWNkhz3btGhAWZt5eXAzcdZdJk04vcDD3OgqKyxAeHIhuMSHw9eFyEERESsewQ57L0l1WffoAO3eaNWfk5CEl/YTJoqCRqkAkJ8QiPi7SiZ0kIiJ342Us8jy5uZaDTmWl1aAzPS3LbPXzfG0ZpqdlISMnz0kdJSIiOWDYIc8iScDvfmfa9tRThrE5Fu7A0ukFUtJPwNIcyVVtKeknoNNzFmUiIqVi2CHPkJlpuZqj1wMrVlh92cHc62YVneoEgDxtGQ7mXndAJ4mISI4Ydkj+JAno2tW0beFCQzXHUgCqpqDYetCxZz8iIvI8HKBM8rVlC/Dww+btdVi4Mzw40KH7ERGR52Flh+RJksyDTlpanVco7xYTgkhVIKzVfyQY7srqFhNiVzeJiEj+GHZIXlassLpwJyZMqPPhfH0kJCfEAoBZ4Kl6npwQy/l2iIgUjGGH5EOSgP/7P9O2776rczWnpvi4SKRO7Ay1yvRSlVoViNSJnTnPDhGRwnHMDrnfyy8Df/ubeXs9Q0518XGRGBSr5gzKREReiGGH3EcIwMdCcfHoUaB9e4e/na+PhB6tQh1+XCIikjdZX8a6ffs2XnrpJcTExCAoKAi/+93v8Ne//hV6vd64jxACr7zyCjQaDYKCgtC3b18cP37cjb0mm4wfbznoCOGUoENERN5L1pWdxYsX47333sOHH36Idu3a4fDhw3jiiSegUqnw3HPPAQCWLFmCpUuXYs2aNWjTpg1effVVDBo0CKdOnUJwcLCbz4DMVFQAAQHm7RcvAi1auL4/RESkeJIQDhwY4WDDhg1DREQEVq5caWx75JFH0LBhQ6xduxZCCGg0GiQmJmL+/PkAgPLyckRERGDx4sWYNm2axeOWl5ejvLzc+LyoqAhRUVHQarVo3Lixc0/Km3XpAmRlmbYFBABlnNCPiIjqrqioCCqVqta/37K+jNWrVy989913OH36NADgxx9/xN69ezF06FAAQG5uLvLz8zF48GDjawICAtCnTx/s27fP6nEXLVoElUplfERFRTn3RLydVmu406pm0Llxg0GHiIicTtaXsebPnw+tVot7770Xvr6+0Ol0eO211zBu3DgAQH5+PgAgIiLC5HURERE4f/681eMmJSVh9uzZxudVlR1ygqAg80DTpQtw+LB7+kNERF5H1mHn3//+N9LS0vDJJ5+gXbt2yM7ORmJiIjQaDSZPnmzcT6oxCZ0QwqytuoCAAARYGjdCjnPxItCypXl7eTnQoIHr+0NERF5L1mFn3rx5ePHFFzF27FgAQPv27XH+/HksWrQIkydPhlqtBmCo8ERG/jYxXEFBgVm1h1zIUtAcNw745BPX94WIiLyerMfs3Lp1Cz41bk/29fU13noeExMDtVqNrVu3GrdXVFRg165d6Nmzp0v7SgCOHbMcdPR6Bh0iInIbWVd2EhIS8Nprr6Fly5Zo164djhw5gqVLl+LJJ58EYLh8lZiYiIULF6J169Zo3bo1Fi5ciIYNG2L8+PFu7r2XsRRyXn4ZSElxfV+IiIiqkXXYWbZsGRYsWIBnnnkGBQUF0Gg0mDZtGl5++WXjPi+88AJKS0vxzDPPoLCwEN27d8e3337LOXZcZft2YMAA83b5zmhAREReRtbz7LiKrffpUw2WqjkrVgBPPeX6vhARkdex9e+3rCs7JFNpacBjj5m3MzcTEZEMMexQ3Viq5mzZAgwZ4vq+EBER2UDWd2ORjCxaZDnoCMGgQ0REssbKDt2ZEJZXJ8/MBDp3dn1/iIiI6oiVHbLu8GGLQafHwm3IaBBp4QVERETyw7BD5vR64Pe/Bx54wKT5gWc/wt3zv0K+tgzT07KQkZPnpg4SERHZjmGHTO3YAfj6AtVWjZ+ZMA93z/8KV+8KAQBU3XOVkn4COj3vwCIiInnjmB0yuH0baNcOOH3a2FTQqCl6Pb0KFX7+ZrsLAHnaMhzMvY4erUJd2FEiIqK6YdghYNMmYMQIk6b9yz7CuEshtb60oLjMWb0iIiJyCF7G8mZlZUDTpqZBp0MHQ5VnyFCbDhEeHOikzhERETkGw463+ugjICgIuHHjt7Y9e4AffwR8fdEtJgSRqkBYmFkHACABiFQFoltM7dUfIiIid2LY8TZFRYbJASdP/q2tf3/DHVi9ehmbfH0kJCfEAoBZ4Kl6npwQC18fa3GIiIhIHhh2vMk77wAqlWnbkSPAd99ZnB05Pi4SqRM7Q60yvVSlVgUidWJnxMdxrh0iIpI/DlD2Br/+CjRrZto2dizw6ae1vjQ+LhKDYtU4mHsdBcVlCA82XLpiRYeIiDwFw46T6PRCHgHhlVeAlBTTttOngdatbT6Er4/E28uJiMhjMew4QUZOHlLSTyBP+9tt2ZGqQCQnxLru0s+lS0BUlGnbjBnAsmWueX8iIiKZ4JgdB8vIycP0tCyToAPAtUsszJhhHnQuXmTQISIir8Sw40A6vUBK+glYWkDBJUssnD5tGGj8z3/+1vbKK4aVy1u0cM57EhERyRwvYznQwdzrZhWd6py6xMLYscC//23advUqEBbm2PchIiLyMKzsOJCtSyc4dImF7GxDNad60Hn7bUM1h0GHiIiIlR1HsnXpBIcssSCEYTLAnTtN24uKgODg+h+fiIhIIVjZcSCXLbGwdy/g42MadD76yBCAGHSIiIhMMOw4kNOXWNDpgPbtgT/84be2kBCgtBR47DH7jklERKRwDDsO5rQlFrZsAfz8gJyc39o2bQKuXQMCufI4ERGRNRyz4wQOXWKhvBxo2RIoKPitrW1bQ+jx48dHRERUG/61dBKHLLHw6afA+PGmbTt2AH371u+4REREXoRhR45KSswHGvfqBezaZRiYTERERDbjX065SU01DzqHDwN79rgt6Oj0AvvPXcOX2Zex/9w1580ATURE5ASs7MjF9etAaI3LXqNGAV98YZg00E1ksagpERFRPbCyIwcLF5oHnZMngf/8x+1Bx+2LmhIREdUTw447XbliCDN/+ctvbVOnGiYHvPde9/ULMljUlIiIyEEYdtxl9mygeXPTtp9/Bj74wC3dqakui5oSERHJGcOOq507Z6jmvPnmb21//rOhmhMd7b5+1eCWRU2JiIicgAOUXWnyZMMaVtX98gsQHu6e/tyBSxc1JSIiciJWdlzh2DFDNad60PnHPwzVHBkGHcCFi5oSERE5GcOOMwkBDBkCdOhg2n7jBjBnjlu6ZCunL2pKRETkIgw7zvT++0BGxm/PV640BCCVyn19qgOnLWpKRETkQhyz40wtWhj+NygI+PVXoGFD9/bHDg5d1JSIiMgNGHacadgwQyXHwzlkUVMiIiI34WUsIiIiUjSGHSIiIlI02Yedy5cvY+LEiQgNDUXDhg1x//33IzMz07hdCIFXXnkFGo0GQUFB6Nu3L44fP+7GHhMREZGcyDrsFBYW4ve//z38/f3x9ddf48SJE3jjjTfQpEkT4z5LlizB0qVLsXz5chw6dAhqtRqDBg1CcXGx+zpOREREsiEJId8RtC+++CK+//577Nmzx+J2IQQ0Gg0SExMxf/58AEB5eTkiIiKwePFiTJs2zab3KSoqgkqlglarRePGjR3WfyIiInIeW/9+y7qys2nTJnTt2hWPPvoowsPD0alTJ6xYscK4PTc3F/n5+Rg8eLCxLSAgAH369MG+ffusHre8vBxFRUUmDyIiIlImWYedn376CampqWjdujW++eYbPP3005g1axY++t+yC/n5+QCAiIgIk9dFREQYt1myaNEiqFQq4yMqKsp5J0FERERuJeuwo9fr0blzZyxcuBCdOnXCtGnTMHXqVKSmpprsJ0mmE9wJIczaqktKSoJWqzU+Ll686JT+ExERkfvJOuxERkYiNjbWpO2+++7DhQsXAABqtRoAzKo4BQUFZtWe6gICAtC4cWOTBxERESmTrMPO73//e5w6dcqk7fTp04iOjgYAxMTEQK1WY+vWrcbtFRUV2LVrF3r27OnSvhIREZE8yXq5iOeffx49e/bEwoULMXr0aBw8eBAffPABPvjgAwCGy1eJiYlYuHAhWrdujdatW2PhwoVo2LAhxo8f7+beExERkRzIOuw88MAD2LBhA5KSkvDXv/4VMTExeOuttzBhwgTjPi+88AJKS0vxzDPPoLCwEN27d8e3336L4OBgN/aciIiI5ELW8+y4CufZISIi8jyKmGeHiIiIqL5kfRnLVaqKW5xckIiIyHNU/d2u7SIVww5gXEeLkwsSERF5nuLiYqhUKqvbOWYHhskLr1y5guDg4DtORlhUVISoqChcvHjR68b2eOu5e+t5A9577t563gDP3RvP3dPPWwiB4uJiaDQa+PhYH5nDyg4AHx8ftGjRwub9vXkiQm89d289b8B7z91bzxvguXvjuXvyed+polOFA5SJiIhI0Rh2iIiISNEYduogICAAycnJCAgIcHdXXM5bz91bzxvw3nP31vMGeO7eeO7ect4coExERESKxsoOERERKRrDDhERESkaww4REREpGsMOERERKRrDTg133303JEkyezz77LMAgMcff9xs24MPPujmXttn9+7dSEhIgEajgSRJ2Lhxo8l2IQReeeUVaDQaBAUFoW/fvjh+/LjJPuXl5Zg5cybCwsLQqFEjDB8+HJcuXXLhWdTdnc67srIS8+fPR/v27dGoUSNoNBpMmjQJV65cMTlG3759zX4Oxo4d6+IzqbvaPnNbfr498TMHaj93S997SZLw97//3biPJ37uixYtwgMPPIDg4GCEh4dj5MiROHXqlMk+Svyu13beSv6u2/KZK/m7bgnDTg2HDh1CXl6e8bF161YAwKOPPmrcJz4+3mSfLVu2uKu79XLz5k107NgRy5cvt7h9yZIlWLp0KZYvX45Dhw5BrVZj0KBBxrXEACAxMREbNmzAunXrsHfvXpSUlGDYsGHQ6XSuOo06u9N537p1C1lZWViwYAGysrKwfv16nD59GsOHDzfbd+rUqSY/B++//74rul8vtX3mQO0/3574mQO1n3v1c87Ly8OqVasgSRIeeeQRk/087XPftWsXnn32WRw4cABbt27F7du3MXjwYNy8edO4jxK/67Wdt5K/67Z85oByv+sWCbqj5557TrRq1Uro9XohhBCTJ08WI0aMcG+nnACA2LBhg/G5Xq8XarVavP7668a2srIyoVKpxHvvvSeEEOLGjRvC399frFu3zrjP5cuXhY+Pj8jIyHBZ3+uj5nlbcvDgQQFAnD9/3tjWp08f8dxzzzm3c05m6dxr+/lWwmcuhG2f+4gRI0T//v1N2pTwuRcUFAgAYteuXUII7/mu1zxvS5T6Xbd07t7yXa/Cys4dVFRUIC0tDU8++aTJAqE7d+5EeHg42rRpg6lTp6KgoMCNvXSO3Nxc5OfnY/Dgwca2gIAA9OnTB/v27QMAZGZmorKy0mQfjUaDuLg44z5KoNVqIUkSmjRpYtL+8ccfIywsDO3atcPcuXNN/hXsye708+0tn/kvv/yCzZs3Y8qUKWbbPP1z12q1AICQkBAA3vNdr3ne1vZR4nfd2rl703edC4HewcaNG3Hjxg08/vjjxrYhQ4bg0UcfRXR0NHJzc7FgwQL0798fmZmZipqBMj8/HwAQERFh0h4REYHz588b92nQoAGaNm1qtk/V6z1dWVkZXnzxRYwfP95kkbwJEyYgJiYGarUaOTk5SEpKwo8//mi87Ompavv59obPHAA+/PBDBAcHY9SoUSbtnv65CyEwe/Zs9OrVC3FxcQC847tu6bxrUup33dq5e9t3nWHnDlauXIkhQ4ZAo9EY28aMGWP8/3FxcejatSuio6OxefNms1+MSlC9ogUYvjg122qyZR9PUFlZibFjx0Kv1+Pdd9812TZ16lTj/4+Li0Pr1q3RtWtXZGVloXPnzq7uqsPY+/OtlM+8yqpVqzBhwgQEBgaatHv65z5jxgwcPXoUe/fuNdum5O/6nc4bUPZ33dq5e9t3nZexrDh//jy2bduGp5566o77RUZGIjo6GmfOnHFRz1xDrVYDgFmCLygoMP4LUK1Wo6KiAoWFhVb38VSVlZUYPXo0cnNzsXXrVpN/6VnSuXNn+Pv7K+7noObPt5I/8yp79uzBqVOnav3uA571uc+cORObNm3Cjh070KJFC2O70r/r1s67ipK/67Wde3VK/64z7FixevVqhIeH4+GHH77jfteuXcPFixcRGRnpop65RlXZtnqptqKiArt27ULPnj0BAF26dIG/v7/JPnl5ecjJyTHu44mqfvmdOXMG27ZtQ2hoaK2vOX78OCorKxX3c1Dz51upn3l1K1euRJcuXdCxY8da9/WEz10IgRkzZmD9+vXYvn07YmJiTLYr9bte23kDyv2u23LuNSn+u+6ecdHyptPpRMuWLcX8+fNN2ouLi8WcOXPEvn37RG5urtixY4fo0aOHaN68uSgqKnJTb+1XXFwsjhw5Io4cOSIAiKVLl4ojR44Y70R4/fXXhUqlEuvXrxfHjh0T48aNE5GRkSbn+vTTT4sWLVqIbdu2iaysLNG/f3/RsWNHcfv2bXedVq3udN6VlZVi+PDhokWLFiI7O1vk5eUZH+Xl5UIIIc6ePStSUlLEoUOHRG5urti8ebO49957RadOnWR93kLc+dxt/fn2xM9ciNp/3oUQQqvVioYNG4rU1FSz13vq5z59+nShUqnEzp07TX6eb926ZdxHid/12s5byd/12s5d6d91Sxh2LPjmm28EAHHq1CmT9lu3bonBgweLZs2aCX9/f9GyZUsxefJkceHCBTf1tH527NghAJg9Jk+eLIQw3JKanJws1Gq1CAgIEL179xbHjh0zOUZpaamYMWOGCAkJEUFBQWLYsGGy/+9xp/POzc21uA2A2LFjhxBCiAsXLojevXuLkJAQ0aBBA9GqVSsxa9Ysce3aNfeemA3udO62/nx74mcuRO0/70II8f7774ugoCBx48YNs9d76udu7ed59erVxn2U+F2v7byV/F2v7dyV/l23RBJCCAcWioiIiIhkhWN2iIiISNEYdoiIiEjRGHaIiIhI0Rh2iIiISNEYdoiIiEjRGHaIiIhI0Rh2iIiISNEYdoiIiEjRGHaIyKP9/PPPkCQJ2dnZ7u4KEckUww4R1cvjjz8OSZIgSRL8/f3xu9/9DnPnzsXNmzdd8v5RUVHIy8tDXFycS96PiDyPn7s7QESeLz4+HqtXr0ZlZSX27NmDp556Cjdv3kRqaqrT39vX1xdqtdrp70NEnouVHSKqt4CAAKjVakRFRWH8+PGYMGECNm7cCAA4fvw4Hn74YTRu3BjBwcH4wx/+gHPnzgEADh06hEGDBiEsLAwqlQp9+vRBVlaWybElSUJqaiqGDBmCoKAgxMTE4PPPPzdur3kZa+fOnZAkCd999x26du2Khg0bomfPnjh16pTJcTdt2oSuXbsiMDAQYWFhGDVqlHFbYWEhJk2ahKZNm6Jhw4YYMmQIzpw5Y9y+Zs0aNGnSBF999RXatm2Lhg0b4k9/+hNu3ryJDz/8EHfffTeaNm2KmTNnQqfTGV9XUVGBF154Ac2bN0ejRo3QvXt37Ny50xEfARHdAcMOETlcUFAQKisrcfnyZfTu3RuBgYHYvn07MjMz8eSTT+L27dsAgOLiYkyePBl79uzBgQMH0Lp1awwdOhTFxcUmx1uwYAEeeeQR/Pjjj5g4cSLGjRuHkydP3rEPf/nLX/DGG2/g8OHD8PPzw5NPPmnctnnzZowaNQoPP/wwjhw5YgxGVR5//HEcPnwYmzZtwv79+yGEwNChQ1FZWWnc59atW3jnnXewbt06ZGRkYOfOnRg1ahS2bNmCLVu2YO3atfjggw/wxRdfGF/zxBNP4Pvvv8e6detw9OhRPProo4iPjzcJUkTkBG5edZ2IPNzkyZPFiBEjjM9/+OEHERoaKkaPHi2SkpJETEyMqKiosOlYt2/fFsHBwSI9Pd3YBkA8/fTTJvt1795dTJ8+XQghRG5urgAgjhw5IoQQYseOHQKA2LZtm3H/zZs3CwCitLRUCCFEjx49xIQJEyz24fTp0wKA+P77741tv/76qwgKChKfffaZEEKI1atXCwDi7Nmzxn2mTZsmGjZsKIqLi41tDz30kJg2bZoQQoizZ88KSZLE5cuXTd5vwIABIikpyab/PkRkH1Z2iKjevvrqK9x1110IDAxEjx490Lt3byxbtgzZ2dn4wx/+AH9/f4uvKygowNNPP402bdpApVJBpVKhpKQEFy5cMNmvR48eZs9rq+x06NDB+P8jIyON7wcA2dnZGDBggMXXnTx5En5+fujevbuxLTQ0FG3btjV5z4YNG6JVq1bG5xEREbj77rtx1113mbRVvWdWVhaEEGjTpg3uuusu42PXrl3Gy3pE5BwcoExE9davXz+kpqbC398fGo3GGG6CgoLu+LrHH38cV69exVtvvYXo6GgEBASgR48eqKioqPU9JUm64/bqAatqX71eX2u/hBBW26u/Z80AV3U3Ws22qvfU6/Xw9fVFZmYmfH19TfarHpCIyPFY2SGiemvUqBHuueceREdHm/zB79ChA/bs2WMy1qW6PXv2YNasWRg6dCjatWuHgIAA/Prrr2b7HThwwOz5vffea3d/O3TogO+++87ittjYWNy+fRs//PCDse3atWs4ffo07rvvPrvfs1OnTtDpdCgoKMA999xj8uDdZETOxbBDRE4zY8YMFBUVYezYsTh8+DDOnDmDtWvXGu+Muueee7B27VqcPHkSP/zwAyZMmGCx6vL5559j1apVOH36NJKTk3Hw4EHMmDHD7n4lJyfj008/RXJyMk6ePIljx45hyZIlAIDWrVtjxIgRmDp1Kvbu3WscFN28eXOMGDHC7vds06YNJkyYgEmTJmH9+vXIzc3FoUOHsHjxYmzZssXu4xJR7Rh2iMhpQkNDsX37dpSUlKBPnz7o0qULVqxYYaz+rFq1CoWFhejUqRMee+wxzJo1C+Hh4WbHSUlJwbp169ChQwd8+OGH+PjjjxEbG2t3v/r27YvPP/8cmzZtwv3334/+/fubVHJWr16NLl26YNiwYejRoweEENiyZYvVsUe2Wr16NSZNmoQ5c+agbdu2GD58OH744QdERUXV67hEdGeSsHaBmohIBiRJwoYNGzBy5Eh3d4WIPBQrO0RERKRoDDtERESkaLz1nIhkjVfaiai+WNkhIiIiRWPYISIiIkVj2CEiIiJFY9ghIiIiRWPYISIiIkVj2CEiIiJFY9ghIiIiRWPYISIiIkX7f478ExZfeouUAAAAAElFTkSuQmCC", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "plt.plot(df.ingreso_muestra,df.consumo_muestra,'o')\n", "plt.plot(df.ingreso_muestra,ajuste_1.fittedvalues,'-',color='r')\n", "plt.xlabel('Pcapincome')\n", "plt.ylabel('Cellphone')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Regresión: Paso a paso" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "La función poblacional sería:\n", "\n", "$$\n", "Y_i = \\beta_1 + \\beta_2X_i+u_i\n", "$$\n", "\n", "Como no es observable, se usa la muestral\n", "\n", "$$\n", "Y_i=\\hat{\\beta}_1+\\hat{\\beta}_2X_i+\\hat{u}_i\n", "$$\n", "\n", "\n", "$$\n", "Y_i=\\hat{Y}_i+\\hat{u}_i\n", "$$\n", "\n", "\n", "$$\n", "\\hat{u}_i = Y_i-\\hat{Y}_i\n", "$$\n", "\n", "\n", "$$\n", "\\hat{u}_i = Y_i- \\hat{\\beta}_1-\\hat{\\beta}_2X_i\n", "$$\n", "\n", "\n", "Es por esto que los residuos se obtienen a través de los betas:\n", "\n", "\n", "$$\n", "\\sum\\hat{u}_i^2 =\\sum (Y_i- \\hat{\\beta}_1-\\hat{\\beta}_2X_i)^2\n", "$$\n", "\n", "\n", "$$\n", "\\sum\\hat{u}_i^2 =f(\\hat{\\beta}_1,\\hat{\\beta}_2)\n", "$$\n", "\n", "\n", "Diferenciando se obtiene:\n", "\n", "$$\n", " \\hat{\\beta}_2 = \\frac{S_{xy}}{S_{xx}}\n", "$$\n", "\n", "$$\n", " \\hat\\beta_1 = \\bar{Y} - \\hat\\beta_2\\bar{X}\n", "$$\n", "donde \n", "\n", "$$\n", "S_{xx} = \\sum_{i=1}^{n}x_i^2-n\\bar{x}^2\n", "$$\n", "\n", "$$\n", "S_{xy} = \\sum_{i=1}^{n}x_i y_i-n\\bar{x}\\bar{y}\n", "$$\n", "\n", "\n", "Abrimos la `tabla3.2`, vamos a obtener:\n", "\n", "- salario promedio por hora (Y) y \n", "- los años de escolaridad (X).\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Y X\n", "0 70 80\n", "1 65 100\n", "2 90 120\n", "3 95 140\n", "4 110 160" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "consumo = pd.read_csv('https://raw.githubusercontent.com/vmoprojs/DataLectures/master/GA/Tabla3_2.csv',\n", " sep = ';',decimal = '.')\n", "\n", "\n", "consumo.head()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(24.454545454545467, 0.509090909090909)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "media_x = np.mean(consumo['X'])\n", "media_y = np.mean(consumo['Y'])\n", "\n", "\n", "n = consumo.shape[0]\n", "\n", "sumcuad_x = np.sum(consumo['X']*consumo['X'])\n", "sum_xy = np.sum(consumo['X']*consumo['Y'])\n", "\n", "beta_som = (sum_xy-n*media_x*media_y)/(sumcuad_x-n*(media_x**2))\n", "alpha_som = media_y-beta_som*media_x\n", "(alpha_som,beta_som)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Verificamos lo anterior mediante:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: Y R-squared: 0.962\n", "Model: OLS Adj. R-squared: 0.957\n", "Method: Least Squares F-statistic: 202.9\n", "Date: Wed, 18 Sep 2024 Prob (F-statistic): 5.75e-07\n", "Time: 05:32:17 Log-Likelihood: -31.781\n", "No. Observations: 10 AIC: 67.56\n", "Df Residuals: 8 BIC: 68.17\n", "Df Model: 1 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "Intercept 24.4545 6.414 3.813 0.005 9.664 39.245\n", "X 0.5091 0.036 14.243 0.000 0.427 0.592\n", "==============================================================================\n", "Omnibus: 1.060 Durbin-Watson: 2.680\n", "Prob(Omnibus): 0.589 Jarque-Bera (JB): 0.777\n", "Skew: -0.398 Prob(JB): 0.678\n", "Kurtosis: 1.891 Cond. No. 561.\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/victormorales/opt/anaconda3/lib/python3.9/site-packages/scipy/stats/_stats_py.py:1971: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=10\n", " k, _ = kurtosistest(a, axis)\n" ] } ], "source": [ "reg_1 = smf.ols('Y~X',data = consumo)\n", "print(reg_1.fit().summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Veamos cómo queda nuestra estimación:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Xy_ajustado
08065.181818
110075.363636
212085.545455
314095.727273
4160105.909091
5180116.090909
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" ], "text/plain": [ " X y_ajustado\n", "0 80 65.181818\n", "1 100 75.363636\n", "2 120 85.545455\n", "3 140 95.727273\n", "4 160 105.909091\n", "5 180 116.090909\n", "6 200 126.272727\n", "7 220 136.454545\n", "8 240 146.636364\n", "9 260 156.818182" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_ajustado = alpha_som+beta_som*consumo['X']\n", "\n", "dfAux = pd.DataFrame(list(zip(consumo['X'],y_ajustado)),\n", " columns = ['X','y_ajustado'])\n", "dfAux" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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XYy_ajustadoe
0807065.1818184.818182
11006575.363636-10.363636
21209085.5454554.454545
31409595.727273-0.727273
4160110105.9090914.090909
5180115116.090909-1.090909
6200120126.272727-6.272727
7220140136.4545453.545455
8240155146.6363648.363636
9260150156.818182-6.818182
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" ], "text/plain": [ " X Y y_ajustado e\n", "0 80 70 65.181818 4.818182\n", "1 100 65 75.363636 -10.363636\n", "2 120 90 85.545455 4.454545\n", "3 140 95 95.727273 -0.727273\n", "4 160 110 105.909091 4.090909\n", "5 180 115 116.090909 -1.090909\n", "6 200 120 126.272727 -6.272727\n", "7 220 140 136.454545 3.545455\n", "8 240 155 146.636364 8.363636\n", "9 260 150 156.818182 -6.818182" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "e = consumo['Y']-y_ajustado\n", "\n", "\n", "dfAux = pd.DataFrame(list(zip(consumo['X'],consumo['Y'],y_ajustado,e)),\n", " columns = ['X','Y','y_ajustado','e'])\n", "\n", "dfAux" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[1.00000000e+00, 1.13838806e-15],\n", " [1.13838806e-15, 1.00000000e+00]])" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.mean(e)\n", "np.corrcoef(e,consumo['X'])" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.9620615604867568" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "SCT = np.sum((consumo['Y']-media_y)**2)\n", "SCE = np.sum((y_ajustado-media_y)**2)\n", "SCR = np.sum(e**2)\n", "\n", "R_2 = SCE/SCT\n", "R_2\n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: Y R-squared: 0.962\n", "Model: OLS Adj. R-squared: 0.957\n", "Method: Least Squares F-statistic: 202.9\n", "Date: Wed, 18 Sep 2024 Prob (F-statistic): 5.75e-07\n", "Time: 05:32:17 Log-Likelihood: -31.781\n", "No. Observations: 10 AIC: 67.56\n", "Df Residuals: 8 BIC: 68.17\n", "Df Model: 1 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "Intercept 24.4545 6.414 3.813 0.005 9.664 39.245\n", "X 0.5091 0.036 14.243 0.000 0.427 0.592\n", "==============================================================================\n", "Omnibus: 1.060 Durbin-Watson: 2.680\n", "Prob(Omnibus): 0.589 Jarque-Bera (JB): 0.777\n", "Skew: -0.398 Prob(JB): 0.678\n", "Kurtosis: 1.891 Cond. No. 561.\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/victormorales/opt/anaconda3/lib/python3.9/site-packages/scipy/stats/_stats_py.py:1971: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=10\n", " k, _ = kurtosistest(a, axis)\n" ] } ], "source": [ "print(reg_1.fit().summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Otro ejemplo" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "H_0:\\beta_2=0\n", "$$\n", "$$\n", "H_1:\\beta_2\\neq 0\n", "$$" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: FOODEXP R-squared: 0.370\n", "Model: OLS Adj. R-squared: 0.358\n", "Method: Least Squares F-statistic: 31.10\n", "Date: Wed, 18 Sep 2024 Prob (F-statistic): 8.45e-07\n", "Time: 05:32:17 Log-Likelihood: -308.16\n", "No. Observations: 55 AIC: 620.3\n", "Df Residuals: 53 BIC: 624.3\n", "Df Model: 1 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "Intercept 94.2088 50.856 1.852 0.070 -7.796 196.214\n", "TOTALEXP 0.4368 0.078 5.577 0.000 0.280 0.594\n", "==============================================================================\n", "Omnibus: 0.763 Durbin-Watson: 2.083\n", "Prob(Omnibus): 0.683 Jarque-Bera (JB): 0.258\n", "Skew: 0.120 Prob(JB): 0.879\n", "Kurtosis: 3.234 Cond. No. 3.66e+03\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "[2] The condition number is large, 3.66e+03. This might indicate that there are\n", "strong multicollinearity or other numerical problems.\n" ] } ], "source": [ "uu = 'https://raw.githubusercontent.com/vmoprojs/DataLectures/master/GA/table2_8.csv'\n", "\n", "datos = pd.read_csv(uu,sep = ';')\n", "datos.shape\n", "datos.columns\n", "m1 = smf.ols('FOODEXP~TOTALEXP',data = datos)\n", "print(m1.fit().summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Regresamos el gasto total en el gasto en alimentos\n", "\n", "¿Son los coeficientes diferentes de cero?" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3.8888077047438685e-07" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import scipy.stats as st\n", "\n", "\n", "t_ho = 0\n", "t1 = (0.4368-t_ho)/ 0.078\n", "(1-st.t.cdf(t1,df = 53))\n", "\n" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.04261898819196597" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "t_ho = 0.3\n", "t1 = (0.4368-t_ho)/ 0.078\n", "(1-st.t.cdf(np.abs(t1),df = 53))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Interpretación de los coeficientes\n", "\n", "- El coeficiente de la variable dependiente mide la tasa de cambio (derivada=pendiente) del modelo\n", "- La interpretación suele ser *En promedio, el aumento de una unidad en la variable independiente produce un aumento/disminución de $\\beta_i$ cantidad en la variable dependiente*\n", "- Interprete la regresión anterior." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Práctica: Paridad del poder de compra" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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COUNTRYBMACLCBMAC$EXCHPPPLOCALC
0United States2.542.54-99999.00-99999.00-99999
1Argentina2.502.501.000.98-40
2Australia3.001.521.981.18-35
3Brazil3.601.642.191.42-31
4Britain1.992.851.431.2812
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" ], "text/plain": [ " COUNTRY BMACLC BMAC$ EXCH PPP LOCALC\n", "0 United States 2.54 2.54 -99999.00 -99999.00 -99999\n", "1 Argentina 2.50 2.50 1.00 0.98 -40\n", "2 Australia 3.00 1.52 1.98 1.18 -35\n", "3 Brazil 3.60 1.64 2.19 1.42 -31\n", "4 Britain 1.99 2.85 1.43 1.28 12" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "uu = \"https://raw.githubusercontent.com/vmoprojs/DataLectures/master/GA/Tabla5_9.csv\"\n", "\n", "datos = pd.read_csv(uu,sep = ';')\n", "datos.head()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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COUNTRYBMACLCBMAC$EXCHPPPLOCALC
0United States2.542.54NaNNaNNaN
1Argentina2.502.501.000.98-40.0
2Australia3.001.521.981.18-35.0
3Brazil3.601.642.191.42-31.0
4Britain1.992.851.431.2812.0
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" ], "text/plain": [ " COUNTRY BMACLC BMAC$ EXCH PPP LOCALC\n", "0 United States 2.54 2.54 NaN NaN NaN\n", "1 Argentina 2.50 2.50 1.00 0.98 -40.0\n", "2 Australia 3.00 1.52 1.98 1.18 -35.0\n", "3 Brazil 3.60 1.64 2.19 1.42 -31.0\n", "4 Britain 1.99 2.85 1.43 1.28 12.0" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "datos['EXCH'] = datos.EXCH.replace(to_replace= -99999, value=np.nan)\n", "datos['PPP'] = datos.PPP.replace(to_replace = -99999, value = np.nan)\n", "datos['LOCALC'] = datos.LOCALC.replace(to_replace= -99999, value = np.nan)\n", "\n", "datos.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Regresamos la paridad del poder de compra en la tasa de cambio" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: EXCH R-squared: 0.987\n", "Model: OLS Adj. R-squared: 0.986\n", "Method: Least Squares F-statistic: 2066.\n", "Date: Wed, 18 Sep 2024 Prob (F-statistic): 8.80e-28\n", "Time: 05:32:17 Log-Likelihood: -205.44\n", "No. Observations: 30 AIC: 414.9\n", "Df Residuals: 28 BIC: 417.7\n", "Df Model: 1 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "Intercept -61.3889 44.987 -1.365 0.183 -153.541 30.763\n", "PPP 1.8153 0.040 45.450 0.000 1.733 1.897\n", "==============================================================================\n", "Omnibus: 35.744 Durbin-Watson: 2.062\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 94.065\n", "Skew: -2.585 Prob(JB): 3.75e-21\n", "Kurtosis: 9.966 Cond. No. 1.18e+03\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "[2] The condition number is large, 1.18e+03. This might indicate that there are\n", "strong multicollinearity or other numerical problems.\n" ] } ], "source": [ "reg1 = smf.ols('EXCH~PPP',data = datos)\n", "print(reg1.fit().summary())" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "plt.plot(np.arange(0,30),reg1.fit().resid,'-o')" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: np.log(EXCH) R-squared: 0.983\n", "Model: OLS Adj. R-squared: 0.983\n", "Method: Least Squares F-statistic: 1655.\n", "Date: Wed, 18 Sep 2024 Prob (F-statistic): 1.87e-26\n", "Time: 05:32:18 Log-Likelihood: -7.4056\n", "No. Observations: 30 AIC: 18.81\n", "Df Residuals: 28 BIC: 21.61\n", "Df Model: 1 \n", "Covariance Type: nonrobust \n", "===============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "-------------------------------------------------------------------------------\n", "Intercept 0.3436 0.086 3.990 0.000 0.167 0.520\n", "np.log(PPP) 1.0023 0.025 40.688 0.000 0.952 1.053\n", "==============================================================================\n", "Omnibus: 2.829 Durbin-Watson: 1.629\n", "Prob(Omnibus): 0.243 Jarque-Bera (JB): 1.449\n", "Skew: -0.179 Prob(JB): 0.485\n", "Kurtosis: 1.985 Cond. No. 5.38\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "reg3 = smf.ols('np.log(EXCH)~np.log(PPP)',data = datos)\n", "print(reg3.fit().summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "La PPA sostiene que con una unidad de moneda debe ser posible comprar la misma canasta de bienes en todos los países." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Práctica: Sueño" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "uu = \"https://raw.githubusercontent.com/vmoprojs/DataLectures/master/WO/sleep75.csv\"\n", "\n", "datos = pd.read_csv(uu,sep = \",\",header=None)\n", "datos.columns\n", "datos.columns = [\"age\",\"black\",\"case\",\"clerical\",\"construc\",\"educ\",\"earns74\",\"gdhlth\",\"inlf\", \"leis1\", \"leis2\", \"leis3\", \"smsa\", \"lhrwage\", \"lothinc\", \"male\", \"marr\", \"prot\", \"rlxall\", \"selfe\", \"sleep\", \"slpnaps\", \"south\", \"spsepay\", \"spwrk75\", \"totwrk\" , \"union\" , \"worknrm\" , \"workscnd\", \"exper\" , \"yngkid\",\"yrsmarr\", \"hrwage\", \"agesq\"]" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#totwrk: minutos trabajados por semana\n", "# sleep: minutos dormidos por semana\n", "plt.figure()\n", "plt.scatter(datos['totwrk'],datos['sleep'])" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: sleep R-squared: 0.103\n", "Model: OLS Adj. R-squared: 0.102\n", "Method: Least Squares F-statistic: 81.09\n", "Date: Wed, 18 Sep 2024 Prob (F-statistic): 1.99e-18\n", "Time: 05:32:18 Log-Likelihood: -5267.1\n", "No. Observations: 706 AIC: 1.054e+04\n", "Df Residuals: 704 BIC: 1.055e+04\n", "Df Model: 1 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "Intercept 3586.3770 38.912 92.165 0.000 3509.979 3662.775\n", "totwrk -0.1507 0.017 -9.005 0.000 -0.184 -0.118\n", "==============================================================================\n", "Omnibus: 68.651 Durbin-Watson: 1.955\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 192.044\n", "Skew: -0.483 Prob(JB): 1.99e-42\n", "Kurtosis: 5.365 Cond. No. 5.71e+03\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "[2] The condition number is large, 5.71e+03. This might indicate that there are\n", "strong multicollinearity or other numerical problems.\n" ] } ], "source": [ "dormir = smf.ols('sleep~totwrk',data = datos)\n", "print(dormir.fit().summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Para acceder a elementos de la estimación" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Intercept 38.912427\n", "totwrk 0.016740\n", "dtype: float64\n", "Intercept 3586.376952\n", "totwrk -0.150746\n", "dtype: float64\n" ] } ], "source": [ "print(dormir.fit().bse)\n", "print(dormir.fit().params)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Intervalo de confianza para $\\beta_2$ y veamos los residuos" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([-0.18422633, -0.11726532])" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dormir.fit().params[1]+(-2*dormir.fit().bse[1],2*dormir.fit().bse[1])" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "plt.hist(dormir.fit().resid,bins = 60,density = True);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Transformaciones lineales" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: Cellphone R-squared: 0.626\n", "Model: OLS Adj. R-squared: 0.615\n", "Method: Least Squares F-statistic: 53.67\n", "Date: Wed, 18 Sep 2024 Prob (F-statistic): 2.50e-08\n", "Time: 05:32:19 Log-Likelihood: -148.94\n", "No. Observations: 34 AIC: 301.9\n", "Df Residuals: 32 BIC: 304.9\n", "Df Model: 1 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "Intercept 12.4795 6.109 2.043 0.049 0.037 24.922\n", "Pcapincome 0.0023 0.000 7.326 0.000 0.002 0.003\n", "==============================================================================\n", "Omnibus: 1.398 Durbin-Watson: 2.381\n", "Prob(Omnibus): 0.497 Jarque-Bera (JB): 0.531\n", "Skew: 0.225 Prob(JB): 0.767\n", "Kurtosis: 3.414 Cond. No. 3.46e+04\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "[2] The condition number is large, 3.46e+04. This might indicate that there are\n", "strong multicollinearity or other numerical problems.\n" ] } ], "source": [ "uu = \"https://raw.githubusercontent.com/vmoprojs/DataLectures/master/GA/Table%2031_3.csv\"\n", "\n", "datos = pd.read_csv(uu, sep =';')\n", "\n", "reg_1 = smf.ols('Cellphone~Pcapincome',data = datos)\n", "print(reg_1.fit().summary())" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Cellphone')" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "plt.plot(datos.Pcapincome,datos.Cellphone,'o')\n", "plt.plot(datos.Pcapincome,reg_1.fit().fittedvalues,'-',color='r')\n", "plt.xlabel('Pcapincome')\n", "plt.ylabel('Cellphone')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Modelo reciproco" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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CMFLRPGNPTFR
count64.00000064.00000064.00000064.000000
mean141.50000051.1875001401.2500005.549688
std75.97806726.0078592725.6957751.508993
min12.0000009.000000120.0000001.690000
25%82.00000029.000000300.0000004.607500
50%138.50000048.000000620.0000006.040000
75%192.50000077.2500001317.5000006.615000
max312.00000095.00000019830.0000008.490000
\n", "
" ], "text/plain": [ " CM FLR PGNP TFR\n", "count 64.000000 64.000000 64.000000 64.000000\n", "mean 141.500000 51.187500 1401.250000 5.549688\n", "std 75.978067 26.007859 2725.695775 1.508993\n", "min 12.000000 9.000000 120.000000 1.690000\n", "25% 82.000000 29.000000 300.000000 4.607500\n", "50% 138.500000 48.000000 620.000000 6.040000\n", "75% 192.500000 77.250000 1317.500000 6.615000\n", "max 312.000000 95.000000 19830.000000 8.490000" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "uu = \"https://raw.githubusercontent.com/vmoprojs/DataLectures/master/GA/tabla_6_4.csv\"\n", "datos = pd.read_csv(uu,sep = ';')\n", "datos.describe()" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'CM')" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "plt.plot(datos.PGNP,datos.CM,'o')\n", "plt.xlabel('PGNP')\n", "plt.ylabel('CM')" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: CM R-squared: 0.166\n", "Model: OLS Adj. R-squared: 0.153\n", "Method: Least Squares F-statistic: 12.36\n", "Date: Wed, 18 Sep 2024 Prob (F-statistic): 0.000826\n", "Time: 05:32:19 Log-Likelihood: -361.64\n", "No. Observations: 64 AIC: 727.3\n", "Df Residuals: 62 BIC: 731.6\n", "Df Model: 1 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "Intercept 157.4244 9.846 15.989 0.000 137.743 177.105\n", "PGNP -0.0114 0.003 -3.516 0.001 -0.018 -0.005\n", "==============================================================================\n", "Omnibus: 3.321 Durbin-Watson: 1.931\n", "Prob(Omnibus): 0.190 Jarque-Bera (JB): 2.545\n", "Skew: 0.345 Prob(JB): 0.280\n", "Kurtosis: 2.309 Cond. No. 3.43e+03\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "[2] The condition number is large, 3.43e+03. This might indicate that there are\n", "strong multicollinearity or other numerical problems.\n" ] } ], "source": [ "reg1 = smf.ols('CM~PGNP',data = datos)\n", "print(reg1.fit().summary())" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: CM R-squared: 0.459\n", "Model: OLS Adj. R-squared: 0.450\n", "Method: Least Squares F-statistic: 52.61\n", "Date: Wed, 18 Sep 2024 Prob (F-statistic): 7.82e-10\n", "Time: 05:32:20 Log-Likelihood: -347.79\n", "No. Observations: 64 AIC: 699.6\n", "Df Residuals: 62 BIC: 703.9\n", "Df Model: 1 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "Intercept 81.7944 10.832 7.551 0.000 60.141 103.447\n", "RepPGNP 2.727e+04 3759.999 7.254 0.000 1.98e+04 3.48e+04\n", "==============================================================================\n", "Omnibus: 0.147 Durbin-Watson: 1.959\n", "Prob(Omnibus): 0.929 Jarque-Bera (JB): 0.334\n", "Skew: 0.065 Prob(JB): 0.846\n", "Kurtosis: 2.671 Cond. No. 534.\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "datos['RepPGNP'] = 1/datos.PGNP\n", "\n", "reg2 = smf.ols('CM~RepPGNP',data = datos)\n", "print(reg2.fit().summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Modelo log-lineal" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: lsalary R-squared: 0.013\n", "Model: OLS Adj. R-squared: 0.008\n", "Method: Least Squares F-statistic: 2.334\n", "Date: Wed, 18 Sep 2024 Prob (F-statistic): 0.128\n", "Time: 05:32:20 Log-Likelihood: -160.84\n", "No. Observations: 177 AIC: 325.7\n", "Df Residuals: 175 BIC: 332.0\n", "Df Model: 1 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "Intercept 6.5055 0.068 95.682 0.000 6.371 6.640\n", "ceoten 0.0097 0.006 1.528 0.128 -0.003 0.022\n", "==============================================================================\n", "Omnibus: 3.858 Durbin-Watson: 2.084\n", "Prob(Omnibus): 0.145 Jarque-Bera (JB): 3.907\n", "Skew: -0.189 Prob(JB): 0.142\n", "Kurtosis: 3.622 Cond. No. 16.1\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "uu = \"https://raw.githubusercontent.com/vmoprojs/DataLectures/master/WO/ceosal2.csv\"\n", "\n", "datos = pd.read_csv(uu,header = None)\n", "datos.columns = [\"salary\", \"age\", \"college\", \"grad\", \"comten\", \"ceoten\", \"sales\", \"profits\",\"mktval\", \"lsalary\", \"lsales\", \"lmktval\", \"comtensq\", \"ceotensq\", \"profmarg\"]\n", "datos.head()\n", "\n", "\n", "reg1 = smf.ols('lsalary~ceoten',data = datos)\n", "print(reg1.fit().summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Regresión a través del origen" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "=======================================================================================\n", "Dep. Variable: Y R-squared (uncentered): 0.502\n", "Model: OLS Adj. R-squared (uncentered): 0.500\n", "Method: Least Squares F-statistic: 241.2\n", "Date: Wed, 18 Sep 2024 Prob (F-statistic): 4.41e-38\n", "Time: 05:32:21 Log-Likelihood: -751.30\n", "No. Observations: 240 AIC: 1505.\n", "Df Residuals: 239 BIC: 1508.\n", "Df Model: 1 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "X 1.1555 0.074 15.532 0.000 1.009 1.302\n", "==============================================================================\n", "Omnibus: 9.576 Durbin-Watson: 1.973\n", "Prob(Omnibus): 0.008 Jarque-Bera (JB): 13.569\n", "Skew: -0.268 Prob(JB): 0.00113\n", "Kurtosis: 4.034 Cond. No. 1.00\n", "==============================================================================\n", "\n", "Notes:\n", "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n", "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "uu = \"https://raw.githubusercontent.com/vmoprojs/DataLectures/master/GA/Table%206_1.csv\"\n", "datos = pd.read_csv(uu,sep = ';')\n", "datos.head()\n", "\n", "\n", "lmod1 = smf.ols('Y~ -1+X',data = datos)\n", "print(lmod1.fit().summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Regresión Lineal Múltiple" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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priceassessbdrmslotsizesqrftcoloniallpricelassessllotsizelsqrft
count88.00000088.00000088.00000088.00000088.00000088.00000088.00000088.00000088.00000088.000000
mean293.546034315.7363643.5681829019.8636362013.6931820.6931825.6331805.7179948.9051057.572610
std102.71344595.3144370.84139310174.150414577.1915830.4638160.3035730.2621130.5440600.258688
min111.000000198.7000002.0000001000.0000001171.0000000.0000004.7095305.2917966.9077557.065613
25%230.000000253.9000003.0000005732.7500001660.5000000.0000005.4380795.5369408.6539087.414873
50%265.500000290.2000003.0000006430.0000001845.0000001.0000005.5816135.6705668.7687197.520231
75%326.250000352.1250004.0000008583.2500002227.0000001.0000005.7876425.8639829.0575677.708266
max725.000000708.6000007.00000092681.0000003880.0000001.0000006.5861726.56329111.4369208.263591
\n", "
" ], "text/plain": [ " price assess bdrms lotsize sqrft \\\n", "count 88.000000 88.000000 88.000000 88.000000 88.000000 \n", "mean 293.546034 315.736364 3.568182 9019.863636 2013.693182 \n", "std 102.713445 95.314437 0.841393 10174.150414 577.191583 \n", "min 111.000000 198.700000 2.000000 1000.000000 1171.000000 \n", "25% 230.000000 253.900000 3.000000 5732.750000 1660.500000 \n", "50% 265.500000 290.200000 3.000000 6430.000000 1845.000000 \n", "75% 326.250000 352.125000 4.000000 8583.250000 2227.000000 \n", "max 725.000000 708.600000 7.000000 92681.000000 3880.000000 \n", "\n", " colonial lprice lassess llotsize lsqrft \n", "count 88.000000 88.000000 88.000000 88.000000 88.000000 \n", "mean 0.693182 5.633180 5.717994 8.905105 7.572610 \n", "std 0.463816 0.303573 0.262113 0.544060 0.258688 \n", "min 0.000000 4.709530 5.291796 6.907755 7.065613 \n", "25% 0.000000 5.438079 5.536940 8.653908 7.414873 \n", "50% 1.000000 5.581613 5.670566 8.768719 7.520231 \n", "75% 1.000000 5.787642 5.863982 9.057567 7.708266 \n", "max 1.000000 6.586172 6.563291 11.436920 8.263591 " ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "uu = \"https://raw.githubusercontent.com/vmoprojs/DataLectures/master/WO/hprice1.csv\"\n", "datos = pd.read_csv(uu,header=None)\n", "datos.columns = [\"price\" , \"assess\" , \n", " \"bdrms\" , \"lotsize\" ,\n", " \"sqrft\" , \"colonial\",\n", " \"lprice\" , \"lassess\" ,\n", " \"llotsize\" , \"lsqrft\"]\n", "\n", "datos.describe()\n" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: lprice R-squared: 0.773\n", "Model: OLS Adj. R-squared: 0.762\n", "Method: Least Squares F-statistic: 70.58\n", "Date: Wed, 18 Sep 2024 Prob (F-statistic): 6.45e-26\n", "Time: 05:32:21 Log-Likelihood: 45.750\n", "No. Observations: 88 AIC: -81.50\n", "Df Residuals: 83 BIC: -69.11\n", "Df Model: 4 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "Intercept 0.2637 0.570 0.463 0.645 -0.869 1.397\n", "lassess 1.0431 0.151 6.887 0.000 0.742 1.344\n", "llotsize 0.0074 0.039 0.193 0.848 -0.069 0.084\n", "lsqrft -0.1032 0.138 -0.746 0.458 -0.379 0.172\n", "bdrms 0.0338 0.022 1.531 0.129 -0.010 0.078\n", "==============================================================================\n", "Omnibus: 14.527 Durbin-Watson: 2.048\n", "Prob(Omnibus): 0.001 Jarque-Bera (JB): 56.436\n", "Skew: 0.118 Prob(JB): 5.56e-13\n", "Kurtosis: 6.916 Cond. No. 501.\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "\n", "modelo1 = smf.ols('lprice~lassess+llotsize+lsqrft+bdrms',data = datos)\n", "print(modelo1.fit().summary())" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: lprice R-squared: 0.643\n", "Model: OLS Adj. R-squared: 0.630\n", "Method: Least Squares F-statistic: 50.42\n", "Date: Wed, 18 Sep 2024 Prob (F-statistic): 9.74e-19\n", "Time: 05:32:21 Log-Likelihood: 25.861\n", "No. Observations: 88 AIC: -43.72\n", "Df Residuals: 84 BIC: -33.81\n", "Df Model: 3 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "Intercept -1.2970 0.651 -1.992 0.050 -2.592 -0.002\n", "llotsize 0.1680 0.038 4.388 0.000 0.092 0.244\n", "lsqrft 0.7002 0.093 7.540 0.000 0.516 0.885\n", "bdrms 0.0370 0.028 1.342 0.183 -0.018 0.092\n", "==============================================================================\n", "Omnibus: 12.060 Durbin-Watson: 2.089\n", "Prob(Omnibus): 0.002 Jarque-Bera (JB): 34.890\n", "Skew: -0.188 Prob(JB): 2.65e-08\n", "Kurtosis: 6.062 Cond. No. 410.\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "modelo2 = smf.ols('lprice~llotsize+lsqrft+bdrms',data = datos)\n", "print(modelo2.fit().summary())" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: lprice R-squared: 0.215\n", "Model: OLS Adj. R-squared: 0.206\n", "Method: Least Squares F-statistic: 23.53\n", "Date: Wed, 18 Sep 2024 Prob (F-statistic): 5.43e-06\n", "Time: 05:32:21 Log-Likelihood: -8.8147\n", "No. Observations: 88 AIC: 21.63\n", "Df Residuals: 86 BIC: 26.58\n", "Df Model: 1 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "Intercept 5.0365 0.126 39.862 0.000 4.785 5.288\n", "bdrms 0.1672 0.034 4.851 0.000 0.099 0.236\n", "==============================================================================\n", "Omnibus: 7.476 Durbin-Watson: 2.056\n", "Prob(Omnibus): 0.024 Jarque-Bera (JB): 13.085\n", "Skew: -0.182 Prob(JB): 0.00144\n", "Kurtosis: 4.854 Cond. No. 17.2\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "modelo3 = smf.ols('lprice~bdrms',data = datos)\n", "print(modelo3.fit().summary())" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import seaborn as sns\n", "\n", "sns.pairplot(datos.loc[:,['lprice','llotsize' , 'lsqrft' , 'bdrms']])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Predicción" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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meanmean_semean_ci_lowermean_ci_upperobs_ci_lowerobs_ci_upper
05.7765770.0291855.7185415.8346145.4049166.148239
15.7077400.0293065.6494635.7660185.3360416.079440
25.3105430.0333845.2441565.3769304.9374865.683600
35.3266810.0318185.2634075.3899554.9541655.699197
45.7972200.0310145.7355445.8588955.4249726.169467
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" ], "text/plain": [ " mean mean_se mean_ci_lower mean_ci_upper obs_ci_lower \\\n", "0 5.776577 0.029185 5.718541 5.834614 5.404916 \n", "1 5.707740 0.029306 5.649463 5.766018 5.336041 \n", "2 5.310543 0.033384 5.244156 5.376930 4.937486 \n", "3 5.326681 0.031818 5.263407 5.389955 4.954165 \n", "4 5.797220 0.031014 5.735544 5.858895 5.424972 \n", "\n", " obs_ci_upper \n", "0 6.148239 \n", "1 6.079440 \n", "2 5.683600 \n", "3 5.699197 \n", "4 6.169467 " ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "datos_nuevos = pd.DataFrame({'llotsize':np.log(2100),'lsqrft':np.log(8000),'bdrms':4},index = [0])\n", "\n", "pred_vals = modelo2.fit().predict()\n", "modelo2.fit().get_prediction().summary_frame().head()" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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meanmean_semean_ci_lowermean_ci_upperobs_ci_lowerobs_ci_upper
06.4288110.1479756.1345466.7230765.9583266.899296
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" ], "text/plain": [ " mean mean_se mean_ci_lower mean_ci_upper obs_ci_lower \\\n", "0 6.428811 0.147975 6.134546 6.723076 5.958326 \n", "\n", " obs_ci_upper \n", "0 6.899296 " ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pred_vals = modelo2.fit().get_prediction(datos_nuevos)\n", "pred_vals.summary_frame()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### RLM: Educación con insumos" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: colGPA R-squared: 0.050\n", "Model: OLS Adj. R-squared: 0.043\n", "Method: Least Squares F-statistic: 7.314\n", "Date: Wed, 18 Sep 2024 Prob (F-statistic): 0.00770\n", "Time: 05:32:25 Log-Likelihood: -56.641\n", "No. Observations: 141 AIC: 117.3\n", "Df Residuals: 139 BIC: 123.2\n", "Df Model: 1 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "Intercept 2.9894 0.040 75.678 0.000 2.911 3.068\n", "PC 0.1695 0.063 2.704 0.008 0.046 0.293\n", "==============================================================================\n", "Omnibus: 2.136 Durbin-Watson: 1.941\n", "Prob(Omnibus): 0.344 Jarque-Bera (JB): 1.852\n", "Skew: 0.160 Prob(JB): 0.396\n", "Kurtosis: 2.539 Cond. No. 2.45\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "uu = \"https://raw.githubusercontent.com/vmoprojs/DataLectures/master/WO/gpa1.csv\"\n", "datosgpa = pd.read_csv(uu, header = None)\n", "datosgpa.columns = [\"age\", \"soph\", \"junior\", \"senior\", \"senior5\", \"male\", \"campus\", \"business\", \"engineer\", \"colGPA\", \"hsGPA\", \"ACT\", \"job19\", \"job20\", \"drive\", \"bike\", \"walk\", \"voluntr\", \"PC\", \"greek\", \"car\", \"siblings\", \"bgfriend\", \"clubs\", \"skipped\", \"alcohol\", \"gradMI\", \"fathcoll\", \"mothcoll\"]\n", "\n", "reg4 = smf.ols('colGPA ~ PC', data = datosgpa)\n", "print(reg4.fit().summary())" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: colGPA R-squared: 0.219\n", "Model: OLS Adj. R-squared: 0.202\n", "Method: Least Squares F-statistic: 12.83\n", "Date: Wed, 18 Sep 2024 Prob (F-statistic): 1.93e-07\n", "Time: 05:32:25 Log-Likelihood: -42.796\n", "No. Observations: 141 AIC: 93.59\n", "Df Residuals: 137 BIC: 105.4\n", "Df Model: 3 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "Intercept 1.2635 0.333 3.793 0.000 0.605 1.922\n", "PC 0.1573 0.057 2.746 0.007 0.044 0.271\n", "hsGPA 0.4472 0.094 4.776 0.000 0.262 0.632\n", "ACT 0.0087 0.011 0.822 0.413 -0.012 0.029\n", "==============================================================================\n", "Omnibus: 2.770 Durbin-Watson: 1.870\n", "Prob(Omnibus): 0.250 Jarque-Bera (JB): 1.863\n", "Skew: 0.016 Prob(JB): 0.394\n", "Kurtosis: 2.438 Cond. No. 298.\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "reg5 = smf.ols('colGPA ~ PC + hsGPA + ACT', data = datosgpa)\n", "print(reg5.fit().summary())" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "celltoolbar": "Raw Cell Format", "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": "3.9.19" } }, "nbformat": 4, "nbformat_minor": 4 }