1. Courses/21. Statistical Thinking in Python (Part 1)/Statistical Thinking in Python (Part 1).ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Statistical Thinking in Python (Part 1).ipynb","provenance":[],"collapsed_sections":["7dKQRcqeCRe2"],"authorship_tag":"ABX9TyOd2Kh5QqotyZ7GlweebUuQ"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","metadata":{"id":"qhiZVshg7SEX"},"source":[""]},{"cell_type":"markdown","metadata":{"id":"7nPA9pI_B0n4"},"source":["# Statistical Thinking in Python (Part 1)\n","\n","---\n","\n","***Pensamiento estadístico en Python (Parte 1)*** \n","\n","---\n","\n","Después de todo el arduo trabajo de adquirir datos y convertirlos en una forma con la que pueda trabajar, en última instancia, desea sacar conclusiones claras y concisas a partir de ellos. Este último paso crucial de un proceso de análisis de datos depende de los principios de la inferencia estadística. En este curso, comenzará a construir la base que necesita para pensar estadísticamente, hablar el idioma de sus datos y comprender lo que le dicen sus datos. Los cimientos del pensamiento estadístico tardaron décadas en construirse, pero hoy en día se pueden comprender mucho más rápido con la ayuda de las computadoras. Con el poder de las herramientas basadas en Python, rápidamente se pondrá al día y comenzará a pensar estadísticamente al final de este curso."]},{"cell_type":"code","metadata":{"id":"OhF0bKiOS5_v"},"source":["import numpy as np\n","import pandas as pd\n","df_swing = pd.read_csv('https://raw.githubusercontent.com/MayumyCH/data-scientist-with-python-datacamp/main/1.%20Courses/21.%20Statistical%20Thinking%20in%20Python%20(Part%201)/dataset/2008_swing_states.csv')\n","belmont = pd.read_csv('https://raw.githubusercontent.com/MayumyCH/data-scientist-with-python-datacamp/main/1.%20Courses/21.%20Statistical%20Thinking%20in%20Python%20(Part%201)/dataset/belmont.csv')"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"6qBZgje8CMBp"},"source":["## 1. Graphical Exploratory Data Analysis"]},{"cell_type":"markdown","metadata":{"id":"-nrVSiNKCpZg"},"source":["\n","**(Análisis de datos exploratorios gráficos)**\n","\n","Antes de sumergirse en técnicas sofisticadas de inferencia estadística, primero debe explorar sus datos trazándolos y calculando estadísticas de resumen simples. Este proceso, llamado análisis exploratorio de datos, es un primer paso crucial en el análisis estadístico de datos. "]},{"cell_type":"code","metadata":{"id":"Dz8KdlFSCysE","colab":{"base_uri":"https://localhost:8080/","height":143},"executionInfo":{"status":"ok","timestamp":1632865201771,"user_tz":300,"elapsed":10,"user":{"displayName":"heydy carrasco huaccha","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiGyezGgm9Px4LBw_sc7MFWfjUVhWWsvnAz1Ttg=s64","userId":"00007591481982700116"}},"outputId":"390d1731-b3ef-4c2d-d4d4-67d0bebbb9b7"},"source":["import pandas as pd\n","df_swing = pd.read_csv('https://raw.githubusercontent.com/MayumyCH/data-scientist-with-python-datacamp/main/1.%20Courses/21.%20Statistical%20Thinking%20in%20Python%20(Part%201)/dataset/2008_swing_states.csv')\n","df_swing.head(3)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>state</th>\n","      <th>county</th>\n","      <th>total_votes</th>\n","      <th>dem_votes</th>\n","      <th>rep_votes</th>\n","      <th>dem_share</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>PA</td>\n","      <td>Erie County</td>\n","      <td>127691</td>\n","      <td>75775</td>\n","      <td>50351</td>\n","      <td>60.08</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>PA</td>\n","      <td>Bradford County</td>\n","      <td>25787</td>\n","      <td>10306</td>\n","      <td>15057</td>\n","      <td>40.64</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>PA</td>\n","      <td>Tioga County</td>\n","      <td>17984</td>\n","      <td>6390</td>\n","      <td>11326</td>\n","      <td>36.07</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["  state           county  total_votes  dem_votes  rep_votes  dem_share\n","0    PA      Erie County       127691      75775      50351      60.08\n","1    PA  Bradford County        25787      10306      15057      40.64\n","2    PA     Tioga County        17984       6390      11326      36.07"]},"metadata":{},"execution_count":2}]},{"cell_type":"code","metadata":{"id":"wYkZJPQ1CypG","colab":{"base_uri":"https://localhost:8080/","height":424},"executionInfo":{"status":"ok","timestamp":1632865201771,"user_tz":300,"elapsed":7,"user":{"displayName":"heydy carrasco huaccha","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiGyezGgm9Px4LBw_sc7MFWfjUVhWWsvnAz1Ttg=s64","userId":"00007591481982700116"}},"outputId":"46c16705-6ae5-4fee-e3ac-3fbfd6179a7d"},"source":["df_swing[['state','county','dem_share']]"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>state</th>\n","      <th>county</th>\n","      <th>dem_share</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>PA</td>\n","      <td>Erie County</td>\n","      <td>60.08</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>PA</td>\n","      <td>Bradford County</td>\n","      <td>40.64</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>PA</td>\n","      <td>Tioga County</td>\n","      <td>36.07</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>PA</td>\n","      <td>McKean County</td>\n","      <td>41.21</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>PA</td>\n","      <td>Potter County</td>\n","      <td>31.04</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>217</th>\n","      <td>OH</td>\n","      <td>Hamilton County</td>\n","      <td>53.53</td>\n","    </tr>\n","    <tr>\n","      <th>218</th>\n","      <td>OH</td>\n","      <td>Highland County</td>\n","      <td>36.54</td>\n","    </tr>\n","    <tr>\n","      <th>219</th>\n","      <td>OH</td>\n","      <td>Hocking County</td>\n","      <td>49.58</td>\n","    </tr>\n","    <tr>\n","      <th>220</th>\n","      <td>OH</td>\n","      <td>Licking County</td>\n","      <td>41.97</td>\n","    </tr>\n","    <tr>\n","      <th>221</th>\n","      <td>OH</td>\n","      <td>Madison County</td>\n","      <td>38.11</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>222 rows × 3 columns</p>\n","</div>"],"text/plain":["    state           county  dem_share\n","0      PA      Erie County      60.08\n","1      PA  Bradford County      40.64\n","2      PA     Tioga County      36.07\n","3      PA    McKean County      41.21\n","4      PA    Potter County      31.04\n","..    ...              ...        ...\n","217    OH  Hamilton County      53.53\n","218    OH  Highland County      36.54\n","219    OH   Hocking County      49.58\n","220    OH   Licking County      41.97\n","221    OH   Madison County      38.11\n","\n","[222 rows x 3 columns]"]},"metadata":{},"execution_count":3}]},{"cell_type":"markdown","metadata":{"id":"3F4mmq3hOBy9"},"source":["### Introduction to Exploratory Data Analysis"]},{"cell_type":"markdown","metadata":{"id":"8934t7naODJ8"},"source":["### Plotting a histogram"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"xnfEPCzrxXzW","executionInfo":{"status":"ok","timestamp":1632865201771,"user_tz":300,"elapsed":7,"user":{"displayName":"heydy carrasco huaccha","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiGyezGgm9Px4LBw_sc7MFWfjUVhWWsvnAz1Ttg=s64","userId":"00007591481982700116"}},"outputId":"a7f3d99b-3d04-4c95-e3c3-036d4096ead7"},"source":["df_swing"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>state</th>\n","      <th>county</th>\n","      <th>total_votes</th>\n","      <th>dem_votes</th>\n","      <th>rep_votes</th>\n","      <th>dem_share</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>PA</td>\n","      <td>Erie County</td>\n","      <td>127691</td>\n","      <td>75775</td>\n","      <td>50351</td>\n","      <td>60.08</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>PA</td>\n","      <td>Bradford County</td>\n","      <td>25787</td>\n","      <td>10306</td>\n","      <td>15057</td>\n","      <td>40.64</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>PA</td>\n","      <td>Tioga County</td>\n","      <td>17984</td>\n","      <td>6390</td>\n","      <td>11326</td>\n","      <td>36.07</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>PA</td>\n","      <td>McKean County</td>\n","      <td>15947</td>\n","      <td>6465</td>\n","      <td>9224</td>\n","      <td>41.21</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>PA</td>\n","      <td>Potter County</td>\n","      <td>7507</td>\n","      <td>2300</td>\n","      <td>5109</td>\n","      <td>31.04</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","    </tr>\n","    <tr>\n","      <th>217</th>\n","      <td>OH</td>\n","      <td>Hamilton County</td>\n","      <td>425086</td>\n","      <td>225213</td>\n","      <td>195530</td>\n","      <td>53.53</td>\n","    </tr>\n","    <tr>\n","      <th>218</th>\n","      <td>OH</td>\n","      <td>Highland County</td>\n","      <td>19186</td>\n","      <td>6856</td>\n","      <td>11907</td>\n","      <td>36.54</td>\n","    </tr>\n","    <tr>\n","      <th>219</th>\n","      <td>OH</td>\n","      <td>Hocking County</td>\n","      <td>12961</td>\n","      <td>6259</td>\n","      <td>6364</td>\n","      <td>49.58</td>\n","    </tr>\n","    <tr>\n","      <th>220</th>\n","      <td>OH</td>\n","      <td>Licking County</td>\n","      <td>82356</td>\n","      <td>33932</td>\n","      <td>46918</td>\n","      <td>41.97</td>\n","    </tr>\n","    <tr>\n","      <th>221</th>\n","      <td>OH</td>\n","      <td>Madison County</td>\n","      <td>17454</td>\n","      <td>6532</td>\n","      <td>10606</td>\n","      <td>38.11</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>222 rows × 6 columns</p>\n","</div>"],"text/plain":["    state           county  total_votes  dem_votes  rep_votes  dem_share\n","0      PA      Erie County       127691      75775      50351      60.08\n","1      PA  Bradford County        25787      10306      15057      40.64\n","2      PA     Tioga County        17984       6390      11326      36.07\n","3      PA    McKean County        15947       6465       9224      41.21\n","4      PA    Potter County         7507       2300       5109      31.04\n","..    ...              ...          ...        ...        ...        ...\n","217    OH  Hamilton County       425086     225213     195530      53.53\n","218    OH  Highland County        19186       6856      11907      36.54\n","219    OH   Hocking County        12961       6259       6364      49.58\n","220    OH   Licking County        82356      33932      46918      41.97\n","221    OH   Madison County        17454       6532      10606      38.11\n","\n","[222 rows x 6 columns]"]},"metadata":{},"execution_count":4}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":302},"id":"Jytl2MmdQC42","executionInfo":{"status":"ok","timestamp":1632865202408,"user_tz":300,"elapsed":643,"user":{"displayName":"heydy carrasco huaccha","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiGyezGgm9Px4LBw_sc7MFWfjUVhWWsvnAz1Ttg=s64","userId":"00007591481982700116"}},"outputId":"b193b2da-076e-49b9-8452-059294a4623a"},"source":["# Import plotting modules\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","import numpy as np\n","\n","# Set default Seaborn style\n","sns.set()\n","\n","# Compute number of data points: n_data\n","n_data = len(df_swing['dem_share'])\n","\n","# Number of bins is the square root of number of data points: n_bins\n","n_bins= np.sqrt(n_data)\n","\n","# Convert number of bins to integer: n_bins\n","n_bins = int(n_bins)\n","print(n_bins)\n","\n","# Plot histogram of versicolor petal lengths\n","_= plt.hist(df_swing['dem_share'],bins=n_bins)\n","_= plt.xlabel('percent of vote for Obama')\n","_= plt.ylabel('number of counties')\n","\n","# Show histogram\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["14\n"]},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"_SdfGNr8OI62"},"source":["### Plot all of your data: Bee swarm plots"]},{"cell_type":"markdown","metadata":{"id":"_jjdm2ReY1rp"},"source":["**DIAGRAMA DE ENGRANAJE**"]},{"cell_type":"code","metadata":{"id":"hSy8_6BaTk9e"},"source":["# sns.swarmplot?\n","# help(sns.swarmplot)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":285},"id":"938qvkoBaHoH","executionInfo":{"status":"ok","timestamp":1632865203578,"user_tz":300,"elapsed":1172,"user":{"displayName":"heydy carrasco huaccha","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiGyezGgm9Px4LBw_sc7MFWfjUVhWWsvnAz1Ttg=s64","userId":"00007591481982700116"}},"outputId":"4c045b7d-54b4-4a6a-cc52-ce43a3083791"},"source":["_ = sns.swarmplot(x='state', y='dem_share', data=df_swing)\n","_ = plt.xlabel('state')\n","_ = plt.ylabel('percent of vote for Obama')\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"rsiwot0TOWhS"},"source":["### Plot all of your data: ECDFs"]},{"cell_type":"markdown","metadata":{"id":"9NaNF14ldYe6"},"source":["**ECDF'S <> FUNCION DE DISTRIBUCION ACUMULATIVA EMPIRICA**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":285},"id":"ZkwrSSR-OWMj","executionInfo":{"status":"ok","timestamp":1632865203579,"user_tz":300,"elapsed":12,"user":{"displayName":"heydy carrasco huaccha","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiGyezGgm9Px4LBw_sc7MFWfjUVhWWsvnAz1Ttg=s64","userId":"00007591481982700116"}},"outputId":"2c5fb3f4-02d5-4acb-ee1b-04afb75287b7"},"source":["x = np.sort(df_swing['dem_share'])\n","y = np.arange(1, len(x)+1) / len(x)\n","_= plt.plot(x, y, marker='.', linestyle='none')\n","_= plt.xlabel('percent of vote for Obama')\n","_= plt.ylabel('ECDF')\n","plt.margins(0.02) # Keeps data off plot edges\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYkAAAEMCAYAAAAxoErWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3de1xT5/0H8E8SwPu4RMQgU+YNqVWh0FmdSkUGVFBwzkup9jdtaVetm251tZ31Ui+ra1+drbWz1dW2o7dZbxXx+rJMrcXaKYpFRVoUDBE1XBS0QJLn9weSJiQR0HBy+7xfr73WnJycfDlp88nznOc8j0wIIUBERGSF3NEFEBGR82JIEBGRTQwJIiKyiSFBREQ2MSSIiMgmhgQREdkkSUisWrUKsbGxCAsLQ0FBgdV99Ho9li5diri4OPz617/Gpk2bpCiNiIjuwEuKNxkzZgwef/xxPPbYYzb32bFjB4qLi7F3715UVlYiNTUVw4YNQ0hISKveq6KiBgaDfW79UCo7Q6uttsux2hLrtC/WaV+s077sXadcLoO/fyebz0sSEtHR0c3uk5WVhUmTJkEulyMgIABxcXHYvXs3nnzyyVa9l8Eg7BYSjcdzBazTvlinfbFO+2ptnYXqKpwrrkBYT3/07eHbqtdKEhItodFoEBwcbHysUqlw+fJlB1ZERO7O9MszMLCLo8tpE4XqKrz6yQno9AZ4KeSY/2hkq4LCaULCXpTKznY9nqv8i8M67Yt12pcz1nn2Qjle/eQE6nUGeHvJsdKvIwaEBji6rBZpzfnMPqWBXm+AEIBeb8Al7U0Mi2h5N77ThIRKpUJpaSkGDx4MwLJl0VJabbXdmoyBgV1w9eoNuxyrLbFO+2Kd9uXoOm11tew89D3qdQYAQL3OgAPflkDZydtRZbZYa89niLIjFAo5oDdAoZAjRNnR7PVyueyOP66dJiQSExOxadMmxMfHo7KyEvv378dHH33k6LKIyEUVqqtwJE+Dw3ka6A2iBV0trnE9orX69vDF/Ecj7/qahCRDYJcvX45Ro0bh8uXLmDFjBpKSkgAA6enpyMvLAwCkpKQgJCQE8fHxmDx5MmbPno2f//znUpRHRC6sUF2FnV9fQKG6ymzbq5+cQHZuKXR6YexqOVdcYdxn+CAVFAoZZAAUChlio3tKX7xE+vbwRdKw0FYHBADI3G2qcHY3OS/WaV/uXmdLRuTYuii78+sL2HLwB5h+u3l7WbYkTN9jWESIW59PW1ymu4mICGhdN9G54groTC7KniuuQN8evgjr6Q8vhRx6vQEyuQwjB6kwfJDK4hh9e/je1a9rT8KQICKHMv01D8A44qiR6Zd/U6ZhoFDIjce41354+glDgogk1xgMnTp445P9543dRb+6vzt0eoPZvqZf/k3dKQzYSrAPhgQRScI0GD7eVwCdXkAuA4RoGFekvx0OLekmMsUwaFsMCSJqc4XqKvz94+PQ6QVkt4MBAAwCkMtwe4SRHMNvhwK7iZwHQ4KI2tyunIvQ6RuSoel4yiF9u6J38M/MQoHh4DwYEkTUps5eKEfu+Wtm22S3/1+ukOGRh3oxFJwYQ4KI2kyhugqf//d7s3uZZQCmJ4ah5lY9u5RcAEOCiNpEdq4aH+0tgL7Jza0R/bri4YgeDqqKWoshQUR21Xgz3MGTpWg6+YHidvcSuQ6GBBHZja3Wg1wGjBoS3OxwVnI+DAkiuiem9z9k7C2wmDtNIZfhsfj+7GJyUQwJIrprjS0Hg0FAJpeZBURj6yFpZB+XWKeBrGNIENFdKVRXIWPPOeN1B2EQUNwOCplchmm3Ww+uMlstWceQIKK7sivnotmFabkMeCy+P4e2uhmGBBG1WnauGiea3CA3pC+HtrojhgQRtUjTC9Sm5DJwaKubYkgQUbNMJ+iTy2DWzSSTAdMSwti95KYYEkRkU2Pr4YfS68YJ+hpnbhUCZheoyT0xJIjIKtPhrcYZ+W6zNnMruSeGBBGZLSHat4evxfBWmLQeOHOrZ2FIEHk40xaDl5cc8x+NxJE8jcXw1mkJnLnVEzEkiDxY0xaDTm/AueIKi/04vNVzyR1dABE5TtMWgwxAWE9/DB+kgkIhu72sKGdu9WRsSRB5sKqaOrPHfXv4GruSnk97gGtNE0OCyJPV3Ko3e9ypw08T8ZkGBnkudjcReahCdRXOX6oy2+bbycdB1ZCzYkgQeahdORct1p4ePkjlqHLISbG7icjDNC4vmttkgr6Ifl3ZvUQWGBJEHsR0DiZTnKCPbGFIEHmQXTkXLQPi9vxLbEWQNQwJIg9hbQ2IX6i64NE4BgTZxgvXRB6g8c5qU3IZGBDULMlaEkVFRViwYAEqKyvh5+eHVatWITQ01GwfrVaLF154ARqNBjqdDkOHDsXChQvh5cUGD9G9aLrUqAxcA4JaRrKWxOLFi5GWloY9e/YgLS0NixYtsthn3bp16NOnD3bs2IEvvvgC3333Hfbu3StViURuyVo3U0Q/zsVELSNJSGi1WuTn5yM5ORkAkJycjPz8fJSXl5vtJ5PJUFNTA4PBgLq6OtTX1yMoKEiKEoncUnauGv+20s3EkUzUUpKEhEajQVBQEBQKBQBAoVCgW7du0Gg0ZvvNmjULRUVFGDFihPF/UVFRUpRI5HYar0MILjVK98CpOvt3796NsLAwfPDBB6ipqUF6ejp2796NxMTEFh9Dqexs15oCA7vY9XhthXXalzvU+W5mvsVa1LMmDkHisNC2L6wJdzifzkTKOiUJCZVKhbKyMuj1eigUCuj1ely5cgUqlfkUABkZGVi5ciXkcjm6dOmC2NhYHD16tFUhodVWNyy3aAeBgV1w9eoNuxyrLbFO+3LVOhtXl+vUwRvFl2/g6OnLZvtH9O2KqL5Kyf82Vz2fzsredcrlsjv+uJYkJJRKJcLDw5GZmYmUlBRkZmYiPDwcAQEBZvuFhITg4MGDGDx4MOrq6vD111/j17/+tRQlErk0W3dSN+J1CLpbko1uWrJkCTIyMpCQkICMjAwsXboUAJCeno68vDwAwIsvvoj//e9/GDduHFJTUxEaGorJkydLVSKRy7J2J3UjuVzG6xB01yS7JtGnTx9s2rTJYvv69euN/9yzZ09s3LhRqpKI3IK1Ia5Aw4pyIwepMHyQigFBd82pLlwTUetYG+IqAxATEcxwILtgSBC5qN1fX8C/rQxxnZ4QxhvlyG4YEkQuqLEFwYCgtsYJ/ohcDAOCpMSQIHIhtu6iZkBQW2FIELmQI3kai9lcGRDUlhgSRC6kqqbO7HG/EF8GBLUphgSRC6m5VW/2uFMHbwdVQp6CIUHkIrJz1Si4VGW2zbeTj4OqIU/BkCByAbbWhRg+SGXjFUT2wfskiJxYoboKR/I0OHiy1GJE0zMTh/COampzDAkiJ2VrZtfGIa+Jw0JdYmprcm0MCSIndSRPYxEQcrkM0+L7c0QTSYYhQeSkmg53VSk7YsbYcHYxkaR44ZrISTUd7tqlgzcDgiTHkCByUtrrP5o9vtEkNIikwJAgckKF6ipor9eabese0NFB1ZAnY0gQOaEjeRqLbVyjmhyBIUHkhEqv1Zg97h/iy+sR5BAMCSInU6iuwvkm028Ed+3koGrI0zEkiJzMrpyLML07QgZOv0GOw5AgciLZuWqcOH/NbFtEv67saiKHYUgQOYnGVedMyWW8YE2OxZAgchK7ci5arDo3LSGMrQhyKIYEkRMoVFch10o3E+doIkdjSBA5WKG6Cp/sL7C4WM1uJnIGnOCPyIFsTQfOi9XkLNiSIHKgXTkXLQJCoZCxFUFOgy0JIgexdh3iF6oueDSuP1sR5DTYkiByEGs3zTEgyNkwJIgcwNZoJgYEORuGBJEDfP5lIUczkUtgSBBJLDtXjYImE/ixFUHOSrKQKCoqwpQpU5CQkIApU6bgwoULVvfLysrCuHHjkJycjHHjxuHatWtW9yNyVYdOllpsYyuCnJVko5sWL16MtLQ0pKSkYPv27Vi0aBE+/PBDs33y8vLw1ltv4YMPPkBgYCBu3LgBHx8fqUokkoS3wvy32c8DO7EVQU5LkpaEVqtFfn4+kpOTAQDJycnIz89HeXm52X7vv/8+Zs6cicDAQABAly5d0K5dOylKJJJMpw7eZo+7+nVwUCVEzZMkJDQaDYKCgqBQKAAACoUC3bp1g0ZjvkTj999/j5KSEjz22GOYMGEC3n77bQghrB2SyGXV3Kp3dAlELeZUN9Pp9XqcO3cOGzduRF1dHZ588kkEBwcjNTW1xcdQKjvbtabAwC52PV5bYZ321VZ17v76gsVF6+5dO931+3n6+bQ31mlJkpBQqVQoKyuDXq+HQqGAXq/HlStXoFKZr7YVHByMxMRE+Pj4wMfHB2PGjMGpU6daFRJabTUMBvu0PgIDu+Dq1Rt2OVZbYp321VZ1Fqqr8M/NJ822yWVAZN+ud/V+nn4+7c1T65TLZXf8cS1Jd5NSqUR4eDgyMzMBAJmZmQgPD0dAQIDZfsnJyTh8+DCEEKivr0dOTg4GDBggRYlEbY7rRZArkmwI7JIlS5CRkYGEhARkZGRg6dKlAID09HTk5eUBAJKSkqBUKjF27Fikpqaib9+++O1vfytViURthutFkKuSCTe7MszuJuflyXWu2XzKbO1qGYAXpkfdUyvCk89nW/DUOp2iu4nIk3GeJnJlDAmiNsRV58jVOdUQWCJ3wlXnyB0025I4efJkc7sQkRVH8jRcdY5cXrMhMWPGDLPHv/nNb9qsGCJ3UlVTZ/ZYpeyI59MeYCuCXEqzIdF08FNJSUmbFUPkTppOv9E9oCMDglxOs9ckZDLZHR8TkbnsXDUOnSxFkcZ8mKJvJ85oTK6n2ZDQ6XTYvHmzsUVRX1+Pzz//3Gwf3vBG1CA7V40Pd5+z2C6XAcMHqay8gsi5NRsSQ4YMwbZt24yP77//fmzfvt34WCaTMSSIbtt3zLI7Vi6XYVp8f3Y1kUtqNiT+/e9/S1EHkcsrVFdBo71ptk2l7IgZY8MZEOSyWnyfRFVVFU6dOoWqqir4+flh0KBB8PXlv/hEjY7kaSy2MSDI1bUoJNauXYt33nkHer0e/v7+KC8vh5eXF5566ik8++yzbV0jkUtoOuS1f4gvA4JcXrMhkZWVhYyMDLz66qsYM2YMvLy8oNPpsH//fixbtgy9e/fG2LFjpaiVyGkVqqtwudy8q6npMqVErqjZkNi0aRMWLFiAhISEn17k5YXExETU1dXhs88+Y0iQR8vOVeOjvQXQN5l9mENeyR00ezPdmTNnEBMTY/W5mJgYnD171u5FEbmKQnUVMvacswgIhULGIa/kFpptSdTV1cHPz8/qc76+vqiv56Lu5LmO5GksVpuLiQjG8EEqXo8gt9BsSAgh7jgVh5utWUTUKk0vVvcL8cXjiVxyl9xHsyFx69YtxMfH2wwDTtNB9BNerCZ302xI8JoDEZHnavbC9Y8//oiCggKrzxUUFKC2ttbuRRG5iqYzvRK5m2ZDYsOGDRYT+jXasmULNmzYYPeiiFxBdq4aBZeqzLZx2Cu5m2ZDIisrC0888YTV52bMmIGdO3favSgiZ9c49NUUZ3old9RsSJSVlSEoKMjqc0FBQSgrK7N7UUTOblfORYuhr9MSwjjsldxOsyHRoUMHaDSWE5cBQGlpKTp06GD3ooicWaG6Crnnr5lti+jXFQ9H9HBQRURtp9mQiImJweuvv271uTfeeMPm3dhE7mpXzkWYDgiXAXjkoV6OKoeoTTU7BHbu3LmYMmUKxo8fj/j4eAQGBuLq1avYt28fqqur8emnn0pRJ5FTKFRX4USTVkQ/zvZKbqzZkAgMDMTWrVvx3nvv4dChQ6isrISfnx9Gjx6NGTNmcE0J8ii7ci5abAvu2skBlRBJo9mQWL58ORYuXIh58+YBaJgVdtKkScbn58yZgzVr1rRdhUROwtq1CBk4ooncW7PXJLZs2WL2+NVXXzV7/NVXX9m3IiIn1fRaBABMT+SIJnJvzYZE0zmbOKEfeSJrrYhIjmgiD9BsSDSdwI8T+pEn4ogm8lTNXpPQ6/XIyckxtiB0Op3ZY4PB0LYVEjmYrfsi2M1EnqDZkFAqlXjxxReNj/38/MweBwQEtE1lRE6CrQjyZM2GxIEDB+zyRkVFRViwYIFxCO2qVasQGhpqdd8ffvgBEyZMQFpaGp5//nm7vD/R3WArgjxds9ck7GXx4sVIS0vDnj17kJaWhkWLFlndT6/XY/HixYiLi5OqNCKrCtVV+GR/AVsR5NEkCQmtVov8/HwkJycDAJKTk5Gfn4/y8nKLfd999108/PDDNlsZRFIoVFfh7x8fR5Hmhtl2tiLI00gSEhqNBkFBQVAoFAAAhUKBbt26WUwcePbsWRw+fBi/+93vpCiLyKZdOReh05sP91YoZGxFkMdp9pqEVOrr6/HSSy/hb3/7mzFM7oZS2dmOVQGBgV3sery2wjrt5+yFcuQWml+H6N/TD+kpgzAg1LkGarjC+QRYp71JWackIaFSqVBWVga9Xg+FQgG9Xo8rV65ApfppOoOrV6+iuLgYTz31FADg+vXrEEKguroay5Yta/F7abXVMBjsc8NfYGAXXL16o/kdHYx12teBb0sgmqwV8duYPlB28naq+l3lfLJO+7J3nXK57I4/riUJCaVSifDwcGRmZiIlJQWZmZkIDw83Gz4bHByMo0ePGh+vWbMGN2/e5Ogmklxx2XWzx5zllTyZZKOblixZgoyMDCQkJCAjIwNLly4FAKSnpyMvL0+qMojuqFBdhfwi8wEVnOWVPJlk1yT69OmDTZs2WWxfv3691f3nzJnT1iURWTiSp7HoauIsr+TJJGtJELmC0ms1Zo9DAjuxq4k8GkOCyIT2+o9mj3V2GgRB5KoYEkS3FaqroL1ea7ate0BHB1VD5BwYEkS3HcnTWGzjzXPk6RgSRLc1vR7Rn0NfiRgSREBDV9P5S1Vm2zj0lYghQQTg9tBXk8cc+krUgCFBBMuuJt5lTdSAIUEeLztXjQJ2NRFZxZAgj1aorkLGnnNm2+QydjURNWJIkMdqXHnO0GQajmcmDmFXE9FtTrOeBJGUsnPV+GhvAfRN7qiO6NcVicNCXWLKaCIpMCTI4zR2MTWdcYMrzxFZYkiQxzmSp7HoYoqJCMbwQSp2MxE1wZAgj1KorsK5kkqzbf1CfPF44gAHVUTk3BgS5DEK1VX4+8fHodOb9zNxuCuRbRzdRB7jSJ7GIiAUChmHuxLdAVsS5DGqaurMHquUHTFjbDivQxDdAVsS5DGuVd4ye9ylgzcDgqgZDAnyCNm5apRcNZ+fqV5vcFA1RK6DIUFuz9rUGwAwckiwA6ohci0MCXJ7Te+LAIBHhvbEwxE9HFMQkQthSJDbs7bi3KTRfR1UDZFrYUiQW+OKc0T3hiFBbm1XzkWuOEd0D3ifBLmlQnUVjuRpkHv+mtn2iH5dOeyVqBUYEuR2bE0DLpeBs7wStRJDgtyKrWnA5XIZpsX3ZyuCqJUYEuRWOA04kX0xJMitNB3uymnAie4NRzeR28jOVaOAw12J7IohQW7B2tQbchmHuxLdK4YEuQVr1yKmJYTxOgTRPZLsmkRRUREWLFiAyspK+Pn5YdWqVQgNDTXbZ+3atcjKyoJcLoe3tzfmzZuHkSNHSlUiubCma0X0C/Hl3ExEdiBZSCxevBhpaWlISUnB9u3bsWjRInz44Ydm+wwePBgzZ85Ehw4dcPbsWUybNg2HDx9G+/btpSqTXFTTtSI6dfB2UCVE7kWS7iatVov8/HwkJycDAJKTk5Gfn4/y8nKz/UaOHIkOHToAAMLCwiCEQGVlpcXxiExZWyvCt5OPg6ohci+ShIRGo0FQUBAUCgUAQKFQoFu3btBoNDZfs23bNvTs2RPdu3eXokRyYfuOlVhs4wVrIvtwyvskvvnmG7zxxht47733Wv1apbKzXWsJDOxi1+O1FU+tc/fXF6DR3jTbNrB3AIZFhNzTcT31fLYV1mlfUtYpSUioVCqUlZVBr9dDoVBAr9fjypUrUKksf+2dOHEC8+fPx9tvv43evXu3+r202moYms7JcJcCA7vg6tUbdjlWW/LUOrNz1fh3k2GvMgApv/rFPb2Pp57PtsI67cvedcrlsjv+uJaku0mpVCI8PByZmZkAgMzMTISHhyMgIMBsv1OnTmHevHl48803MXDgQClKIxfVGBDCdNirDJieyGGvRPYk2X0SS5YsQUZGBhISEpCRkYGlS5cCANLT05GXlwcAWLp0KX788UcsWrQIKSkpSElJwblzlmsTk2ezGRAJYRz2SmRnkl2T6NOnDzZt2mSxff369cZ/3rx5s1TlkAtqXCPi4MlSBgSRRJzywjVRU4XqKvz94+PQ6c2vNzEgiNoWQ4KcXqG6Cp/sL7AIiMY1IhgQRG2HIUFOzVYL4heqLng0josIEbU1hgQ5pUJ1Fc4VV+CH0usWAaFQyBgQRBJhSJDTMW09yJo8xxYEkbQYEuR0juRpjK0HARiDQs4WBJHkGBLkNBq7mKwtQTqojxJhPf0ZEEQSY0iQwzXe/3A4TwO9lSlVgrt2QtKwUOkLIyKGBDlWdq4aH+0tMAsHGRqWHhWioYuJM7oSOQ5DghymcV3qpo0HLy85Ho3rh5pb9exiInIwhgRJ5uyFcuScUiOspz8A4JP9BRbrUsdEBGP4IBWDgchJMCRIEo3dSgaDgFwhgwywuP8hol9XPJ44wDEFEpFVDAlqU6aT8jW2GvR6y4vTCoUMjzzUS+LqiKg5DAlqM7am1JDLGuZdMhgEZHIZRg5SsYuJyEkxJKjNmN4U16hxUr6QwM44V1zBC9NETo4hQW2iUF2FcyWVZtt+HtQZ0+N/WjmO4UDk/BgSZHfW7n0AgPt7KxkMRC6GIUF2ZeveB4VChtjono4piojuGkOC7KZxcSBb9z4MCA3A1as3HFYfEbUeQ4LswlYXE+99IHJtDAm6Z9m5avx7zzkIK11MvPeByLUxJOieNF6DMA0IuQwYNYTTaxC5A4YE3ZNdORctrkFMSwjDwxE9HFYTEdmP3NEFkOvKzlXjxPlrZtsi+nVlQBC5EYYE3ZXGbiZTchl4DYLIzTAkqNVsDXWdlhDGaxBEbobXJKhVbE3ax24mIvfEkKBmZeeq8b9zVxAV1g3Fl29YBASHuhK5L4YE3dGmLwux62gxAOC7ogr0DzHvTvqFqgsejevPbiYiN8WQIKsaFwv6b26p2fZ6vQEKhQwGfcMKcwwIIvfGkCAzjeFw2MpaEAAwckgw14Ig8iAMCTdUqK66qy/xQnUVXv3kBOp1BovnZDIg8Zc9jRenGQ5EnoEh4YLuFAKNX/Q6vQFeCjnmPxrZ4i/0c8UV0OnNA0Kh4PKiRJ5MspAoKirCggULUFlZCT8/P6xatQqhoaFm++j1eixfvhyHDh2CTCbDU089hUmTJklVolNorhVgLQQCA7sYn2/8ohcC0OsNOFdc0eIv97Ce/vBSyKHXG7j2NBEBkDAkFi9ejLS0NKSkpGD79u1YtGgRPvzwQ7N9duzYgeLiYuzduxeVlZVITU3FsGHDEBISck/vfbfdL1JrSSvAWggMi/jp/Jh+0SsUcoT19G/x+/ft4Yv5j0a6xLkiImlIcse1VqtFfn4+kpOTAQDJycnIz89HeXm52X5ZWVmYNGkS5HI5AgICEBcXh927d9/Tezd+8W45+ANe/eQECtVV93S8tmQtAJpqDAG5DFZDoPGLfsKo3q3qajJ9fdKwUAYEEQGQqCWh0WgQFBQEhUIBAFAoFOjWrRs0Gg0CAgLM9gsODjY+VqlUuHz58j299710v0itJa2Alvza79vD12n/RiJyLW534Vqp7Gz2+KHBPbDjyAXodAZ4ecnx0OAeZn34zWnNvvcqMLALVvp1RN731zCoT1cMCA2wuZ9pF1PjNlfAOu2LddoX67QkSUioVCqUlZVBr9dDoVBAr9fjypUrUKlUFvuVlpZi8ODBACxbFi2h1VbDYDLznLKTN56b+tMvb2Un7xavsxwY2EXyNZmVnbzx8OCG8+LMdd4N1mlfrNO+PLVOuVxm8ePalCQhoVQqER4ejszMTKSkpCAzMxPh4eFmXU0AkJiYiE2bNiE+Ph6VlZXYv38/Pvroo1a9l1wus9jW/+d+6P9zv7uq3drxnBHrtC/WaV+s077sWWdzx5IJ0XRl4rbx/fffY8GCBbh+/Tp+9rOfYdWqVejduzfS09Pxhz/8AYMGDYJer8fLL7+Mr776CgCQnp6OKVOmSFEeERFZIVlIEBGR6+GiQ0REZBNDgoiIbGJIEBGRTQwJIiKyiSFBREQ2MSSIiMgmhgQREdnEkCAiIpvcboK/u7Fq1Srs2bMHarUaO3bsQP/+/QG0bKEkKVVUVOAvf/kLiouL4ePjg169euHll19GQEAAcnNzsWjRItTW1qJHjx549dVXoVQqHVbrrFmzcOnSJcjlcnTs2BEvvfQSwsPDne6cAsBbb72FNWvWGD97ZzuXABAbGwsfHx+0a9cOAPDcc89h5MiRTlVrbW0tVq5cia+//hrt2rVDREQEli1b5lSf+aVLlzB79mzj4xs3bqC6uhrffPONU9UJAF9++SXeeOMNCCEghMCzzz6L+Ph46esUJI4dOyZKS0vF6NGjxblz54zbp0+fLrZt2yaEEGLbtm1i+vTpjipRCCFERUWFyMnJMT5+5ZVXxAsvvCD0er2Ii4sTx44dE0IIsXbtWrFgwQJHlSmEEOL69evGf963b59ITU0VQjjfOT19+rR44oknjJ+9M55LIYTFv5tCCKerddmyZWLFihXCYDAIIYS4evWqEML5PnNTy5cvF0uXLhVCOFedBoNBREdHGz/zM2fOiIiICKHX6yWvkyFhwvQ/xGvXromoqCih0+mEEELodDoRFRUltFqtI0s0s3v3bvF///d/4uTJkyIpKcm4XavVioiICAdWZm7r1q1iwoQJTndOa2trxeTJk0VJSYnxs3fWc2ktJJyp1urqahEVFSWqq6vNtjvbZ26qtrZWDB06VJw+fdrp6jQYDOKXv/yl+Pbbb4UQQnzzzTciPj7eIXWyu8mGli6U5CgGgwGffPIJYmNjLaZUDwgIgMFgMFsL4xAAAA1jSURBVDZHHeWvf/0rvvrqKwghsGHDBqc7p2+88QbGjx9vtjyus55LoKGLSQiBqKgo/OlPf3KqWktKSuDn54e33noLR48eRadOnfDHP/4R7du3d6rP3NSBAwcQFBSEgQMH4vTp005Vp0wmw+rVqzFr1ix07NgRNTU1ePfddx3y3xAvXLuoZcuWoWPHjpg2bZqjS7FpxYoVyM7Oxrx58/D3v//d0eWYOXHiBE6fPo20tDRHl9IiH330Eb744gts3rwZQgi8/PLLji7JjF6vR0lJCe677z5s2bIFzz33HObMmYObN286ujSbNm/ejIkTJzq6DKt0Oh3eeecdvP322/jyyy/xz3/+E3PnznXI+WRI2GC6UBIAmwslOcKqVatw8eJFrF69GnK53LhYU6Py8nLI5XKH//JtlJqaiqNHj6J79+5Oc06PHTuG77//HmPGjEFsbCwuX76MJ554AhcvXnTKc9l4jnx8fJCWlobjx4871eeuUqng5eVlXMd+yJAh8Pf3R/v27Z3mMzdVVlaGY8eOYdy4cQCc77/3M2fO4MqVK4iKigIAREVFoUOHDmjXrp3kdTIkbDBdKAmAzYWSpPb666/j9OnTWLt2LXx8fAAA999/P3788Ud8++23AIBPP/0UiYmJDquxpqYGGo3G+PjAgQPw9fV1qnP61FNP4fDhwzhw4AAOHDiA7t2741//+heefPJJpzqXAHDz5k3cuNGwEpkQAllZWQgPD3eqzz0gIABDhw41rgVTVFQErVaL0NBQp/nMTW3duhUxMTHw929YR96Z/t0EgO7du+Py5cv44YcfADSsx6PVatGrVy/J6+R6EgCWL1+OvXv34tq1a/D394efnx927txpc6EkRzl//jySk5MRGhqK9u3bAwBCQkKwdu1aHD9+HIsXLzYbCtm1a1eH1Hnt2jXMmjULt27dglwuh6+vL55//nkMHDjQ6c5po9jYWKxbtw79+/d3qnMJNPT3z5kzB3q9HgaDAX369MHChQvRrVs3p6q1pKQEL774IiorK+Hl5YW5c+ciJibGKT/zhIQE/PWvf8WoUaOM25ytzi+++ALr16+HTNawctwf/vAHxMXFSV4nQ4KIiGxidxMREdnEkCAiIpsYEkREZBNDgoiIbGJIEBGRTQwJoiauXbuGxx57DJGRkXjllVccXY6Zjz/+GMOHD0dkZCQqKiokec81a9bgueeek+S9yPlw7iZyOwsWLEBQUBDmzZt3V6//7LPP4O/vj+PHjxvHqNvD0aNHMX/+fBw8ePCuXl9fX49XXnkF//nPfzBgwAC71bVlyxZs3LgRxcXF6Ny5M+Li4vDnP/8ZP/vZz+z2HuS62JIgh9DpdI4uwabS0lL06dPHrgFhD1qtFrW1tejbt2+rXyuEgMFgsNj+3nvv4bXXXsP8+fPx7bff4rPPPkNpaSlmzJiBuro6e5RNLo4hQa0SGxuLd955B2PHjsWDDz6IF154AbW1tcbnv/zyS6SkpCA6OhpTp07F2bNnzV777rvvYty4cYiIiIBOp8O3336LqVOnIjo6GjExMdiyZQsAoK6uDqtWrcLDDz+M4cOHY9GiRfjxxx8BNPwiHzVqFN577z0MGzYMI0aMwObNmwE0tAJ27NiBf/3rX4iMjMTvf/97q3/H8ePHMXHiRERFRWHixIk4fvw4gIZWyLZt24yvP3LkiNnrTp48iV/96lfGuXMAYN++fcY5gOrq6rBixQqMGDECI0aMwIoVK1BXV4ebN28iPT0dV65cQWRkJCIjI1FWVgaDwYB3330XcXFxGDp0KP74xz+isrLSot6ioiLjlBsPPvggHn/88Tv+HQAwffp0/OMf/8DUqVMxZMgQlJSUmB2zuroaa9aswcKFCzFq1Ch4e3sjJCQEq1evhlqtxhdffGHct66uDnPnzkVkZCQmTJhg9rk21h8ZGYmxY8di3759xue2bNmCqVOnYuXKlYiOjsaYMWNw/PhxbNmyBTExMRg2bBi2bt1q3D87Oxupqal44IEHEBMTgzVr1lj9/EhCbTYJObml0aNHi6SkJFFaWioqKirElClTxOuvvy6EEOK7774TDz30kMjNzRU6nU5s2bJFjB49WtTW1hpfO378eFFaWipu3bolLl26JCIiIsSOHTtEXV2dKC8vF/n5+UIIIVasWCGefvppUVFRIW7cuCGefvpp8dprrwkhhMjJyRHh4eFi9erVoq6uTmRnZ4vBgweLyspKIYQQzz//vLEmayoqKkR0dLTYunWrqK+vFzt27BDR0dGivLy8Ra8fM2aMOHz4sPHxnDlzxDvvvCOEEGL16tVi0qRJ4tq1a0Kr1YopU6aIf/zjH8a6R44caXas999/X0yaNEloNBpRW1srXnrpJTFv3jyr71tSUiL69+8v6uvrW/R3TJs2TcTExIiCggJRX18v6urqzI733//+V4SHhxuPZ+ovf/mLsY4333xT3HfffWLXrl2irq5ObNiwQYwePdp4vKysLHH58mWh1+vFzp07xZAhQ0RZWZkQQojNmzeL8PBw8fnnnwudTidef/11ERMTI5YsWSJqa2vFoUOHREREhHEdipycHHH27Fmh1+vFmTNnxLBhw8S+fftsfhbU9tiSoFZ77LHHoFKp4Ofnh2eeeQY7d+4E0PArfsqUKRgyZAgUCgUmTJgAb29v5ObmGl87ffp0qFQqtG/fHpmZmRg+fDiSk5Ph7e0Nf39/hIeHQwiB//znP3jxxRfh5+eHzp074+mnnza+DwB4eXlh9uzZ8Pb2RkxMDDp27IiioqIW1Z+dnY1evXohNTXVOHNp79698eWXX7bo9UlJScYJ1qqrq3Hw4EEkJSUBAHbs2IHZs2dDqVQiICAAs2fPNvtF3tSnn36KefPmoXv37vDx8cGzzz6LPXv2tKg7riV/x4QJE9CvXz94eXnB29vb7PUVFRXw9/eHl5flpcnAwECzC+MDBw5EYmIivL29jV1RJ0+eBAA88sgjCAoKglwux9ixY9GrVy+cOnXK+NqQkBBMnDgRCoUCY8eOhUajwezZs+Hj44MRI0bAx8cHxcXFAIChQ4ciLCwMcrkcAwYMQFJSEr755ptmzwW1HV64plYznZY4ODgYV65cAdDQl79t2zZkZGQYn6+vrzc+3/S1Go0GPXv2tDh+eXk5bt26hd/85jfGbaJJn7qfn5/Zl1uHDh1aPNf+lStXzBbrafw7ysrKWvT6cePGYerUqVi6dCn27duH++67Dz169LB6bNPzY01paSlmz54Nufyn32tyuRxarRZBQUH3/HfcaQppf39/VFRUQKfTWQTF1atXjTOkAg2zkprWFxQUZPy7tm3bho0bN0KtVgNomLXWNGBM19xunJjSdBLCdu3aoaamBkBDd95rr72G8+fPo76+HnV1dQ6fhdfTMSSo1UynAS8tLUW3bt0ANHwh/f73v8czzzxj87WmF4NVKpXZL85GjesQ7Ny5s9kvyubew5pu3bqZrcMANPxNI0eObNHx+/bti+DgYBw8eBCZmZnGNRRMj92vXz/jcRvPj7W6unfvjpUrVxrXDWiNlvwddzoXkZGR8PHxwd69ezF27Fjj9pqaGhw8eBB/+tOfjNsuX75s/GeDwYCysjJ069YNarUaCxcuxPvvv4/IyEgoFAqkpKS0+m9p9Oc//xnTpk3Dhg0b0K5dO6xYsUKyob5kHbubqNU+/vhjXL58GZWVlVi3bp3xC2bSpEn49NNPcfLkSQghcPPmTWRnZ6O6utrqccaNG4cjR44gKysLOp0OFRUVOHPmDORyOSZNmoSVK1dCq9UCaFgk5tChQy2qT6lU4tKlSzafj4mJwYULF7Bjxw7odDpkZWWhsLAQDz/8cIvPQXJyMj744AMcO3bM7JduUlIS/vnPf6K8vBzl5eVYu3at8aK2UqlEZWWlcW0IAHj00UeNF4qBhlbU/v37W1TDvf4dXbp0wezZs7F8+XIcPHgQ9fX1uHTpEubOnYvu3bubfdl/99132Lt3L3Q6HT744AP4+PhgyJAhuHXrFmQymXE9g82bN+P8+fMten9rampq4Ovri3bt2uHUqVPGbj1yHIYEtVpycjJmzpyJuLg49OzZ09hyGDRoEJYtW4aXX34ZDz74IOLj442jlawJDg7G+vXrsXHjRvzyl79EamqqcdTM/Pnz0atXL0yePBkPPPAAfve737X4msNvf/tbFBYWIjo6GrNmzbJ43t/fH+vWrcPGjRsxdOhQbNiwAevWrWvVwi3Jyck4duwYHnroIbPXzZo1C/fffz/Gjx+P8ePHY+DAgcYa+vTpg6SkJMTFxSE6OhplZWV4/PHHERsbi5kzZyIyMhKTJ0+22rqyxh5/R3p6unF52aioKEyePBkqlQrvv/++cVErABgzZgyysrLw4IMPYvv27VizZg28vb3Rt29fzJw5E1OnTsXw4cNRUFCABx54oMXv39TixYvx5ptvIjIyEmvXrsUjjzxy18ci++B6EtQqsbGxWL58OYYPH+7oUohIAmxJEBGRTQwJIiKyid1NRERkE1sSRERkE0OCiIhsYkgQEZFNDAkiIrKJIUFERDYxJIiIyKb/B93e89gQ/5G9AAAAAElFTkSuQmCC\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{}}]},{"cell_type":"code","metadata":{"id":"EIxT6FbWeizJ","executionInfo":{"status":"ok","timestamp":1632978581246,"user_tz":300,"elapsed":290,"user":{"displayName":"heydy carrasco huaccha","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiGyezGgm9Px4LBw_sc7MFWfjUVhWWsvnAz1Ttg=s64","userId":"00007591481982700116"}}},"source":["def ecdf(data):\n","    \"\"\"Compute ECDF for a one-dimensional array of measurements.\"\"\"\n","    # Number of data points: n\n","    n = len(data)\n","\n","    # x-data for the ECDF: x\n","    x = np.sort(data)\n","\n","    # y-data for the ECDF: y\n","    y = np.arange(1,len(x)+1) / n\n","\n","    return x, y"],"execution_count":2,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"iusKTNeMgTMb","executionInfo":{"status":"ok","timestamp":1632865203579,"user_tz":300,"elapsed":10,"user":{"displayName":"heydy carrasco huaccha","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiGyezGgm9Px4LBw_sc7MFWfjUVhWWsvnAz1Ttg=s64","userId":"00007591481982700116"}},"outputId":"29de85fa-8112-406d-95d8-a702b02fb189"},"source":["df_swing.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>state</th>\n","      <th>county</th>\n","      <th>total_votes</th>\n","      <th>dem_votes</th>\n","      <th>rep_votes</th>\n","      <th>dem_share</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>PA</td>\n","      <td>Erie County</td>\n","      <td>127691</td>\n","      <td>75775</td>\n","      <td>50351</td>\n","      <td>60.08</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>PA</td>\n","      <td>Bradford County</td>\n","      <td>25787</td>\n","      <td>10306</td>\n","      <td>15057</td>\n","      <td>40.64</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>PA</td>\n","      <td>Tioga County</td>\n","      <td>17984</td>\n","      <td>6390</td>\n","      <td>11326</td>\n","      <td>36.07</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>PA</td>\n","      <td>McKean County</td>\n","      <td>15947</td>\n","      <td>6465</td>\n","      <td>9224</td>\n","      <td>41.21</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>PA</td>\n","      <td>Potter County</td>\n","      <td>7507</td>\n","      <td>2300</td>\n","      <td>5109</td>\n","      <td>31.04</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["  state           county  total_votes  dem_votes  rep_votes  dem_share\n","0    PA      Erie County       127691      75775      50351      60.08\n","1    PA  Bradford County        25787      10306      15057      40.64\n","2    PA     Tioga County        17984       6390      11326      36.07\n","3    PA    McKean County        15947       6465       9224      41.21\n","4    PA    Potter County         7507       2300       5109      31.04"]},"metadata":{},"execution_count":10}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":285},"id":"jod4MAoyfLzD","executionInfo":{"status":"ok","timestamp":1632865203580,"user_tz":300,"elapsed":10,"user":{"displayName":"heydy carrasco huaccha","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiGyezGgm9Px4LBw_sc7MFWfjUVhWWsvnAz1Ttg=s64","userId":"00007591481982700116"}},"outputId":"95b66202-c438-4f34-9be2-1baf2dbbb9c6"},"source":["# Compute ECDF for versicolor data: x_vers, y_vers\n","x_, y_ = ecdf(df_swing['dem_share'])\n","\n","# Generate plot\n","_= plt.plot(x_, y_, marker='.', linestyle='none')\n","\n","# Label the axes\n","_= plt.xlabel('percent of vote for Obama')\n","_= plt.ylabel('ECDF')\n","\n","# Display the plot\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"i9HlBEx2gXfE","executionInfo":{"status":"ok","timestamp":1632865203580,"user_tz":300,"elapsed":8,"user":{"displayName":"heydy carrasco huaccha","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiGyezGgm9Px4LBw_sc7MFWfjUVhWWsvnAz1Ttg=s64","userId":"00007591481982700116"}},"outputId":"1d927fa8-7c8e-4caf-b455-5e72a2169e73"},"source":["state_PA = df_swing[df_swing['state'] == \"PA\"]\n","state_OH = df_swing[df_swing['state'] == \"OH\"]\n","state_FL = df_swing[df_swing['state'] == \"FL\"]\n","len(state_OH)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["88"]},"metadata":{},"execution_count":12}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":285},"id":"HE-vgQClg8jB","executionInfo":{"status":"ok","timestamp":1632865204530,"user_tz":300,"elapsed":955,"user":{"displayName":"heydy carrasco huaccha","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiGyezGgm9Px4LBw_sc7MFWfjUVhWWsvnAz1Ttg=s64","userId":"00007591481982700116"}},"outputId":"1b5ca44f-f345-4569-9505-5cbf88064eaa"},"source":["# Compute ECDFs\n","x_pa, y_pa = ecdf(state_PA['dem_share'])\n","x_oh, y_oh = ecdf(state_OH['dem_share'])\n","x_fl, y_fl = ecdf(state_FL['dem_share'])\n","\n","# Plot all ECDFs on the same plot\n","_= plt.plot(x_pa, y_pa, marker='.', linestyle='none')\n","_= plt.plot(x_oh, y_oh, marker='.', linestyle='none')\n","_= plt.plot(x_fl, y_fl, marker='.', linestyle='none')\n","\n","# Annotate the plot\n","plt.legend(('PA', 'OH', 'FL'), loc='lower right')\n","_= plt.xlabel('percent of vote for Obama')\n","_= plt.ylabel('ECDF')\n","\n","# Display the plot\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"7dKQRcqeCRe2"},"source":["## 2. Quantitative Exploratory Data Analysis"]},{"cell_type":"markdown","metadata":{"id":"7K6noXUDC0JN"},"source":["En este capítulo, calculará estadísticas resumidas útiles, que sirven para describir de manera concisa las características más destacadas de un conjunto de datos con unos pocos números."]},{"cell_type":"code","metadata":{"id":"34VXdM1zCzNd","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1632865204531,"user_tz":300,"elapsed":7,"user":{"displayName":"heydy carrasco huaccha","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiGyezGgm9Px4LBw_sc7MFWfjUVhWWsvnAz1Ttg=s64","userId":"00007591481982700116"}},"outputId":"9bf6f985-6e77-4348-81c6-05558a861774"},"source":["np.mean(df_swing['dem_share']) # MEDIA"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["43.76441441441444"]},"metadata":{},"execution_count":14}]},{"cell_type":"code","metadata":{"id":"SExBe3XSCzKk","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1632865204531,"user_tz":300,"elapsed":5,"user":{"displayName":"heydy carrasco huaccha","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiGyezGgm9Px4LBw_sc7MFWfjUVhWWsvnAz1Ttg=s64","userId":"00007591481982700116"}},"outputId":"4ceeaf36-f757-4225-d304-3c02db267248"},"source":["_ = sns.boxplot(x='state', y='dem_share', data=df_swing)\n","_ = plt.xlabel('state')\n","_ = plt.ylabel('percent of vote for Obama')\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"_XcWjvBImmay"},"source":["### Percentiles, outliers, and box plots"]},{"cell_type":"code","metadata":{"id":"FEJRks4TCzIF","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1632865204531,"user_tz":300,"elapsed":4,"user":{"displayName":"heydy carrasco huaccha","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiGyezGgm9Px4LBw_sc7MFWfjUVhWWsvnAz1Ttg=s64","userId":"00007591481982700116"}},"outputId":"dbbdc3c1-70bc-4023-8164-3353fc641455"},"source":["percentiles = [2.5, 25, 50, 75, 97.5]\n","percentil_share = np.percentile(df_swing['dem_share'], percentiles)\n","percentil_share"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([25.58675, 37.3025 , 43.185  , 49.925  , 64.7195 ])"]},"metadata":{},"execution_count":16}]},{"cell_type":"code","metadata":{"id":"qyZyB-cjh8Vy","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1632865205524,"user_tz":300,"elapsed":997,"user":{"displayName":"heydy carrasco huaccha","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiGyezGgm9Px4LBw_sc7MFWfjUVhWWsvnAz1Ttg=s64","userId":"00007591481982700116"}},"outputId":"c2b8605a-2e1e-453c-e550-9d2900b9b520"},"source":["x = np.sort(df_swing['dem_share'])\n","y = np.arange(1, len(x)+1) / len(x)\n","_= plt.plot(x, y, marker='.', linestyle='none')"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"gIMiGQ_LhRFp","executionInfo":{"status":"ok","timestamp":1632865205524,"user_tz":300,"elapsed":10,"user":{"displayName":"heydy carrasco huaccha","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiGyezGgm9Px4LBw_sc7MFWfjUVhWWsvnAz1Ttg=s64","userId":"00007591481982700116"}},"outputId":"3033cd9c-1986-4786-b2b3-cffb384b0ffe"},"source":["# Plot the ECDF\n","x = np.sort(df_swing['dem_share'])\n","y = np.arange(1, len(x)+1) / len(x)\n","_ = plt.plot(x, y, '.')\n","_ = plt.xlabel('petal length (cm)')\n","_ = plt.ylabel('ECDF')\n","\n","# Overlay percentiles as red diamonds.\n","_ = plt.plot(percentil_share, np.array(percentiles)/100, marker='D', color='red', linestyle='none')\n","\n","# Show the plot\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"IiwFJbldmiaz"},"source":["### Variance and standard deviation"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"NDlbRhWGmdrg","executionInfo":{"status":"ok","timestamp":1632865205524,"user_tz":300,"elapsed":9,"user":{"displayName":"heydy carrasco huaccha","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiGyezGgm9Px4LBw_sc7MFWfjUVhWWsvnAz1Ttg=s64","userId":"00007591481982700116"}},"outputId":"026ffd51-ec55-4201-a457-c900bd7aab31"},"source":["# Array of differences to mean: differences\n","differences = df_swing['dem_share'] - np.mean(df_swing['dem_share'])\n","\n","# Square the differences: diff_sq\n","diff_sq = [x**2 for x in  differences]\n","\n","# Compute the mean square difference: variance_explicit\n","variance_explicit = np.mean(diff_sq)\n","\n","# Compute the variance using NumPy: variance_np\n","variance_np = np.var(df_swing['dem_share'])\n","\n","# Print the results\n","print(variance_explicit,variance_np)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["114.2464949273598 114.24649492735986\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"__yDvNpqmdpD","executionInfo":{"status":"ok","timestamp":1632865205524,"user_tz":300,"elapsed":8,"user":{"displayName":"heydy carrasco huaccha","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiGyezGgm9Px4LBw_sc7MFWfjUVhWWsvnAz1Ttg=s64","userId":"00007591481982700116"}},"outputId":"2752050a-7a18-4183-f635-2c78e28a67de"},"source":["# Compute the variance: variance\n","variance = np.var(df_swing['dem_share'])\n","\n","# Print the square root of the variance\n","print(np.sqrt(variance))\n","\n","# Print the standard deviation\n","print(np.std(df_swing['dem_share']))"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["10.68861520157592\n","10.68861520157592\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ek_2DRHomdmI","executionInfo":{"status":"ok","timestamp":1632865205525,"user_tz":300,"elapsed":8,"user":{"displayName":"heydy carrasco huaccha","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiGyezGgm9Px4LBw_sc7MFWfjUVhWWsvnAz1Ttg=s64","userId":"00007591481982700116"}},"outputId":"80b1c25c-3ee2-4f62-d46e-96998d72e857"},"source":["# Make a scatter plot\n","_ = plt.plot(df_swing['total_votes']/1000, df_swing['dem_share'], marker='.', linestyle='none')\n","\n","# Label the axes\n","_ = plt.xlabel('total votes (thousands)')\n","_ = plt.ylabel('percent of vote for Obama')\n","\n","# Show the result\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"HCcyHlvVmddy","executionInfo":{"status":"ok","timestamp":1632865205525,"user_tz":300,"elapsed":7,"user":{"displayName":"heydy carrasco huaccha","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiGyezGgm9Px4LBw_sc7MFWfjUVhWWsvnAz1Ttg=s64","userId":"00007591481982700116"}},"outputId":"90dea2f9-4e23-49c8-d0ef-4be169f43cc6"},"source":["# Compute the covariance matrix: covariance_matrix\n","covariance_matrix =np.cov(df_swing['total_votes']/1000,df_swing['dem_share'])\n","\n","# Print covariance matrix\n","print(covariance_matrix)\n","\n","# Extract covariance of length and width of dem_share: dem_share_cov\n","dem_share_cov = covariance_matrix[0,1]\n","\n","# Print the length/width covariance\n","print(dem_share_cov)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[[20245.10393632   817.30936184]\n"," [  817.30936184   114.76344739]]\n","817.3093618405288\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hdaNHkv81DKl","executionInfo":{"status":"ok","timestamp":1632865205525,"user_tz":300,"elapsed":7,"user":{"displayName":"heydy carrasco huaccha","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiGyezGgm9Px4LBw_sc7MFWfjUVhWWsvnAz1Ttg=s64","userId":"00007591481982700116"}},"outputId":"ac8d4702-468a-4c57-cf9f-7d487163fff9"},"source":["def pearson_r(x, y):\n","    \"\"\"Compute Pearson correlation coefficient between two arrays.\"\"\"\n","    # Compute correlation matrix: corr_mat\n","    corr_mat = np.corrcoef(x,y)\n","\n","    # Return entry [0,1]\n","    return corr_mat[0,1]\n","\n","# Compute Pearson correlation coefficient for I. versicolor: r\n","r = pearson_r(df_swing['total_votes']/1000,df_swing['dem_share'])\n","\n","# Print the result\n","print(r)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["0.5361973649586781\n"]}]},{"cell_type":"markdown","metadata":{"id":"7JbmYLi6CRoF"},"source":["## 3. Thinking Probabilistically-- Discrete Variables"]},{"cell_type":"markdown","metadata":{"id":"EQ_AaBcWCxhF"},"source":["La inferencia estadística se basa en la probabilidad. Debido a que muy pocas veces podemos decir algo significativo con absoluta certeza a partir de los datos, usamos un lenguaje probabilístico para hacer declaraciones cuantitativas sobre los datos. En este capítulo, aprenderá a pensar de manera probabilística sobre cantidades discretas: aquellas que solo pueden tomar ciertos valores, como números enteros. "]},{"cell_type":"markdown","metadata":{"id":"9-_mqp53NC2C"},"source":["### Probabilistic logic and statistical inference"]},{"cell_type":"code","metadata":{"id":"yVuVixxvRbbc"},"source":["# FULL TEORIA"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"tdb2ErqINFYO"},"source":["### Random number generators and hacker statistics"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6rt143_SQI5L","executionInfo":{"status":"ok","timestamp":1632865205881,"user_tz":300,"elapsed":8,"user":{"displayName":"heydy carrasco huaccha","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiGyezGgm9Px4LBw_sc7MFWfjUVhWWsvnAz1Ttg=s64","userId":"00007591481982700116"}},"outputId":"b844af34-87d9-4220-9600-e408fe723b2d"},"source":["# Nos da un # entre 0 y 1 con la misma probabilidad de ocurrir\n","\n","np.random.seed(42)\n","random_number = np.random.random(size=4)\n","random_number"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0.37454012, 0.95071431, 0.73199394, 0.59865848])"]},"metadata":{},"execution_count":25}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"PR8kTLK-TELN","executionInfo":{"status":"ok","timestamp":1632865205881,"user_tz":300,"elapsed":6,"user":{"displayName":"heydy carrasco huaccha","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiGyezGgm9Px4LBw_sc7MFWfjUVhWWsvnAz1Ttg=s64","userId":"00007591481982700116"}},"outputId":"6a0618e3-703b-4e86-f318-2ba00422dd2a"},"source":["heads = random_number <0.5\n","heads"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([ True, False, False, False])"]},"metadata":{},"execution_count":26}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"uDeiPPRWTMBj","executionInfo":{"status":"ok","timestamp":1632865205882,"user_tz":300,"elapsed":6,"user":{"displayName":"heydy carrasco huaccha","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiGyezGgm9Px4LBw_sc7MFWfjUVhWWsvnAz1Ttg=s64","userId":"00007591481982700116"}},"outputId":"666135df-9001-42e8-91d3-52efeea21d36"},"source":["np.sum(heads)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["1"]},"metadata":{},"execution_count":27}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":268},"id":"F5gPXU5nTL-L","executionInfo":{"status":"ok","timestamp":1632865205882,"user_tz":300,"elapsed":5,"user":{"displayName":"heydy carrasco huaccha","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiGyezGgm9Px4LBw_sc7MFWfjUVhWWsvnAz1Ttg=s64","userId":"00007591481982700116"}},"outputId":"a54355d6-1cf8-4131-d974-53fc899ca704"},"source":["# Seed the random number generator\n","np.random.seed(42)\n","\n","# Initialize random numbers: random_numbers (empty array)\n","random_numbers = np.empty(100000)\n","\n","# Generate random numbers by looping over range(100000)\n","for i in range(100000):\n","    random_numbers[i] = np.random.random()\n","\n","# Plot a histogram\n","_ = plt.hist(random_numbers)\n","\n","# Show the plot\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{}}]},{"cell_type":"code","metadata":{"id":"4Img3qosTL7b"},"source":["def perform_bernoulli_trials(n, p):\n","    \"\"\"Perform n Bernoulli trials with success probability p\n","    and return number of successes.\"\"\"\n","    # Initialize number of successes: n_success\n","    n_success = 0\n","\n","    # Perform trials\n","    for i in range(n):\n","        # Choose random number between zero and one: random_number\n","        num = np.random.random()\n","\n","        # If less than p, it's a success so add one to n_success\n","        if num < p:\n","            n_success += 1\n","\n","    return n_success"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":285},"id":"5oe_Mhl6ZKMX","executionInfo":{"status":"ok","timestamp":1632865206521,"user_tz":300,"elapsed":643,"user":{"displayName":"heydy carrasco huaccha","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiGyezGgm9Px4LBw_sc7MFWfjUVhWWsvnAz1Ttg=s64","userId":"00007591481982700116"}},"outputId":"96f74fc4-110c-4f35-db0f-c86b47692a12"},"source":["# Seed random number generator\n","np.random.seed(42)\n","\n","# Initialize the number of defaults: n_defaults\n","n_defaults = np.empty(1000)\n","\n","# Compute the number of defaults\n","for i in range(1000):\n","    n_defaults[i] = perform_bernoulli_trials(100, 0.05)\n","\n","# Plot the histogram with default number of bins; label your axes\n","_ = plt.hist(n_defaults, density=True) # normed = True\n","_ = plt.xlabel('number of defaults out of 100 loans')\n","_ = plt.ylabel('probability')\n","\n","# Show the plot\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":302},"id":"2MdM_s67a8QV","executionInfo":{"status":"ok","timestamp":1632865206886,"user_tz":300,"elapsed":367,"user":{"displayName":"heydy carrasco huaccha","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiGyezGgm9Px4LBw_sc7MFWfjUVhWWsvnAz1Ttg=s64","userId":"00007591481982700116"}},"outputId":"0d3cacd4-b4f8-4204-9154-b0ef3df382cd"},"source":["# Compute ECDF: x, y\n","x, y = ecdf(n_defaults)\n","\n","# Plot the ECDF with labeled axes\n","_= plt.plot(x, y, marker='.', linestyle='none')\n","_= plt.xlabel('percent')\n","_= plt.ylabel('ECDF')\n","plt.show()\n","\n","# Show the plot\n","plt.show()\n","\n","# Compute the number of 100-loan simulations with 10 or more defaults: n_lose_money\n","n_lose_money = np.sum (n_defaults >= 10)\n","\n","# Compute and print probability of losing money\n","print('Probability of losing money =', n_lose_money / len(n_defaults))"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Probability of losing money = 0.022\n"]}]},{"cell_type":"markdown","metadata":{"id":"mAMXwQTgNFim"},"source":["### Probability distributions and stories: The Binomial distribution\n"]},{"cell_type":"markdown","metadata":{"id":"FtPevzAgb-iP"},"source":["**PMF <> FUNCION DE MASA PROBABILIDAD**\n","\n","Conjunto de probabilidades de resultados discretos\n","\n","\n","**DUD <> BINOMIAL**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":285},"id":"EEhtSp-gQJRT","executionInfo":{"status":"ok","timestamp":1632865207161,"user_tz":300,"elapsed":277,"user":{"displayName":"heydy carrasco huaccha","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiGyezGgm9Px4LBw_sc7MFWfjUVhWWsvnAz1Ttg=s64","userId":"00007591481982700116"}},"outputId":"122fd32a-d300-41dc-842d-ec795b13245e"},"source":["# Take 10,000 samples out of the binomial distribution: n_defaults\n","n_defaults = np.random.binomial(100, 0.05, size=10000)\n","\n","# Compute CDF: x, y\n","x, y = ecdf(n_defaults)\n","\n","# Plot the CDF with axis labels\n","_= plt.plot(x, y, marker='.', linestyle='none')\n","_= plt.xlabel('number of defaults out of 100 loans')\n","_= plt.ylabel('CDF')\n","\n","\n","# Show the plot\n","plt.show()\n"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":285},"id":"9tw8hivTflKq","executionInfo":{"status":"ok","timestamp":1632865207633,"user_tz":300,"elapsed":474,"user":{"displayName":"heydy carrasco huaccha","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiGyezGgm9Px4LBw_sc7MFWfjUVhWWsvnAz1Ttg=s64","userId":"00007591481982700116"}},"outputId":"991ae220-e170-4db4-c6bf-50ce19bbffd3"},"source":["# Compute bin edges: bins\n","bins = np.arange(0, max(n_defaults) + 1.5) - 0.5\n","\n","# Generate histogram\n","_ = plt.hist(n_defaults, density=True, bins=bins) # normed = True\n","\n","# Label axes\n","_ = plt.xlabel('number of defaults out of 100 loans')\n","_ = plt.ylabel('probability')\n","\n","# Show the plot\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"igw39z3WNFpc"},"source":["### Poisson processes and the Poisson distribution"]},{"cell_type":"code","metadata":{"id":"m6LT-B1ICz5d","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1632865207633,"user_tz":300,"elapsed":8,"user":{"displayName":"heydy carrasco huaccha","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiGyezGgm9Px4LBw_sc7MFWfjUVhWWsvnAz1Ttg=s64","userId":"00007591481982700116"}},"outputId":"622f112f-46e4-4ebf-fbbd-bfbb4951595c"},"source":["# Draw 10,000 samples out of Poisson distribution: samples_poisson\n","samples_poisson = np.random.poisson(10, size=10000)\n","\n","# Print the mean and standard deviation\n","print('Poisson:     ', np.mean(samples_poisson),\n","                       np.std(samples_poisson))\n","\n","# Specify values of n and p to consider for Binomial: n, p\n","n = [20, 100, 1000]\n","p = [0.5, 0.1, 0.01]\n","\n","# Draw 10,000 samples for each n,p pair: samples_binomial\n","for i in range(3):\n","    samples_binomial = np.random.binomial(n[i], p[i],size = 10000)\n","\n","    # Print results\n","    print('n =', n[i], 'Binom:', np.mean(samples_binomial),\n","                                 np.std(samples_binomial))\n"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Poisson:      10.0145 3.1713545607516043\n","n = 20 Binom: 10.0592 2.23523944131272\n","n = 100 Binom: 10.0441 2.9942536949964675\n","n = 1000 Binom: 10.0129 3.139639085946026\n"]}]},{"cell_type":"code","metadata":{"id":"E_aCZECiDSkH"},"source":["# ¡Correcto! Cuando tenemos eventos raros (p bajo, n alto), la distribución Binomial es Poisson. Esto tiene un solo parámetro, \n","# vel número medio de éxitos por intervalo de tiempo, en nuestro caso el número medio de juegos sin hits por temporada."],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"9RB4hctIDSfE","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1632865207634,"user_tz":300,"elapsed":6,"user":{"displayName":"heydy carrasco huaccha","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiGyezGgm9Px4LBw_sc7MFWfjUVhWWsvnAz1Ttg=s64","userId":"00007591481982700116"}},"outputId":"c7903e39-5643-4ad9-b4ac-a3f0a90ab7c5"},"source":["# Draw 10,000 samples out of Poisson distribution: n_nohitters\n","n_nohitters = np.random.poisson(251/115, size=10000)\n","\n","# Compute number of samples that are seven or greater: n_large\n","n_large = np.sum(n_nohitters >= 7)\n","\n","# Compute probability of getting seven or more: p_large\n","p_large = n_large / 10000\n","\n","# Print the result\n","print('Probability of seven or more no-hitters:', p_large)\n"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Probability of seven or more no-hitters: 0.0072\n"]}]},{"cell_type":"markdown","metadata":{"id":"8HznbzycCRv-"},"source":["## 4. Thinking Probabilistically-- Continuous Variables"]},{"cell_type":"markdown","metadata":{"id":"NeuMQ1-NC-Ba"},"source":["Es hora de pasar a las variables continuas, como las que pueden tomar cualquier valor fraccionario. Muchos de los principios son los mismos, pero hay algunas sutilezas. Al final de este capítulo final, hablará el lenguaje probabilístico que necesita para iniciar las técnicas de inferencia que se tratan en la secuela de este curso."]},{"cell_type":"markdown","metadata":{"id":"2ByqjcyLWhi7"},"source":["### Probability density functions"]},{"cell_type":"code","metadata":{"id":"f_8IaQTcYZa8"},"source":["# FULL TEORIA"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"nq1BSrwwWoD9"},"source":["### Introduction to the Normal distribution"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":268},"id":"0iQQgWxSYdLm","executionInfo":{"status":"ok","timestamp":1632866392757,"user_tz":300,"elapsed":800,"user":{"displayName":"heydy carrasco huaccha","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiGyezGgm9Px4LBw_sc7MFWfjUVhWWsvnAz1Ttg=s64","userId":"00007591481982700116"}},"outputId":"9ed6f2af-c804-4358-9e31-b721c0974594"},"source":["# Draw 100000 samples from Normal distribution with stds of interest: samples_std1, samples_std3, samples_std10\n","samples_std1 = np.random.normal(20, 1, size=100000)\n","samples_std3 = np.random.normal(20, 3, size=100000)\n","samples_std10 = np.random.normal(20, 10, size=100000)\n","\n","# Make histograms\n","_ = plt.hist(samples_std1, bins=100, density = True,histtype='step' )\n","_ = plt.hist(samples_std3, bins=100, density = True,histtype='step' )\n","_ = plt.hist(samples_std10, bins=100, density = True,histtype='step' )\n","\n","# Make a legend, set limits and show plot\n","_ = plt.legend(('std = 1', 'std = 3', 'std = 10'))\n","plt.ylim(-0.01, 0.42)\n","plt.show()\n"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"OcS2pPJXbFC7"},"source":["¡Buen trabajo! Puede ver cómo las diferentes desviaciones estándar dan como resultado archivos PDF de diferentes anchos. Todos los picos están centrados en la media de 20."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":268},"id":"6vd820_DbICE","executionInfo":{"status":"ok","timestamp":1632866653284,"user_tz":300,"elapsed":959,"user":{"displayName":"heydy carrasco huaccha","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiGyezGgm9Px4LBw_sc7MFWfjUVhWWsvnAz1Ttg=s64","userId":"00007591481982700116"}},"outputId":"6c69b9be-a458-4a28-f4ca-202688d8d90e"},"source":["# The Normal CDF\n","\n","# Generate CDFs\n","x_std1, y_std1 = ecdf(samples_std1)\n","x_std3, y_std3 = ecdf(samples_std3)\n","x_std10, y_std10 = ecdf(samples_std10)\n","\n","# Plot CDFs\n","_= plt.plot(x_std1, y_std1, marker='.', linestyle='none')\n","_= plt.plot(x_std3, y_std3, marker='.', linestyle='none')\n","_= plt.plot(x_std10, y_std10, marker='.', linestyle='none')\n","\n","# Make a legend and show the plot\n","_ = plt.legend(('std = 1', 'std = 3', 'std = 10'), loc='lower right')\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"oUyn9ATtcChx"},"source":["¡Buen trabajo! Todas las CDF pasan por la media en el percentil 50; la media y la mediana de una distribución normal son iguales. El ancho de la CDF varía con la desviación estándar."]},{"cell_type":"code","metadata":{"id":"PFFFjEFubH-j"},"source":["belmont_no_outliers =np.array([148.51, 146.65, 148.52, 150.7 , 150.42, 150.88, 151.57, 147.54,\n","       149.65, 148.74, 147.86, 148.75, 147.5 , 148.26, 149.71, 146.56,\n","       151.19, 147.88, 149.16, 148.82, 148.96, 152.02, 146.82, 149.97,\n","       146.13, 148.1 , 147.2 , 146.  , 146.4 , 148.2 , 149.8 , 147.  ,\n","       147.2 , 147.8 , 148.2 , 149.  , 149.8 , 148.6 , 146.8 , 149.6 ,\n","       149.  , 148.2 , 149.2 , 148.  , 150.4 , 148.8 , 147.2 , 148.8 ,\n","       149.6 , 148.4 , 148.4 , 150.2 , 148.8 , 149.2 , 149.2 , 148.4 ,\n","       150.2 , 146.6 , 149.8 , 149.  , 150.8 , 148.6 , 150.2 , 149.  ,\n","       148.6 , 150.2 , 148.2 , 149.4 , 150.8 , 150.2 , 152.2 , 148.2 ,\n","       149.2 , 151.  , 149.6 , 149.6 , 149.4 , 148.6 , 150.  , 150.6 ,\n","       149.2 , 152.6 , 152.8 , 149.6 , 151.6 , 152.8 , 153.2 , 152.4 ,\n","       152.2 ])"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":285},"id":"j5yjT7n1bH2f","executionInfo":{"status":"ok","timestamp":1632869057272,"user_tz":300,"elapsed":619,"user":{"displayName":"heydy carrasco huaccha","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiGyezGgm9Px4LBw_sc7MFWfjUVhWWsvnAz1Ttg=s64","userId":"00007591481982700116"}},"outputId":"9a5a88f7-be24-40bb-b182-354d42bc0d5d"},"source":["# Compute mean and standard deviation: mu, sigma\n","mu = np.mean(belmont_no_outliers)\n","sigma = np.std(belmont_no_outliers)\n","\n","# Sample out of a normal distribution with this mu and sigma: samples\n","samples = np.random.normal(mu,sigma, size=10000)\n","\n","# Get the CDF of the samples and of the data\n","x_theor, y_theor = ecdf(samples)\n","x, y = ecdf(belmont_no_outliers)\n","\n","# Plot the CDFs and show the plot\n","_ = plt.plot(x_theor, y_theor)\n","_ = plt.plot(x, y, marker='.', linestyle='none')\n","_ = plt.xlabel('Belmont winning time (sec.)')\n","_ = plt.ylabel('CDF')\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"1VmJF55mlQag"},"source":["El CDF teórico y el ECDF de los datos sugieren que los tiempos Belmont ganadores están, de hecho, normalmente distribuidos. Esto también sugiere que en los últimos 100 años más o menos, no ha habido grandes avances tecnológicos o de entrenamiento que hayan afectado significativamente la velocidad a la que los caballos pueden correr esta carrera."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-57-w-m9m-je","executionInfo":{"status":"ok","timestamp":1632869770621,"user_tz":300,"elapsed":224,"user":{"displayName":"heydy carrasco huaccha","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiGyezGgm9Px4LBw_sc7MFWfjUVhWWsvnAz1Ttg=s64","userId":"00007591481982700116"}},"outputId":"447606b9-52b5-45ec-a775-594732195d1b"},"source":["# Take a million samples out of the Normal distribution: samples\n","samples = np.random.normal(mu,sigma, size=1000000)\n","\n","# Compute the fraction that are faster than 144 seconds: prob\n","prob = np.sum(samples <= 144)/len(samples)\n","\n","# Print the result\n","print('Probability of besting Secretariat:', prob)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Probability of besting Secretariat: 0.000658\n"]}]},{"cell_type":"markdown","metadata":{"id":"h3VCttN8n7On"},"source":["¡Buen trabajo! Tuvimos que tomar un millón de muestras porque la probabilidad de un tiempo rápido es muy baja y teníamos que asegurarnos de muestrear lo suficiente. Sabemos que solo hay un 0,06% de posibilidades de que un caballo corra por el Belmont tan rápido como la Secretaría."]},{"cell_type":"markdown","metadata":{"id":"5KXauisQWoPd"},"source":["### The Normal distribution: Properties and warnings"]},{"cell_type":"code","metadata":{"id":"zXoDy5uVYdtk"},"source":["# La distribución exponencial describe los tiempos de espera entre eventos raros"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"tUJK8ALZWoYI"},"source":["### The Exponential distribution"]},{"cell_type":"code","metadata":{"id":"sJ9A8j-RBjGK"},"source":["def successive_poisson(tau1, tau2, size=1):\n","    \"\"\"Compute time for arrival of 2 successive Poisson processes.\"\"\"\n","    # Draw samples out of first exponential distribution: t1\n","    t1 = np.random.exponential(tau1, size=size)\n","\n","    # Draw samples out of second exponential distribution: t2\n","    t2 = np.random.exponential(tau2, size=size)\n","\n","    return t1 + t2"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":285},"id":"sOMeW6g9Wmff","executionInfo":{"status":"ok","timestamp":1632870480321,"user_tz":300,"elapsed":689,"user":{"displayName":"heydy carrasco huaccha","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiGyezGgm9Px4LBw_sc7MFWfjUVhWWsvnAz1Ttg=s64","userId":"00007591481982700116"}},"outputId":"55ebc491-23e4-499f-d168-5db43ed7cf16"},"source":["# Draw samples of waiting times: waiting_times\n","waiting_times = successive_poisson(764,715,size=100000)\n","\n","# Make the histogram\n","_ = plt.hist(waiting_times, density=True, bins=100,histtype='step') # normed = True \n","\n","# Label axes\n","_ = plt.xlabel('number of defaults out of 100 loans')\n","_ = plt.ylabel('probability')\n","\n","# Show the plot\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"uba7LiO8qhEp"},"source":["¡Buen trabajo! Observe que el PDF tiene su punto máximo, a diferencia del tiempo de espera para un solo proceso de Poisson. Para divertirse (e iluminarse), les animo a que también tracen el CDF."]},{"cell_type":"code","metadata":{"id":"dNwkMyuODTFc"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"atLghOhhDTCU"},"source":[""],"execution_count":null,"outputs":[]}]}