Esteban_Garcia_Tarea2.ipynb

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  "metadata": {
    "colab": {
      "name": "Esteban_Garcia_Tarea2",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "EugnGayGk2e7",
        "colab_type": "code",
        "outputId": "756932e5-5984-4457-f6cf-712d357c0313",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#@title\n",
        "import numpy as np;\n",
        "import matplotlib.pyplot as plt;\n",
        "import pandas as pd\n",
        "from scipy.stats import norm\n",
        "from scipy.stats import poisson\n",
        "from scipy.stats import expon\n",
        "from scipy.stats import gamma\n",
        "from scipy.stats import geom\n",
        "from scipy.stats import kstest\n",
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/drive')\n",
        "url = '/content/drive/My Drive/Colab Notebooks/estadistica/datasetTarea2.csv'\n",
        "df = pd.read_csv(url, decimal=',', quotechar='\\'')\n",
        "\n",
        "def printTable(table):\n",
        "  col_width = max(len(word) for row in table for word in row) + 2  # padding\n",
        "  for row in table:\n",
        "      print(\"\".join(word.ljust(col_width) for word in row))\n",
        "  print(\"\")\n",
        "\n",
        "def plotHistogram(data, bins, title, show = False, cumu=False):\n",
        "    histDetails = plt.hist(data, bins, density=True, cumulative=cumu)\n",
        "    if(cumu):\n",
        "      plt.title('CDF {}'.format(title))\n",
        "    else:\n",
        "      plt.title('PPF {}'.format(title))\n",
        "    if(show):\n",
        "      plt.legend(loc='best', frameon=False)\n",
        "      plt.show()  \n",
        "    return histDetails\n",
        "\n",
        "def plotPoisson(l, loc, cumu = False, show = False):\n",
        "  x = np.arange(poisson.ppf(0.01, l, loc), poisson.ppf(0.99, l, loc))  \n",
        "  if(cumu):\n",
        "    plt.plot(x, poisson.cdf(x, l, loc), label='poisson')\n",
        "  else:\n",
        "    plt.plot(x, poisson.pmf(x, l, loc), label='poisson')\n",
        "  if(show):\n",
        "    plt.show()\n",
        "\n",
        "def plotGeom(p, loc, show = False):\n",
        "  x = np.linspace(geom.ppf(0.01, p=p, loc=loc),\n",
        "                  geom.ppf(0.99, p=p, loc=loc), 10)\n",
        "  x = np.around(x)\n",
        "  plt.plot(x, geom.pmf(x, p=p, loc=loc), 'bo', label='geom')\n",
        "  if(show):\n",
        "    plt.show()\n",
        "\n",
        "def plotExpon(l, loc, cumu = False, show = False):\n",
        "  scale = 1 / l\n",
        "  x = np.linspace(expon.ppf(0.01, loc, scale),expon.ppf(0.99, loc, scale), 100)\n",
        "  if(cumu):\n",
        "    plt.plot(x, expon.cdf(x, loc, scale), label='expon', linestyle=\":\")\n",
        "  else:\n",
        "    plt.plot(x, expon.pdf(x, loc, scale), label='expon', linestyle=\":\")\n",
        "  if(show):\n",
        "    plt.show()\n",
        "\n",
        "def plotNorm(loc, scale, cumu = False, show = False, ):\n",
        "  x = np.linspace(norm.ppf(0.001, loc, scale),norm.ppf(0.999, loc, scale), 100)\n",
        "  if(cumu):\n",
        "    plt.plot(x, norm.cdf(x, loc, scale), label='norm')\n",
        "  else:  \n",
        "    plt.plot(x, norm.pdf(x, loc, scale), label='norm')\n",
        "  if(show):\n",
        "    plt.show()\n",
        "\n",
        "def plotGamma(a, loc, scale, cumu = False, show = False):\n",
        "  x = np.linspace(gamma.ppf(0.001, a, loc, scale), gamma.ppf(0.999, loc, scale), 100)\n",
        "  if(cumu):\n",
        "    plt.plot(x, gamma.cdf(x, a, loc, scale), label='gamma')\n",
        "  else:\n",
        "    plt.plot(x, gamma.pdf(x, a, loc, scale), label='gamma')\n",
        "  if(show):\n",
        "    plt.show()\n",
        "\n",
        "#Only For n greater than 50 and a = 0.05\n",
        "def evaluateD_a05(D, n):\n",
        "  Dna = 1.35810 / np.sqrt(n)\n",
        "  if(D > Dna):\n",
        "    print('D = {} > Dna = {}, se acepta H0'.format(D, Dna))\n",
        "  else:\n",
        "    print('D = {} &lt; Dna = {},  se rechaza H0'.format(D, Dna))\n",
        "\n",
        "\n"
      ],
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jdtaAwa8XzNA",
        "colab_type": "text"
      },
      "source": [
        "# Estadística para Ciencia de los Datos\n",
        "# Presentado por Esteban García Solís\n",
        "# Tarea #2\n",
        "En esta asignación se busca que los estudiantes exploren un conjunto de datos y determinen qué distribución modela más efectivamente cada columna. En particular, se sugiere que los estudiantes utilicen el conjunto de datos provisto, pero queda abierto a la decisión de los estudiantes usar otros sets de datos .\n",
        "\n",
        "Deberán tomar en cuenta todas las distribuciones vistas en clase pero no deben limitarse a ellas. Se espera que haya un uso significativo de bibliotecas como `matplotlib` para mostrar visualmente la comparación entre los datos y las distribuciones propuestas.\n",
        "\n",
        "Los estudiantes deberán entregar a más tardar el día 29 de setiembre a las 10:00PM un notebook de colab con la solución a los siguientes ejercicios:\n",
        "\n",
        "1. Elija alguna variable **cuantitativa** del set de datos y: \n",
        "  - Muestre el histograma calculado dinámicamente (e.g. con `matplotlib`) (2 puntos)\n",
        "  - Brinde al menos una propuesta de distribución candidata que modela esa columna, definiendo los parámetros de la distribución y una gráfica de la distribución paramétrica con `matplotlib` (10 puntos)\n",
        "  - Proporcione una explicación detallada de qué valores y distribuciones se probaron en el proceso y por qué se hizo la escogencia. (3 puntos)\n",
        "\n",
        "2. Seleccione 3 columnas adicionales (también cuantitativas) y realize el mismo proceso del punto 2 (45 puntos)\n",
        " - Histograma (2 puntos)\n",
        " - Parámetros y gráfica (10 puntos)\n",
        " - Justificación (3 puntos)\n",
        " \n",
        "3. Para cada columna aplicar una prueba KS  con un $\\alpha=0.05$ para determinar si la muestra en efecto sigue la misma distribución que los estudiantes plantean. Si bien es cierto en clase se abordó la prueba KS para pruebas de normalidad, estas también se pueden utilizar para cualquier otro tipo de distribución\n",
        " - 4 columnas x 10 puntos cada una (40 puntos)\n",
        " - Debe mostrarse suficiente detalle gráfico y aritmético en su respuesta\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jbnt8O3bZauc",
        "colab_type": "text"
      },
      "source": [
        "**Generamos una descripción de los datos para facilitar el ajuste de parámetros en las distribuciones.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mNyOF4AvZhCA",
        "colab_type": "code",
        "outputId": "dd09ee59-a2bc-46da-aa48-1c611bb6f89a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 149,
      "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>carat</th>\n",
              "      <th>depth</th>\n",
              "      <th>table</th>\n",
              "      <th>price</th>\n",
              "      <th>x</th>\n",
              "      <th>y</th>\n",
              "      <th>z</th>\n",
              "      <th>unknownCol</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>200.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>200.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.788450</td>\n",
              "      <td>61.788000</td>\n",
              "      <td>57.298500</td>\n",
              "      <td>3751.065000</td>\n",
              "      <td>5.703450</td>\n",
              "      <td>5.711200</td>\n",
              "      <td>3.524900</td>\n",
              "      <td>5.929425</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.492473</td>\n",
              "      <td>1.424959</td>\n",
              "      <td>2.280978</td>\n",
              "      <td>3884.761126</td>\n",
              "      <td>1.122998</td>\n",
              "      <td>1.117658</td>\n",
              "      <td>0.693152</td>\n",
              "      <td>0.857031</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.240000</td>\n",
              "      <td>56.900000</td>\n",
              "      <td>53.000000</td>\n",
              "      <td>394.000000</td>\n",
              "      <td>4.040000</td>\n",
              "      <td>4.060000</td>\n",
              "      <td>2.410000</td>\n",
              "      <td>3.997159</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.407500</td>\n",
              "      <td>61.000000</td>\n",
              "      <td>56.000000</td>\n",
              "      <td>962.500000</td>\n",
              "      <td>4.735000</td>\n",
              "      <td>4.760000</td>\n",
              "      <td>2.897500</td>\n",
              "      <td>5.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.700000</td>\n",
              "      <td>61.800000</td>\n",
              "      <td>57.000000</td>\n",
              "      <td>2354.500000</td>\n",
              "      <td>5.640000</td>\n",
              "      <td>5.680000</td>\n",
              "      <td>3.480000</td>\n",
              "      <td>5.900000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.020000</td>\n",
              "      <td>62.600000</td>\n",
              "      <td>58.250000</td>\n",
              "      <td>4790.250000</td>\n",
              "      <td>6.465000</td>\n",
              "      <td>6.455000</td>\n",
              "      <td>4.010000</td>\n",
              "      <td>6.512438</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2.530000</td>\n",
              "      <td>67.100000</td>\n",
              "      <td>64.000000</td>\n",
              "      <td>17103.000000</td>\n",
              "      <td>8.540000</td>\n",
              "      <td>8.480000</td>\n",
              "      <td>5.400000</td>\n",
              "      <td>8.462215</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            carat       depth       table  ...           y           z  unknownCol\n",
              "count  200.000000  200.000000  200.000000  ...  200.000000  200.000000  200.000000\n",
              "mean     0.788450   61.788000   57.298500  ...    5.711200    3.524900    5.929425\n",
              "std      0.492473    1.424959    2.280978  ...    1.117658    0.693152    0.857031\n",
              "min      0.240000   56.900000   53.000000  ...    4.060000    2.410000    3.997159\n",
              "25%      0.407500   61.000000   56.000000  ...    4.760000    2.897500    5.200000\n",
              "50%      0.700000   61.800000   57.000000  ...    5.680000    3.480000    5.900000\n",
              "75%      1.020000   62.600000   58.250000  ...    6.455000    4.010000    6.512438\n",
              "max      2.530000   67.100000   64.000000  ...    8.480000    5.400000    8.462215\n",
              "\n",
              "[8 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 149
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SRj2nQ8khfaQ",
        "colab_type": "text"
      },
      "source": [
        "##Analisis para la columna \"price\"\n",
        "\n",
        "En este caso como los valores son discretos se decidió probar la distribución geométrica y por la forma del histograma también se probó la distribución exponencial.\n",
        "\n",
        "En el gráfico se aprecia que ambas distribuciones tienen un comportamiento muy similar y aproximan de forma casi idéntica al histograma.\n",
        "\n",
        "para la exponencial se usó $\\lambda = 0.00027$ y para la geométrica $p=0.00027$\n",
        "\n",
        "Se seleciona la distribución **exponencial** por ser continua, lo que puede facilitar los cálculos para la prueba KS.\n",
        "\n",
        "**Prueba KS:**\n",
        "\n",
        "Con $\\alpha = 0.05$, $D = 0.9993 > D_{n,\\alpha} = 0.0960$, se acepta $H_0$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9WfH9XJ9k4f_",
        "colab_type": "code",
        "outputId": "b943922d-59d5-4123-f512-384418ad079b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        }
      },
      "source": [
        "plotGeom(0.00027, 394)\n",
        "plotExpon(0.00027, 394)\n",
        "histPrice = plotHistogram(df['price'].values, 10, \"price\", True)\n",
        "\n",
        "plotExpon(0.00027, 394, True)\n",
        "histPrice = plotHistogram(df['price'].values, 10, \"price\", True, True)\n",
        "\n",
        "(D, pValues) = kstest(df['carat'].values, lambda x: expon.cdf(x, loc=0.24, scale=(1/0.00027)))\n",
        "\n",
        "evaluateD_a05(D, 200)\n"
      ],
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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I6sKSNHXsoO4cO6g7AHc/tov/eWUpG7eNxD2P1ath8uTYfoWFIQYp0s4pgUjM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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ASuDWLd3v7g+4e6W7V3bo0CGbm27Q1GRPhtTcyLOJI3K6XRGRMGQyLDMf6Ja2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\nQiruIiJF6P8BuGlEuk6c/lIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "D = 0.9993818911080556 > Dna = 0.09603217195294503, se acepta H0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "csLlj-QJq3V_",
        "colab_type": "text"
      },
      "source": [
        "##Analisis para la columna \"carat\"\n",
        "\n",
        "Se probaron las distribuciones gamma y exponencial debido a la forma del histograma.\n",
        "\n",
        "Para la exponencial se usó $\\lambda = 1.5$ y para la gamma $\\alpha = 0.91$ y  $\\beta = 1$ \n",
        "\n",
        "Se seleciona la distribución **exponencial** pues se asemeja más a la forma del histograma.\n",
        "\n",
        "**Prueba KS:**\n",
        "\n",
        "Con $\\alpha = 0.05$, $D = 0.0936 &lt; D_{n,\\alpha} = 0.0960$,  se rechaza $H_0$\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "81bL0CKEfmV5",
        "colab_type": "code",
        "outputId": "d31a621a-9a9f-460d-a0af-c156e9b59eb3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        }
      },
      "source": [
        "plotGamma(0.91, 0.24, 1)\n",
        "plotExpon(1.5, 0.24)\n",
        "histCarat = plotHistogram(df['carat'].values, 10, \"carat\", True)\n",
        "\n",
        "plotExpon(1.5, 0.24, True)\n",
        "histCarat = plotHistogram(df['carat'].values, 10, \"carat\", True, True)\n",
        "\n",
        "(D, pValues) = kstest(df['carat'].values, lambda x: expon.cdf(x, loc=0.24, scale=(1/1.5)))\n",
        "\n",
        "evaluateD_a05(D, 200)"
      ],
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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iS4uKiloprI7D7f+WtH6PoOF0KjddSSSYgUeVG4p3c2JFZbzDM8YkkeYk/i1A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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Nx4hIScjrAVUzOxOoAv7Y2OPuPtbdq9y9qmPHjvncdZNmpHswrPY6xqcGNOt+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\nxV1EJIT+PxKeze9jHEHfAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "D = 0.09365402649977028 &lt; Dna = 0.09603217195294503,  se rechaza H0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CBkTR6CRs6zV",
        "colab_type": "text"
      },
      "source": [
        "##Analisis para la columna \"depth\"\n",
        "\n",
        "Se probaron las distribuciones gamma y normal debido a la forma del histograma.\n",
        "\n",
        "Para la normal se usó $\\mu = 61.78$ y $\\sigma = 1.2$. Para la gamma $\\alpha = 9$ y  $\\beta = 0.5$ \n",
        "\n",
        "Se seleciona la distribución **normal** pues se asemeja más a la forma del histograma.\n",
        "\n",
        "**Prueba KS:**\n",
        "\n",
        "Con $\\alpha = 0.05$, $D = 0.0798 &lt; D_{n,\\alpha} = 0.0960$,  se rechaza $H_0$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wFs2ExfPfnHm",
        "colab_type": "code",
        "outputId": "a4e584d5-5e5b-4383-dec2-224454c228db",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        }
      },
      "source": [
        "\n",
        "plotGamma(9, 56.9, 0.5)\n",
        "plotNorm(61.78, 1.424)\n",
        "histDepth = plotHistogram(df['depth'].values, 10, \"depth\", True)\n",
        "\n",
        "\n",
        "plotNorm(61.78, 1.424, True)\n",
        "histDepth = plotHistogram(df['depth'].values, 10, \"depth\", True, True)\n",
        "\n",
        "(D, pValues) = kstest(df['depth'].values, lambda x: norm.cdf(x, loc=61.78, scale=1.424))\n",
        "\n",
        "evaluateD_a05(D, 200)"
      ],
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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O9vSTDO+4gZqr0BdfB+LtJtZ3xvF1R+U5NrxjTAGy0D8LjW5Pf1ayMf1ch744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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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M1BvtuYvUoSlBNx5JHcaZ0RfoEfkq7DjSgKjcRepIwqP8PnEWHSnjd7ExYceRBkbTMiJ1\n5L7UkczyzvwzfrPOHSP1TnvuInVgvrfhzuRx/CLyLodGp4YdRxoglbtIHbgucSoAQ+K6ZJ6EQ+Uu\nkmOvpnbnpWBvzo89RUdbFHYcaaBU7iI5tMbjDE2exra2gP+Ljq95BZE6oi9URXLon6kj+cq35KH4\nDTSyZNhxpAHTnrtIjnzjrRmW7McvIu9yYPSjsONIA6dyF8mRvyYGkiTC1bFRYUcRUbmL5MKUoBtP\nB/szKPo8nSK6fLCET+UuUkuBG0MTp7Eli/hNTJfNk/ygcheppTGpA/jIt+XK+KM01S9RJU+o3EVq\nYZU34ubkAHraZ/SLvBl2HJG1VO4itfCP5FGU0ppr4v/CLOw0Iv+jchfZRAt9c4anjuKoyCT2jHwW\ndhyRH1C5i2yimxMDCDCuiI0OO4rIj6jcRTbB9KArTwX7cWb0RTpFvgs7jsiPqNxFNpI7XJ88iVas\n5DexZ8KOI7JeKneRjfTfoCdvBT24MDaGFrY67Dgi66VyF9kISY9wQ3IgXe0bTopOCDuOSLVU7iIb\nYUzqQD71TlwRG02JpcKOI1ItlbtIllZ7Cbclj2cP+4w+kffCjiOyQSp3kSw9kPoF39CGK+Oj9YMl\nyXsqd5EsLPHNuCfZl0Mj77NP5JOw44jUKKtyN7M+ZjbLzGab2ZXVjOlvZjPNbIaZPZLbmCLhujvZ\nl5U04fLYo2FHEclKjZfZM7MoMAz4OTAPeM/Mxrn7zIwx3YCrgP3cfYmZbVFXgUXq23xvw8jU4RwX\nnciOkXlhxxHJSjZ77nsDs939C3evBB4F+q0z5mxgmLsvAXD30tzGFAnPHcnjAOOi2JNhRxHJWjbl\n3hGYm/F4XnpZph2AHczsTTN728z65CqgSJhmBx14MnUQp0ZfpqMtCjuOSNZqnJbZiNfpBhwMbA28\nbma7uvvSzEFmNggYBNC5c+ccvbVI3flbsj9NWaPTDEjByWbPfT7QKePx1ullmeYB49w94e5fAp9S\nVfY/4O7D3b23u/du167dpmYWqRfTgu14Mdibs2PP08aWhx1HZKNkU+7vAd3MbBszKwFOBNa9UOTT\nVO21Y2ZtqZqm+SKHOUXq3c3JAWzOMs6KvhB2FJGNVmO5u3sSGAy8BHwMPO7uM8zsOjPrmx72ErDI\nzGYCrwKXubsmKKVgvZnahTeDHvw29jSb2Zqw44hstKzm3N19PDB+nWVDMu47cHH6JlLQ3OHmZH+2\nYhEn6+RgUqD0C1WRdbwS9GKad+PC2BgaWyLsOCKbROUukiEInL8l+7ONLeT46MSw44hsMpW7SIZn\nP1zALO/M72JPEtcpfaWAqdxF0hKpgNte/pSd7CuOjrwddhyRWsnVj5hECt7Y9+cxZ9Eq7os/QcQ8\n7DgitaJyl/w1tGW9vVWFx7iz4hZ2t3IOi7xfb+8rUlc0LSMCjE4dwnzacVnscV2IQ4qCyl0avNVe\nwrBkP/axmewXmR52HJGcULlLg/dQ6nDKaM2l8Se01y5FQ+UuDdoKb8w/kkdzUGQae0VmhR1HJGdU\n7tKgPZDqwxKac0nsibCjiOSUyl0arHJvxvDkLzk88h67Rb4MO45ITqncpcG6L3kky2mmy+dJUVK5\nS4O0yJszInUEv4xMYufI3JpXECkwKndpkP6RPJo1lHBRbEzYUUTqhMpdGpxSb8VDqcM5JvIG20cW\nhB1HpE6o3KXBGZbsR4oIF8bGhh1FpM6o3KVBme9tGJ06hBOir9ElUhp2HJE6o3KXBuXvyWMBOD/2\nVMhJROqWyl0ajDlBe55IHcRJ0Ql0sMVhxxGpUyp3aTDuTB5HnCS/iT0TdhSROqdylwbhs6AjTwX7\ncVr032xh5WHHEalzKndpEG5L/oqmVHBO7Lmwo4jUC5W7FL3pQRfGB/twVvQFNrflYccRqRcqdyl6\ntyZPoCUrOCs2PuwoIvVG5S5FbUrQjf8EvRgUe46WtirsOCL1RuUuRe2W5Am0pZwzoi+FHUWkXqnc\npWi9lerOW0EPfhN7hqZWEXYckXqlcpei5A43JQewFYs4KToh7Dgi9U7lLkXplaAX07wbF8bG0NgS\nYccRqXcqdyk6gRt/S/ZnG1vI8dGJYccRCYXKXYrOs8G+zPLOXBR7krilwo4jEgqVuxSVhEe5Nfkr\ndrKvOCrydthxREKTVbmbWR8zm2Vms83syg2MO97M3Mx65y6iSPYeTx3EV74ll8UeJ2IedhyR0NRY\n7mYWBYYBRwDdgYFm1n0945oDFwLv5DqkSDZWewl3JI+nt83ikMjUsOOIhCqbPfe9gdnu/oW7VwKP\nAv3WM+5PwI3AmhzmE8nag6lfUEprrog/ilnYaUTClU25dwTmZjyel162lpn1Ajq5+/MbeiEzG2Rm\nk81scllZ2UaHFalOuTfjnmRfDom8z16RWWHHEQldrb9QNbMIcCtwSU1j3X24u/d2997t2rWr7VuL\nrHVv8iiW04TLYo+FHUUkL2RT7vOBThmPt04v+15zoAfwXzObA/wEGKcvVaW+fOOtGZHqQ9/IJHaO\nzK15BZEGIJtyfw/oZmbbmFkJcCIw7vsn3b3c3du6e1d37wq8DfR198l1klhkHXckjydFlEtjj4cd\nRSRv1Fju7p4EBgMvAR8Dj7v7DDO7zsz61nVAkQ2ZHXTgsdTBnBJ9mU4RfY8j8r1YNoPcfTwwfp1l\nQ6oZe3DtY4lk5+Zkf5qyhsGxp8OOIpJX9AtVKVhTgm68FOzNObHnaKPL54n8gMpdCpI7XJ84iXYs\n4azoC2HHEck7KncpSC8GezHFd+Ti2JO6EIfIeqjcpeBUepQbkwPZweZyQvS1sOOI5CWVuxScUanD\nmONbclVsNDELwo4jkpdU7lJQyr0pdyaP46eR6RwcmRZ2HJG8pXKXgnJX8hiW0ozfx0bp5GAiG6By\nl4IxJ2jPg6k+/Cr6Oj0iX4UdRySvqdylYNyQHEicJJfpNAMiNVK5S0GYlNqZl4K9OS82ji1sadhx\nRPKeyl3yXsqNPydPoQPfcXZ0g5cMEJE0lbvkvcdSP2OGb8NV8UdobImw44gUBJW75LVyb8bfkv3Z\n2z7mqMjbYccRKRgqd8lrtyePYymb8cf4Qzr0UWQjqNwlb30WdOSh1OGcGP0Pu+jQR5GNonKXvOTu\nDEmezmas5tLYE2HHESk4KnfJS89+uJBJwS5cFnuMzXWudpGNpnKXvLOiIsmfn5vJbvY5A6P/CTuO\nSEHK6jJ7IvXpjlc+pWxFBffFHyBqHnYckYKkPXfJKx8vXMaIN+dw4l6d2D3yRdhxRAqWyl3yRhA4\nV439iFZN4lzRZ6ew44gUNJW75I1R737NtLlLueao7rRqWhJ2HJGCpnKXvFC6bA03vfAJ+2/fln49\nO4QdR6TgqdwlL1z77EwqUgF/OqYHpp+iitSayl1C9+L0b3j+o4VceGg3tmnbLOw4IkVB5S6hKl+V\n4JpnptN9qxYMOnDbsOOIFA0d5y6h+vPzM1m8spIHTt+LeFT7GiK5on9NEprXPy3jiSnzOOfAbenR\nsWXYcUSKispdQlG+OsHlT37I9ltsxgWHdgs7jkjRUblLKK4dN4OyFRXc2n93GsejYccRKToqd6l3\nL07/hrFT5zP4Z9uz29atwo4jUpT0harUbGju5sPLvAV/qLiRXWwxg984Fd5M5ey1ReR/stpzN7M+\nZjbLzGab2ZXref5iM5tpZh+a2QQz65L7qFLo3OGyxLmsoAm3xe8mbip2kbpSY7mbWRQYBhwBdAcG\nmln3dYZNBXq7+27Ak8BNuQ4qhe/B1C/4b9CTq2P/YofI/LDjiBS1bPbc9wZmu/sX7l4JPAr0yxzg\n7q+6+6r0w7eBrXMbUwrdJ0EnbkgO5LDIFE6JvhJ2HJGil025dwTmZjyel15WnbOAF9b3hJkNMrPJ\nZja5rKws+5RS0FZ5IwYnzqclK7kxPhydOkak7uX0aBkzOwXoDdy8vufdfbi793b33u3atcvlW0ue\ncoerE2fyhXfgjvgw2uh6qCL1IpujZeYDnTIeb51e9gNmdhjwB+Agd6/ITTwpdI+nDmZscAAXxZ7g\np9GZYccRaTCy2XN/D+hmZtuYWQlwIjAuc4CZ7QHcC/R199Lcx5RCNDPozJDk6ewf+YjB0afDjiPS\noNRY7u6eBAYDLwEfA4+7+wwzu87M+qaH3QxsBjxhZtPMbFw1LycNxBLfjHMSF9Oa5dwWH6YLXYvU\ns6x+xOTu44Hx6ywbknH/sBznkgKWcuOCxGC+9dY8VnId7WxZ2JFEGhz9QlVy7qbkACYGu3FjbDh7\nRD4PO45Ig6Rzy0hOPZk6gHtTfTk5+goDYv8NO45Ig6Vyl5x5J9iJqxJn89PIdIbGRoYdR6RBU7lL\nTswJ2nNO5UV0slLuid+h88aIhEzlLrX2nbfg9MQVAIyI30xLWxlyIhHRF6pSKyu9EWdWXsY33ppR\nJdfTNfJt2JFEBJW71EKlRzk3cREzvCvD47eyZ+SzsCOJSJqmZWSTJD3ChYnBTAx244bY/RwanRp2\nJBHJoHKXjZZy45LEebwQ7MM1sYfoH3st7Egisg6Vu2yUlBtXJAfxTLAfV8RGc1bsxbAjich6aM5d\nspb0CJckzuOZYD9+F3uS82LPhh1JRKqhcpesVHqUCxLn82KwN5fFHuW3MZ0bTiSfqdylRiu9Eecl\nfsfrwe4MiT3EmZqKEcl7KnfZoLLlFZxZeQ0zvQs3xe7Vl6ciBULlLtWaXbqcMx+cTJl34L74LRwS\nnRZ2JBHJkspd1uvVT0q5YPRUGsUjPFLyF526V6TA6FBI+QF3597XPufMke/RafOmPDN4fxW7SAHS\nnrusVb4qwaVPfsDLM7/lyF235G8n7E7TEv0nIlKI9C9XAJg2dynnj36fhUvXcM1R3Tlzv66YWdix\nRGQTqdwbuEQq4K7/zOauV2ezZYvGPH7uvvTq3DrsWCJSSyr3QjG0Zc5f8pOgE5cnBvGhb8dxkYn8\ncfVIWo5YlfP3EZH6p3JvgNZ4nL8nj+Xe1FG0YBV3x2/nyOi7YccSkRxSuTcg7vDvoDd/SZ7M196e\n4yOvcXV8FK1tRdjRRCTHVO4NxIygC9cnT+bNoAfdbB6j4n9hv+iMsGOJSB1RuRe5z4OtuDX5K54P\n9qUVy7ku9gAnRScQsyDsaCJSh1TuRWp60JV7kkfzQrAPTajgguhY/i/2PC1sddjRRKQeqNyLSMqN\nCUEvRqZ+wZtBD5qzikHR5zg79jxtbHnY8USkHqnci8B8b8PY1AE8mvwZ82nHVizi8thoTom+oj11\nkQZK5b6x6uB4802xxDfjpVRvng1+yltBd5wI+0ZmcHX0X/w8MkVz6iINnMq9gMwN2jEh2IMJQS8m\nBd1JEqOLfcOFsbEcH5lIp0hZ2BFFJE+o3PNYqbdkcrAjbwW78GbQgy99KwC2s/mcFR3P0dG32cXm\noFPAiMi6VO55YqU34hPvzIfBtnwYbMv73o2vfEsAmrGan0Q+5tToyxwSmUrXyLchpxWRfKdyr0fu\nUEZLvvb2fBlsyefegdnegU+9E197+7XjtmAJPSOzOTk6gT0jn7KbfUHcUiEmF5FCk1W5m1kf4A4g\nCtzv7n9d5/lGwEPAnsAiYIC7z8lt1PwVuLGMpizx5iyiBd95C8q8FaXemm9pxUJvw4L0bTWN164X\nJ0kX+4Zd7UtOiL7GTvY1u0W+oL0tDXFrRKQY1FjuZhYFhgE/B+YB75nZOHefmTHsLGCJu29vZicC\nNwID6iIwsElHrFR4jLm+BUmiJImQJEaCGAmPUkmMSuJr/6zwOGsoYQ0lrPYSVtOIlTRmtTdiORez\nkiYs96YsoynLvCnlNMPXc1GrCAFtKKeDLWYHm8fBkQ/obKV0tm/pat/SyUp1VIuI1Ils9tz3Bma7\n+xcAZvYo0A/ILPd+wND0/SeBu8zM3N1zmLVW5nk7Dqv82yat24Q1NKOCJlbBZqyiOatpZ0vZnvk0\nj6yiFStpZctpbStowzLaWDntrJw2LFN5i0gosin3jsDcjMfzgH2qG+PuSTMrB9oA32UOMrNBwKD0\nwxVmNmtTQm+aZcBRuXzBtqyzfUWuIW1vQ9pW0PbWv2trdYhbl2wG1esXqu4+HBhen+9ZV8xssrv3\nDjtHfWlI29uQthW0vcXqxxPFPzYf6JTxeOv0svWOMbMY0JKqL1ZFRCQE2ZT7e0A3M9vGzEqAE4Fx\n64wZB5yWvv8r4D/5NN8uItJta9LTAAADf0lEQVTQ1Dgtk55DHwy8RNWhkCPcfYaZXQdMdvdxwD+B\nh81sNrCYqv8BFLuimF7aCA1pexvStoK2tyiZdrBFRIpPNtMyIiJSYFTuIiJFSOWeJTObY2Yfmdk0\nM5ucXtbTzN7+fpmZ7R12zlwws1Zm9qSZfWJmH5vZvma2uZm9bGafpf9sHXbOXKlme29OP/7QzJ4y\ns1Zh58yV9W1vxnOXmJmbWdswM+ZKddtqZuenl80ws5vCzlkXVO4b52fu3jPjGNmbgGvdvScwJP24\nGNwBvOjuOwG7Ax8DVwIT3L0bMCH9uFisb3tfBnq4+27Ap8BVIebLtfVtL2bWCTgc+DrEbLn2o201\ns59R9av63d19F2DTfrqe51TuteNAi/T9lsCCELPkhJm1BA6k6ggo3L3S3ZdS9Y9hZHrYSOCYcBLm\nVnXb6+7/dvdketjbVP2+o+Bt4O8X4Dbgcqr+uy54G9jW84C/untFenlpeCnrjso9ew7828ympE+j\nAPA74GYzm0vV//2LYe9uG6AMeMDMpprZ/WbWDGjv7gvTY74B2lf7CoWluu3NdCbwQv1HqxPr3V4z\n6wfMd/cPQs6XS9X93e4AHGBm75jZa2a2V7gx64bKPXv7u3sv4Ajgt2Z2IFV7ABe5eyfgItJ7CAUu\nBvQC7nH3PYCVrDMFk/6BWlHs3VHD9prZH4AkMCqceDm3vu0dCvyeqqnFYlLd320M2Bz4CXAZ8LhZ\n8V3PTOWeJXefn/6zFHiKqrNlngaMTQ95Ir2s0M0D5rn7O+nHT1L1D+RbM9sKIP1nsXyUrW57MbPT\nqTrb3MlF9Ivr6rZ3G+ADM5tD1RTU+2a2ZTgRc6a6bZ0HjPUq7wIBVScTKyoq9yykP7Y2//4+VV86\nTadqjv2g9LBDgM/CSZg77v4NMNfMdkwvOpSq0ztnnmLiNOCZEOLlXHXbm75AzeVAX3dfFVrAHKtm\ne9939y3cvau7d6Wq/HqlxxasDfy3/DTwMwAz2wEoIeyzRNYBXWYvO+2Bp9Kf3GLAI+7+opmtAO5I\nnyxtDf87nXGhOx8YlT6X0BfAGVTtCDxuZmcBXwH9Q8yXa+vb3veARsDL6b/3t9393PAi5tT6trdY\nrW9bVwIjzGw6UAmcVkSfzNbS6QdERIqQpmVERIqQyl1EpAip3EVEipDKXUSkCKncRUSKkMpdRKQI\nqdxFRIrQ/wNAWqAyWpiDpAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "D = 0.07979081359897755 &lt; Dna = 0.09603217195294503,  se rechaza H0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fMf0ecJ-tpZe",
        "colab_type": "text"
      },
      "source": [
        "##Analisis para la columna \"table\"\n",
        "\n",
        "Se probaron las distribuciones gamma y normal debido a la forma del histograma.\n",
        "\n",
        "Para la normal se usó $\\mu = 57.29$ y $\\sigma = 2.5$. Para la gamma $\\alpha = 2.6$ y  $\\beta = 1.7$ \n",
        "\n",
        "Se seleciona la distribución **gamma** pues se asemeja más a la forma del histograma.\n",
        "\n",
        "**Prueba KS:**\n",
        "\n",
        "Con $\\alpha = 0.05$, $D = 0.1496 > D_{n,\\alpha} = 0.0960$, se acepta $H_0$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LvWmnJY4fnRG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        },
        "outputId": "96246c58-912d-483c-ff8c-1a7d1a1e7891"
      },
      "source": [
        "plotNorm(57.29, 2.5)\n",
        "plotGamma(2.6, 53, 1.7)\n",
        "histTable = plotHistogram(df['table'].values, 10, \"table\", True)\n",
        "\n",
        "plotGamma(2.6, 53, 1.7, True)\n",
        "histTable = plotHistogram(df['table'].values, 10, \"table\", True, True)\n",
        "\n",
        "(D, pValues) = kstest(df['table'].values, lambda x: gamma.cdf(x, 2.6, loc=53, scale=1.7))\n",
        "\n",
        "evaluateD_a05(D, 200)"
      ],
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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RRn7U5O3HqzAmufoPWqz3eQkAV8RoNrpkeDYO0ZlPlUom8QSEDcB4ERktImnA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\n/x+H21utsb9CQQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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cVZb7A9FRrKcdo8Onko4iItIqijrlb9kp8suum+MOd0TDONjmcmDwbglDiYiU\nj6rbc/+b78dc78XXtNcuIilWdeV+Z3YYnVnLCJ1LRkRSrKrKfbl35vF4MKeHz9LeGpOOIyLSaqqq\n3O+LjqGRGk3JiEjqVU25R27cGR3LYHuNfsHCpOOIiLSqqin3CfEQ3vXdOCczMekoIiKtrirKPXbj\nuuwp7GMLGB5MSTqOiEirq4pyfzIeyBzvw3czDxOYJx1HRKTVpb7c3eG67Cn0sfcZEejwRxGpDqkv\n97/EB/GKf4bvhA+TsTjpOCIibSLV5e4Ov8meyu4s57Tw2aTjiIi0mVSX+/j4CKb6vnwn87DO/igi\nVSW15b7CO3Fl0zc42N7iq/rQkohUmco8K2QRfpr9KivpxG01PyPUETIiUmVSuef+16g/f4yGcn74\nGP2D95KOIyLS5lJX7iu9I5dmv8WetpgLM/cnHUdEJBGpmpb50NtzduPFLPKu3Fb7M+qsKelIIiKJ\nSE25r/V2nNt4EbN8T26ouZYhwetJRxIRSUxR0zJmNtzM5pjZXDO7uJn725nZPfn7XzKzvqUOujVz\n4z04t/EiZvg+/KbmNxwXvtyWqxcRKTvb3HM3sxC4HjgeWABMMbPx7j67YNh5wAp338fMzgR+Dnyl\nNQIX+nu8F9dnRzExbqCORn5V81tOCnViMBGRYqZlBgNz3f1tADO7GxgFFJb7KOCK/PX7gOvMzNy9\n1Y5BfDneh9Mar6QzaxkbPsy5mSfYxVa31upERCpKMeXeE5hfcHsBMGRLY9w9a2argK7AssJBZjYG\nGJO/ucbM5mxP6JyXgS8B8O/5S4l0Y7PcKVN52/cja8noytu+lkn79kH6t3FHt2/PYga16Ruq7j4O\nGNeW62wpM5vq7g1J52gt2r7Klvbtg/RvY1ttXzFvqC4Eehfc7pVf1uwYM8sA9cDyUgQUEZGWK6bc\npwD9zGwvM6sFzgTGbzZmPHB2/vqXgT+15ny7iIhs3TanZfJz6GOBiUAI3OTus8zsSmCqu48HbgRu\nM7O5wAfkfgFUqrKeNioBbV9lS/v2Qfq3sU22z7SDLSKSPqk7t4yIiKjcRURSqarL3czmmdnfzWyG\nmU3NL7vCzBbml80ws5OTzrm9zKyLmd1nZq+b2WtmdoSZ7WJmk83szfzPnZPOuSO2sI2peA3NbN+C\nbZhhZh+a2YVpeQ23sn2peP0AzOxfzWyWmb1qZneZWV3+4JSX8qdruSd/oErp113Nc+5mNg9ocPdl\nBcuuANa4+9VJ5SoVM7sFeNbd/5D/H6gDcCnwgbv/V/48QTu7+/9LNOgO2MI2XkhKXsON8qcBWUju\nA4TfJUWvIXxq+84lBa+fmfUlMuPEAAACgUlEQVQEngP6u/tHZnYvMAE4GXjA3e82sxuAme7+u1Kv\nv6r33NPMzOqBo8kdyYS7N7r7SnKnirglP+wW4JRkEu64rWxjGg0D3nL3d0nRa1igcPvSJAO0z3/+\npwPwD+BYcqdpgVZ8/aq93B2YZGbT8qdG2Gismb1iZjdV6p+8wF7AUuD/zGy6mf3BzDoCu7r7P/Jj\nFgO7JpZwx21pGyEdr2GhM4G78tfT9BpuVLh9kILXz90XAlcD75Er9VXANGClu2fzwxaQO31LyVV7\nuR/l7gOBk4DvmtnRwO+AzwCHkHtBrkkw347IAAOB37n7ocBaYJPTNec/aFbJ83Jb2sa0vIYA5Keb\nRgJ/3Py+FLyGzW1fKl6//C+lUeR2QvYAOgLD22r9VV3u+d+suPsS4EFgsLu/7+6Ru8fA78mdFbMS\nLQAWuPtL+dv3kSvC981sd4D8zyUJ5SuFZrcxRa/hRicBL7v7+/nbaXoNYbPtS9Hrdxzwjrsvdfcm\n4AHgSKBLfpoGmj+dS0lUbbmbWUcz67zxOnAC8OrGfzR5pwKvJpFvR7n7YmC+me2bXzSM3GmaC08V\ncTbwcALxSmJL25iW17DAaDadskjNa5i3yfal6PV7DzjczDqYmfHJv8GnyZ2mBVrx9avao2XMbG9y\ne+uQ+/P+Tnf/iZndRu7PQQfmARcUzG9WFDM7BPgDUAu8Te4ohAC4F+gDvAuc4e4fJBZyB21hG/+H\n9LyGHcmVxN7uviq/rCspeQ23sH1p+jf4I3JfXJQFpgPfIjfHfjewS37ZWe6+oeTrrtZyFxFJs6qd\nlhERSTOVu4hICqncRURSSOUuIpJCKncRkRRSuYuIpJDKXUQkhf4/QFF177gFRhQAAAAASUVORK5C\nYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "D = 0.14961652564906006 > Dna = 0.09603217195294503, se acepta H0\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}