Box plot.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/galexbh/varianza-test/blob/master/Box%20plot.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "900ea98a-e621-4fdc-9e72-5759a87701c3",
      "metadata": {
        "id": "900ea98a-e621-4fdc-9e72-5759a87701c3"
      },
      "source": [
        "# UNAH\n",
        "## Facultad de Ingenieria\n",
        "## Carrera de Ingenieria en sistemas\n",
        "### Clase: Inteligencia Artificial\n",
        "\n",
        "*Prueba*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 246,
      "id": "d89d7223-d7bc-4a2f-b8e2-7777b3abbbc9",
      "metadata": {
        "id": "d89d7223-d7bc-4a2f-b8e2-7777b3abbbc9"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a6101627-a1bb-41ae-9a9c-6597e20ed26b",
      "metadata": {
        "id": "a6101627-a1bb-41ae-9a9c-6597e20ed26b"
      },
      "source": [
        "Segun el siguiente Dataset:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 247,
      "id": "129c774e-4da0-4b46-918a-19e51df03fe3",
      "metadata": {
        "id": "129c774e-4da0-4b46-918a-19e51df03fe3"
      },
      "outputs": [],
      "source": [
        "pd_vector = pd.DataFrame( { \"num\" : range(1,101) } )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 248,
      "id": "48d3c5b7-be61-4c2f-ada7-d9766b5d82db",
      "metadata": {
        "id": "48d3c5b7-be61-4c2f-ada7-d9766b5d82db",
        "outputId": "099e0088-1a12-4e26-b97e-ed923a42c794",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    num\n",
              "0     1\n",
              "1     2\n",
              "2     3\n",
              "3     4\n",
              "4     5\n",
              "..  ...\n",
              "95   96\n",
              "96   97\n",
              "97   98\n",
              "98   99\n",
              "99  100\n",
              "\n",
              "[100 rows x 1 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-08a4fa57-0f5b-4e58-9c84-0cbc1f9ed67f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>num</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>97</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 1 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-08a4fa57-0f5b-4e58-9c84-0cbc1f9ed67f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-08a4fa57-0f5b-4e58-9c84-0cbc1f9ed67f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-08a4fa57-0f5b-4e58-9c84-0cbc1f9ed67f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 248
        }
      ],
      "source": [
        "pd_vector"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "68d2dfb1-9c1f-4116-89c5-05e2171c7018",
      "metadata": {
        "id": "68d2dfb1-9c1f-4116-89c5-05e2171c7018"
      },
      "source": [
        "Y segun los siguientes metodos de numpy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 249,
      "id": "81d3dd14-6205-421a-9cf9-031027c6a213",
      "metadata": {
        "id": "81d3dd14-6205-421a-9cf9-031027c6a213",
        "outputId": "e336a956-2e5e-4881-c669-9d0d0462b721",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "833.25"
            ]
          },
          "metadata": {},
          "execution_count": 249
        }
      ],
      "source": [
        "np.var( pd_vector[\"num\"] ) ## Calcula la varianza"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 250,
      "id": "4b17ddd3-044d-4328-9f34-03bde615cc7a",
      "metadata": {
        "id": "4b17ddd3-044d-4328-9f34-03bde615cc7a",
        "outputId": "050825c3-3252-4e8a-add5-7792ff9be695",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "28.86607004772212"
            ]
          },
          "metadata": {},
          "execution_count": 250
        }
      ],
      "source": [
        "np.std( pd_vector[\"num\"] ) ## Calcula la desviacion estandar"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b0db84f8-c351-41db-b981-336b6738b19b",
      "metadata": {
        "id": "b0db84f8-c351-41db-b981-336b6738b19b"
      },
      "source": [
        "## Responda las siguientes ejercicios"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5d99d524-4a87-4fbf-a2b8-436a6cb4ddcd",
      "metadata": {
        "id": "5d99d524-4a87-4fbf-a2b8-436a6cb4ddcd"
      },
      "source": [
        "1. Grafique el Boxplot del vector \"num\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 251,
      "id": "f36238bb-56d3-4bdb-9108-bb652a2c2f9c",
      "metadata": {
        "id": "f36238bb-56d3-4bdb-9108-bb652a2c2f9c",
        "outputId": "42945c53-8dad-4ff0-b3c0-b9ba53ee71ba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc5128cb110>"
            ]
          },
          "metadata": {},
          "execution_count": 251
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "pd_vector.boxplot(column=['num'])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd_vector.describe()"
      ],
      "metadata": {
        "id": "xNteS1O3Tb-H",
        "outputId": "5cfe1e01-dcb2-4421-b134-c4ca12fbf86f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "id": "xNteS1O3Tb-H",
      "execution_count": 252,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              num\n",
              "count  100.000000\n",
              "mean    50.500000\n",
              "std     29.011492\n",
              "min      1.000000\n",
              "25%     25.750000\n",
              "50%     50.500000\n",
              "75%     75.250000\n",
              "max    100.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-af3af811-2692-45f4-bacd-7e710bdd2500\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>num</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>100.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>50.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>29.011492</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>25.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>50.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>75.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>100.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-af3af811-2692-45f4-bacd-7e710bdd2500')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-af3af811-2692-45f4-bacd-7e710bdd2500 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-af3af811-2692-45f4-bacd-7e710bdd2500');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 252
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c3011ce8-15f1-4b3b-b7ab-44172bf7c8a2",
      "metadata": {
        "id": "c3011ce8-15f1-4b3b-b7ab-44172bf7c8a2"
      },
      "source": [
        "2. Calcule la varianza y desviacion estandard de cada uno de los quartiles\n",
        "\n",
        "Nota: realice manual la divicion del dataset para obtener cada uno de quartiles, al final obtendra cuatro dataframes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 253,
      "id": "8b18a52f-71b7-4760-81c3-7728e69613ce",
      "metadata": {
        "id": "8b18a52f-71b7-4760-81c3-7728e69613ce"
      },
      "outputs": [],
      "source": [
        "Q1 = pd_vector.iloc[0:25]\n",
        "Q2 = pd_vector.iloc[25:50]\n",
        "Q3 = pd_vector.iloc[50:75]\n",
        "Q4 = pd_vector.iloc[75:100]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import widgets\n",
        "grid = widgets.Grid(2, 2, header_row=True, header_column=True)\n",
        "with grid.output_to(0, 0):\n",
        "    Q1.boxplot()\n",
        "with grid.output_to(0, 1):\n",
        "    Q2.boxplot()\n",
        "with grid.output_to(1, 0):\n",
        "    Q3.boxplot()\n",
        "with grid.output_to(1, 1):\n",
        "    Q4.boxplot()"
      ],
      "metadata": {
        "id": "Z_EJu8I5i4hm",
        "outputId": "105e7539-dca9-4388-a58c-c1cb4da30a06",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        }
      },
      "id": "Z_EJu8I5i4hm",
      "execution_count": 254,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<style>\n",
              "       table#id24, #id24 > tbody > tr > th, #id24 > tbody > tr > td {\n",
              "         border: 1px solid lightgray;\n",
              "         border-collapse:collapse;\n",
              "         \n",
              "        }</style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<table id=id24><tr><th id=id24-0-0></th><th id=id24-0-1></th></tr><tr><th id=id24-1-0></th><td id=id24-1-1></td></tr></table>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"8b7fd282-5411-11ed-bcd1-0242ac1c0002\"] = google.colab.output.getActiveOutputArea();\n",
              "//# sourceURL=js_8de264fae1"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"8b8008f6-5411-11ed-bcd1-0242ac1c0002\"] = document.querySelector(\"#id24-0-0\");\n",
              "//# sourceURL=js_856865fb5c"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"8b806652-5411-11ed-bcd1-0242ac1c0002\"] = google.colab.output.setActiveOutputArea(window[\"8b8008f6-5411-11ed-bcd1-0242ac1c0002\"]);\n",
              "//# sourceURL=js_ed0c335526"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"8b90ec34-5411-11ed-bcd1-0242ac1c0002\"] = google.colab.output.setActiveOutputArea(window[\"8b7fd282-5411-11ed-bcd1-0242ac1c0002\"]);\n",
              "//# sourceURL=js_e40b10ff70"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"8b94b76a-5411-11ed-bcd1-0242ac1c0002\"] = google.colab.output.getActiveOutputArea();\n",
              "//# sourceURL=js_4fcbccbd75"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"8b955454-5411-11ed-bcd1-0242ac1c0002\"] = document.querySelector(\"#id24-0-1\");\n",
              "//# sourceURL=js_9454aed8c6"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"8b9613c6-5411-11ed-bcd1-0242ac1c0002\"] = google.colab.output.setActiveOutputArea(window[\"8b955454-5411-11ed-bcd1-0242ac1c0002\"]);\n",
              "//# sourceURL=js_291dd88e06"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"8c6dbf88-5411-11ed-bcd1-0242ac1c0002\"] = google.colab.output.setActiveOutputArea(window[\"8b94b76a-5411-11ed-bcd1-0242ac1c0002\"]);\n",
              "//# sourceURL=js_674a7cd7b7"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"8c6fb9fa-5411-11ed-bcd1-0242ac1c0002\"] = google.colab.output.getActiveOutputArea();\n",
              "//# sourceURL=js_25134a9609"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"8c700504-5411-11ed-bcd1-0242ac1c0002\"] = document.querySelector(\"#id24-1-0\");\n",
              "//# sourceURL=js_e541ef0db2"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"8c70496a-5411-11ed-bcd1-0242ac1c0002\"] = google.colab.output.setActiveOutputArea(window[\"8c700504-5411-11ed-bcd1-0242ac1c0002\"]);\n",
              "//# sourceURL=js_06696004ec"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"8c8142e2-5411-11ed-bcd1-0242ac1c0002\"] = google.colab.output.setActiveOutputArea(window[\"8c6fb9fa-5411-11ed-bcd1-0242ac1c0002\"]);\n",
              "//# sourceURL=js_2eb9c25a11"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"8c836e32-5411-11ed-bcd1-0242ac1c0002\"] = google.colab.output.getActiveOutputArea();\n",
              "//# sourceURL=js_d0085e342a"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"8c83b1e4-5411-11ed-bcd1-0242ac1c0002\"] = document.querySelector(\"#id24-1-1\");\n",
              "//# sourceURL=js_2a9a94d17c"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"8c83fb9a-5411-11ed-bcd1-0242ac1c0002\"] = google.colab.output.setActiveOutputArea(window[\"8c83b1e4-5411-11ed-bcd1-0242ac1c0002\"]);\n",
              "//# sourceURL=js_64ca463dfc"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"8c93a82e-5411-11ed-bcd1-0242ac1c0002\"] = google.colab.output.setActiveOutputArea(window[\"8c836e32-5411-11ed-bcd1-0242ac1c0002\"]);\n",
              "//# sourceURL=js_59e7bcdc58"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"sample variance of Q1\", np.var(Q1[\"num\"], ddof=1)) #ddof -> Grados de libertad\n",
        "print(\"sample variance of Q2\", np.var(Q2[\"num\"], ddof=1))\n",
        "print(\"sample variance of Q3\", np.var(Q3[\"num\"], ddof=1))\n",
        "print(\"sample variance of Q4\", np.var(Q4[\"num\"], ddof=1))"
      ],
      "metadata": {
        "id": "Lg_0Jnr5jS8L",
        "outputId": "e25f77b5-e6ff-488b-ed22-bb44a9a8ae22",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "Lg_0Jnr5jS8L",
      "execution_count": 255,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "sample variance of Q1 54.166666666666664\n",
            "sample variance of Q2 54.166666666666664\n",
            "sample variance of Q3 54.166666666666664\n",
            "sample variance of Q4 54.166666666666664\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Sample standard deviation of Q1\", np.std(Q1[\"num\"], ddof=1))\n",
        "print(\"Sample standard deviation of Q2\", np.std(Q2[\"num\"], ddof=1))\n",
        "print(\"Sample standard deviation of Q3\", np.std(Q3[\"num\"], ddof=1))\n",
        "print(\"Sample standard deviation of Q4\", np.std(Q4[\"num\"], ddof=1))"
      ],
      "metadata": {
        "id": "HInV-X6x6Hfh",
        "outputId": "68e7e619-7247-4031-8eef-ccd7db454576",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "HInV-X6x6Hfh",
      "execution_count": 256,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sample standard deviation of Q1 7.359800721939872\n",
            "Sample standard deviation of Q2 7.359800721939872\n",
            "Sample standard deviation of Q3 7.359800721939872\n",
            "Sample standard deviation of Q4 7.359800721939872\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b1aac673-aa4f-40c4-8ae2-364247dcaad1",
      "metadata": {
        "id": "b1aac673-aa4f-40c4-8ae2-364247dcaad1"
      },
      "source": [
        "3. Explique la respuesta que obtuvo en el ejercicio dos, recuerde que se puede apoyar del grafico del ejercicio uno para hacer referencias sobre distribucion en caso de que sea necesario."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "En términos de varianza muestral y desviación estándar muestral, reflejan que los datos tienen un grado de dispersión bastante **alto**, ya que los datos de 1 a 100 (muy pequeño a muy grande) afectan el grado de dispersión de los datos datos.\n",
        "\n",
        "Aunque el diagrama de caja de medidas discretas (boxplot) que se usó en el ejercicio 1 no parece tener valores atípicos, sí sugiere que los hay las medidas que se utilizaron en cada muestra del ejercicio 2.\n",
        "\n",
        "Además, el hecho de que todos los valores de Qn sean iguales se debe al promedio de las muestras."
      ],
      "metadata": {
        "id": "JO9-jR26X33S"
      },
      "id": "JO9-jR26X33S"
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.8"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}