Testes de Independência.ipynb

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    "colab": {
      "name": "Trabalho de Estatística",
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      "include_colab_link": true
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    "kernelspec": {
      "name": "ir",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/silveiraluiza/analise-covid-19/blob/master/Testes%20de%20Independ%C3%AAncia.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_Dy6-Vlt8gIF"
      },
      "source": [
        "## Testes de Independência\n",
        "\n",
        "Os testes de independência são utilizados para determinar se existe uma relação significativa entre duas variáveis categóricas. Existem dois tipos diferentes de testes de independência:\n",
        "- o teste do qui-quadrado (o mais comum)\n",
        "- o teste exacto do Fisher\n",
        "\n",
        "Por um lado, o teste do qui-quadrado é utilizado quando a amostra é suficientemente grande (neste caso o valor p é uma aproximação que se torna exata quando a amostra se torna infinita, o que é o caso de muitos testes estatísticos). Por outro lado, o teste exato de Fisher é utilizado quando a amostra é pequena (e neste caso o valor p é exato e não é uma aproximação).\n",
        "\n",
        "A literatura indica que a regra habitual para decidir se a aproximação χ2 é suficientemente boa é que o teste Qui-quadrado não é apropriado quando os valores esperados numa das células da tabela de contingência são inferiores a 5, e neste caso é preferível o teste exacto de Fisher (McCrum-Gardner 2008; Bower 2003).\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WPqYmHsx8410"
      },
      "source": [
        "### Hipóteses\n",
        "\n",
        "As hipóteses do teste exato do Fisher são as mesmas que para o teste do qui-quadrado, ou seja:\n",
        "\n",
        "- H0 : as variáveis são independentes, não há relação entre as duas variáveis categóricas. O conhecimento do valor de uma variável não ajuda a prever o valor da outra variável\n",
        "- H1 : as variáveis são dependentes, existe uma relação entre as duas variáveis categóricas. O conhecimento do valor de uma variável ajuda a prever o valor da outra variável\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3tkz3LbzcqTY"
      },
      "source": [
        "## Exemplo:\n",
        "\n",
        "De maneira geral, os doentes psiquiátricos podem ser classificados em psicóticos e neuróticos. Um psiquiatra realiza um estudo sobre os sintomas suicidas em duas amostras de 20 doentes de cada grupo. A nossa hipótese é que a proporção de psicóticos com sintomas suicidas é igual a proporção de neuróticos com estes sintomas (em um teste de independência, a hipótese nula seria, a presença ou ausência de sintomas suicidas é independente do tipo de doente envolvido)\n",
        "\n",
        "> Fonte: http://www.portalaction.com.br/tabela-de-contingencia/51-aplicacao-do-teste-de-fisher"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Db33YAgZYvPG"
      },
      "source": [
        "linhas <- c(\"Presente\", \"Ausente\")\n",
        "colunas <- c(\"Psicótico\", \"Neurótico\")\n",
        "\n",
        "tabela <- matrix(c(2,6,18,14),ncol=2,byrow=TRUE)\n",
        "colnames(tabela) <- colunas\n",
        "rownames(tabela) <- linhas\n",
        "tabela <- as.table(tabela)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OSWUiM0haJd8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "outputId": "c2deb56a-b062-4d9c-eb33-bb21f1243f6a"
      },
      "source": [
        "counts <- tabela\n",
        "barplot(counts,\n",
        "  xlab=\"Número de Pacientes\", col=c(\"darkblue\",\"red\"),\n",
        "  legend = rownames(counts), beside=TRUE)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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ACUL6mPTeTk5Nxxxx0X/bPBgwf369ev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\nHwAAAABJRU5ErkJggg==",
            "text/plain": [
              "plot without title"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j8tjT1HBaPKK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "ba4c28bd-8cfc-49ca-e1c1-db71239cb0b9"
      },
      "source": [
        "test <- fisher.test(tabela)\n",
        "test"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\n",
              "\tFisher's Exact Test for Count Data\n",
              "\n",
              "data:  tabela\n",
              "p-value = 0.2351\n",
              "alternative hypothesis: true odds ratio is not equal to 1\n",
              "95 percent confidence interval:\n",
              " 0.02308176 1.80076397\n",
              "sample estimates:\n",
              "odds ratio \n",
              " 0.2680041 \n"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9HaUqorqarbv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e8285f44-9de8-4c5f-d734-83640fb5ee99"
      },
      "source": [
        "test$p.value"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "[1] 0.2351162"
            ],
            "text/latex": "0.235116235116235",
            "text/markdown": "0.235116235116235",
            "text/html": [
              "0.235116235116235"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oAKtu7z1bJfV"
      },
      "source": [
        "A partir das informações do teste e do valor do test$p.value vemos que o p-valor é superior ao nível de significância de 5%, portanto não podemos rejeitar a hipótese nula."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0kTchhwUeI5p"
      },
      "source": [
        "## Exemplo:\n",
        " Considere o processo de produção de uma fibra sintética, no qual o experimentador quer conhecer a influência da porcentagem de algodão na resistência da fibra. Para isto, foi realizado um experimento totalmente aleatorizado, no qual diversos níveis de porcentagem de algodão foram avaliados com respeito à resistência da fibra. Um ponto importante no planejamento do experimento é que para cada nível do fator (porcentagem de algodão), os outros fatores que influenciam o processo (como o meio ambiente, máquina, matéria prima, etc) devem apresentar um padrão homogêneo de variabilidade. No experimento, tomamos 5 níveis para a porcentagem de algodão e 5 replicações. \n",
        "\n",
        "> fonte: http://www.portalaction.com.br/anova/anova-um-fator#ex1\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rJ6Gr7Y5_Vn4"
      },
      "source": [
        "\n",
        "\n",
        "*   H0: Os dados seguem distribuição normal;\n",
        "*   H1: Os dados não seguem distribuição normal.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ft4g-J71oiUN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 857
        },
        "outputId": "8587748d-754c-4e5f-b2eb-c8bd3903f43f"
      },
      "source": [
        "## Dados\n",
        "\n",
        "pct_alg <-c(15,15,15,15,15,\n",
        "           20,20,20,20,20,\n",
        "           25,25,25,25,25,\n",
        "           30,30,30,30,30,\n",
        "           35,35,35,35,35)\n",
        "\n",
        "resitencia <-c(7,7,15,11,9,\n",
        "           12,17,12,18,18,\n",
        "           14,18,18,19,19,\n",
        "           19,25,22,19,23,\n",
        "           7,10,11,15,11)\n",
        "\n",
        "\n",
        "boxplot(resitencia~pct_alg)\n",
        "plot(resitencia,pct_alg)\n",
        "\n",
        "## Ao considerarmos um nível de significância de $ 5\\% $, não rejeitamos a hipótese de igualdade entre as médias dos níveis: $ (15,35); (20,25) $."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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tKe3wiC3e8sLNxbLzYnQuqAkHzgGtLp\nQx4PPnJk4cv+C4PkJkVIUYTkA9eQnt1tbPAjNfSM0eocuUn5EtLIQ8Yn4JCRae8nzJx/jrTs\n5qB1dh9Vc9oGsTl5ExL3J0JI5sqGLa++JzGZHQiJkDzjEtK6psIvTXBWhERInnEJSU0o/NIE\nZ+VJSJxsQMglpIZ5hV+a4KwIKYKQfMC/R3JASAi5h/Ry8XTdy88LzaeEkCIIyQeuITVPU48X\nnm5UU1vE5kRIHRCSD1xDuk6d8krhaWWD+q7YnAipA0LygWtIo05tX5goeZKWkCIIyQeuIfW5\nrn1hfq3IfEoIKYKQfOAa0vtntC9c+H6R+ZQQUgQh+cA1pGl9/7v41Hxbr/OkphQQUgeE5APX\nkNbuo4addOq4PdU+r8lNipCiCMkHzj9HWv/FgUqpvb/wN7EpBYTUASH5QODKhta/r94kNJsQ\nIUUQkg+4RMgBISHkGlLr/aeO/mCJ3KQIKYqQfOAa0rVK9a0vkZsUIUURkg9cQ9pvwhq5yexA\nSBGE5APn97V7Rm4uGiFFEJIPnP9EWiI3F42QIgjJB64hzbpQbi4aIUUQkg9cQ3p3wtmPrljV\nRm5ShBRFSD5wDYk3P4kdIfnANaTJU6aH5CZFSFGE5AOubHBASAgJhPTOyxuFJrMDIUUQkg+c\nQ3pirFKPBMGnfiM2pYCQOiAkHzjfjaKu/4RCSG8MrlsmNylCiiIkH7iGdMqwv64r/on0j2Gn\ny02KkKIIyQeuIQ2cF7SFFHxrgNicCKkDQvKBa0i9ftIe0p28i1BMCMkHztfafbU9pM8PF5sT\nIXVASD5wDen8AcuLITVdoSQvuiOkCELygWtI64b2GqNGj+6thq2XmxQhRRGSD5x/jvSPLxXf\nRWivL/1DbEoBIXVASD6QeBeh9ask/zQqIqQIQvKBa0hPvtW+8OwDIvMpIaQIQvKB8z+jeKh9\n4Tv8HCkmhOQDp5BWPfKImvNIm4VH9xWcFSFFEJIPnEKaF72J/WcEZ0VIEYTkA7dv7dY+rM6b\n12b+A82CsyKkCELygfNFq7yLUNwIyQcuIa1rKvzSBGdFSBGE5AOXkNQE3vwkAYTkA5eQGuYV\nfmmCsyKkCELyAW9+4oCQEHIPqaXwa+szz7cKTagNIUUQkg9cQ2q58DNB8OqBSo2T/NInpAhC\n8oFrSPPUl4NgYs2XLuwxT25ShBRFSD5wDenQM4PgbzXTg2DaaLlJEVIUIfnANaTdbwmCH6rf\nBMFNe8hNipCiCMkHriH1L4Q0ud+2IPhBP7lJEVIUIfnA+Vu7s4P1u59RWPjCIdWP+dziVyqv\nQUgRhOQD15C+pT6yr3oiCP6zbpb5E7+xuPh4ywCl1NgXKq1ISBGE5APXkLZM7VNf/HLaZ1RT\nFZ/YWHj4pep9xgXHqvrVFVYkpAhC8oHUlQ1L/lnNJxZDOrh+ReHxwZrPV1iRkCIIyQeJ3tal\nGNIb6oq25UlDKqxISBGE5INEb+tSDOl1dXfb8pWV3uKYkCIISc7W55/fGs+WE72tSzGklvrS\nJRDT9qywIiFFEJKU9ZN7KdVrsuhbMIYSva2Lmrx01YbZIzYXFv/U71MVViSkCEIS8sZBRz66\nceOjRx70RgwbT/S2Lu3/AvCBILinX4/nKqxISBGEJOSLhxX/Fx5sHvXFGDae6G1d7rxh7swp\nk45fFAQ3DflFpRUJKYKQZLTscW9pYcEe2+W3ntJtXd7deVfWjhu7wzD1TpdnVW5bHQ0fXu6V\ncWu7PAwh+WS9WlFaWKGk32I7SPG2Lm+u6vSBzdd9e4czHP9Eim6ro7Fjy71y3eYuD0NIPtmo\n2q+meV5V+/OaLkjtti6Nlf7Benzf2k2dKrgxQvLKQfNLz9ccFMPGU7utCyFVi5CE3DDgD8Wn\n3w+4IYaNp3ZbF0KqFiEJaflsvxk/+cnF/Sa3xLBx15AefrkLnxj9u/1gQqoSIUlpvefk/fY7\n+R7R9+kJuYa027e78Ik9evTeoWc6IS1dKrgxQkLINaTxn+zCOfnG/vpUXUrf2okiJIRcQ1o/\n+eQFy1a1MX9i8xFH7rhnBSFVi5B84HzHvi699/eKPl8JFwmpWoTkA9eQGs6bNr1dNZ/6dnjL\n2eCJSu+DF19Id9whuDFCQihv7/3NWTvEgpAcEBJChOSAkBAiJAeEhBAhOSAkhPIWElc2IBZ5\nC0kUISFESA4ICSFCckBICOUtJK5sQCzyFhJn7RALQnJASAgRkgNCQoiQHBASQoTkgJAQyltI\nXNmAWOQtJFH+hfTH++77o9zWoBGSA99C+sNYNWiQOvIPUtuDRkgOPAtp9Z6ffiUIXjlz4Bqh\nDULLW0h5vrLhrBPa3mK05YQGoQ1Cy1tIOT5r19zn56WFh/tUcwt6dAkhOfArpLVqZWlhper6\nraBgQEgO/ArpHfVsaeEZP/4G6hdCcuBXSMHhs0vPlx8utEFohOTAs5AW7Par4tOvei8Q2iC0\nvIWU6ysb5vSccNVVE3rOkdoetLyFJMq3kIJnZ3z84zOeE9scNEJy4F1IiA0hOSAkhPIWUp6v\nbECM8hZSns/aIUaE5ICQECIkB4SEECE5ICSECMkBISGUt5ByfWUD4pO3kEQREkKE5ICQECIk\nB4SEUN5C4soGxCJvIXHWDrEgJAeEhBAhOSAkhAjJASEhREgOCAmhvIXElQ2IRd5CEkVICBGS\nA0JCiJAcEBJCeQuJKxsQi7yFxFk7xIKQHBASQoTkgJAQIiQHhIQQITkgJITyFhJXNiAWeQtJ\nFCEhREgOCAkhQnJASAjlLSSubEAs8hYSZ+0QC0JyQEgIEZIDQkKIkBwQEkKE5ICQEMpbSFzZ\ngFjkLSRRhIQQITkgJIQIyQEhIZS3kLiyAbHIW0ictUMsCMkBISFESA4ICSFCckBICBGSA0JC\nKG8hcWUDYpG3kEQREkKE5ICQECIkB4SEUN5C4soGxCJvIXHWDrEgJAeEhBAhOSAkhAjJASEh\nREgORs1aVsaCu8u49dZyrywot61ZhOSBvIUkemXDCSoRJwhOGTHJW0hALAgJEEBIgIC8hSR6\nZQMQyltIomftgBAhAQIICRBASIAAQgIE5C0k0SsbgFDeQgJiQUiAAEICBOQtJK5sQCzyFhJn\n7RALQgIEEBIggJAAAYQECMhbSFzZgFjkLSQgFoQECCAkQEDeQuLKBsQibyFx1g6xICRAACEB\nAggJEEBIgIC8hcSVDd3Ia+cn4rVq5uJ1SEtuTcSSuHcXdu5M5rY6d1YzF69DOnyvAxOw1+Fx\n7y7sjEwmpJHVzMXrkLj1ZL6NSCakEdXMhZCMCCmrDp27JgFzD61mLoRkREhZlaXjT0hGhJRV\nWTr+hGRESFmVpeNPSEaElFVZOv6EZERIWZWl409IRoSUVVk6/oRkREhZlaXjT0hGhJRVWTr+\nhGRESFmVpeNPSEaElFVZOv6EZERIWZWl409IRoSUVVk6/oRkREhZlaXjT0hGhJRVWTr+hGRE\nSFmVpeNPSEaElFVZOv6EZERIWZWl409IRoSUVVk6/kmH1LrmsYULF71uWIuQUIUsHf9kQ2q6\nbFDp/SSGff29SusREqqQpeOfaEhrD1AHT507f/6Vk/dVhzdVWJGQUIUsHf9EQ5pee3/7UstN\nNTMrrEhIqEKWjn+iIQ2eppcbhlZYkZBQhSwd/0RDqv2mXv5aXYUVCQlVyNLxTzSk4Wfp5dP3\nr7AiIaEKWTr+iYY0s+baraWlTXNUY4UVCQlVyNLxTzSkjWNU/xOnXnzRlOP7qo9WSoWQUIUs\nHf9kf4607frRPYs/Rqr98G0tldYjJFQhS8c/8UuEtvxl+fJV23bxwqa5jTtMICSYjRrfuGtf\nPvbDZXzwg+VeOfbLZTY2Ppshhd5c1ekD6yeO32G02lLNNggp364aX8Zxe+1ZRr9+5V7Z67hy\nW7uqmrmkFlJjpa08rXb1Z9ZOCAlZQUhGhAQzQjIiJJglGtLYiMGEhG4k0ZB69Oi9Q09CQjeS\naEiN/fWpOr61Q3eSaEjNRxzZHC4TErqTZE82rOjzlXCRkNCdJHzW7u23wqUn5lVYjZDgmWy+\nixAhwTOEZERIMCMkI0KCGSEZERLMCMmIkGBGSEaEBDNCMiIkmBGSESHBjJCMCAlmhGRESDAj\nJCNCghkhGRESzAjJiJBgRkhGhAQzQjIiJJgRkhEhwYyQjAgJZoRkREgwIyQjQoIZIRkREsy8\nDungvgMS0PfguHcX/vM6pBEqESPi3l34j5AICQK8DunggQcmYCDf2sHI65A42YCsICQjQoIZ\nIRkREswIyYiQYEZIRoQEM0IyIiSYEZIRIcGMkIwICWaEZERIMCMkI0KCGSEZERLMCMmIkGBG\nSEaEBDNCMiIkmBGSESHBjJCMCAlmhGRESDAjJCNCghkhGRESzAjJiJBgRkhGhAQzQjIiJJgR\nkhEhwYyQjAgJZoRkREgwIyQjQoIZIRkREswIyYiQYEZIRoQEM0IyIiSYEZIRIcGMkIwICWaE\nZERIMCMkI0KCGSEZERLMCMmIkGBGSEaEBDNCMiIkmBGSESHBjJCMCAlmhGRESDAjJCNCghkh\nGRESzAjJiJBgRkhGhAQzQjIiJJgRkhEhwYyQjAgJZoRkREgwIyQjQoIZIRkREswIyYiQYEZI\nRoQEM0IyIiSYEZIRIcGMkIwICWaEZERI3cb2ZT/+8bLtsWyakIwIqbtYfqjaf3916PI4tk1I\nRoTUTaysP3tdEKw7u/7PMWyckIwIqZs48xOtxaftn/h0DBsnJCNC6h6ad/tlaeEXuzXLb93r\nkI7oO2DX6mss1JfZWN8j4t5dJGGtav+WbqVaK791r0Na+VgZv/7ONWVceWW5V77z63JbWxn3\n7iIJ76olp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            "text/plain": [
              "plot without title"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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iBASIAAIQEChAQIEBIgQEiAACEBAoQE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\nAIH/B0ljLxiRvlIDAAAAAElFTkSuQmCC",
            "text/plain": [
              "plot without title"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EO6Vx9VH2weQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "outputId": "e6b674ed-88f9-4b97-b765-6e527598ed4b"
      },
      "source": [
        "shapiro.test(resitencia)\n",
        "qqnorm(resitencia)\n",
        "qqline(resitencia)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\n",
              "\tShapiro-Wilk normality test\n",
              "\n",
              "data:  resitencia\n",
              "W = 0.94971, p-value = 0.247\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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yRwT0bHSjxPz6EXUkPacc2+sHsVl3lsEJJ6jS1yiHoENZIaErO/xkuYidcjeYUV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\ncICQADhASAAcICQADhASAAf/B7akwkGRHr03AAAAAElFTkSuQmCC",
            "text/plain": [
              "Plot with title “Normal Q-Q Plot”"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "twxfJ8EW_AOm"
      },
      "source": [
        "Comparando o W calculado com W alfa (0,927, pela tabela) temos que não é possível rejeitar H0 ao nível de significância α, pois Wcalculado > Wα (os valores críticos da estatística W de Shapiro-Wilk são dados na Tabela do link abaixo)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P_wYF5VY2EuT"
      },
      "source": [
        "tabela: http://www.portalaction.com.br/inferencia/64-teste-de-shapiro-wilk"
      ]
    }
  ]
}