Desafio_semana_5_Codenation_Clarisse.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/ClarisseAlvarenga/codenation_aceleradev_ds/blob/master/Desafio_semana_5_Codenation_Clarisse.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3YZB-OrbGxNK",
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      "source": [
        "# Desafio 4\n",
        "\n",
        "Neste desafio, vamos praticar um pouco sobre testes de hipóteses. Utilizaremos o _data set_ [2016 Olympics in Rio de Janeiro](https://www.kaggle.com/rio2016/olympic-games/), que contém dados sobre os atletas das Olimpíadas de 2016 no Rio de Janeiro.\n",
        "\n",
        "Esse _data set_ conta com informações gerais sobre 11538 atletas como nome, nacionalidade, altura, peso e esporte praticado. Estaremos especialmente interessados nas variáveis numéricas altura (`height`) e peso (`weight`). As análises feitas aqui são parte de uma Análise Exploratória de Dados (EDA).\n",
        "\n",
        "> Obs.: Por favor, não modifique o nome das funções de resposta."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "djYqBFFlGxNL",
        "colab_type": "text"
      },
      "source": [
        "## _Setup_ geral"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IDUL6AEgGxNM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import scipy.stats as sct\n",
        "import seaborn as sns"
      ],
      "execution_count": 135,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8AXid50YGxNP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#%matplotlib inline\n",
        "\n",
        "#from IPython.core.pylabtools import figsize\n",
        "\n",
        "\n",
        "#figsize(12, 8)\n",
        "\n",
        "#sns.set()"
      ],
      "execution_count": 136,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_W6NzwKYGxNS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "athletes = pd.read_csv(\"athletes.csv\")"
      ],
      "execution_count": 137,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y2C3iCHOGxNV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_sample(df, col_name, n=100, seed=42):\n",
        "    \"\"\"Get a sample from a column of a dataframe.\n",
        "    \n",
        "    It drops any numpy.nan entries before sampling. The sampling\n",
        "    is performed without replacement.\n",
        "    \n",
        "    Example of numpydoc for those who haven't seen yet.\n",
        "    \n",
        "    Parameters\n",
        "    ----------\n",
        "    df : pandas.DataFrame\n",
        "        Source dataframe.\n",
        "    col_name : str\n",
        "        Name of the column to be sampled.\n",
        "    n : int\n",
        "        Sample size. Default is 100.\n",
        "    seed : int\n",
        "        Random seed. Default is 42.\n",
        "    \n",
        "    Returns\n",
        "    -------\n",
        "    pandas.Series\n",
        "        Sample of size n from dataframe's column.\n",
        "    \"\"\"\n",
        "    np.random.seed(seed)\n",
        "    \n",
        "    random_idx = np.random.choice(df[col_name].dropna().index, size=n, replace=False)\n",
        "    \n",
        "    return df.loc[random_idx, col_name]"
      ],
      "execution_count": 138,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "itkyBOGAGxNY",
        "colab_type": "text"
      },
      "source": [
        "## Inicia sua análise a partir daqui"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7e_t49X6GxNY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "5fecec59-7827-4410-db74-88b1fee57238"
      },
      "source": [
        "# Sua análise começa aqui.\n",
        "athletes.head()"
      ],
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>name</th>\n",
              "      <th>nationality</th>\n",
              "      <th>sex</th>\n",
              "      <th>dob</th>\n",
              "      <th>height</th>\n",
              "      <th>weight</th>\n",
              "      <th>sport</th>\n",
              "      <th>gold</th>\n",
              "      <th>silver</th>\n",
              "      <th>bronze</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>736041664</td>\n",
              "      <td>A Jesus Garcia</td>\n",
              "      <td>ESP</td>\n",
              "      <td>male</td>\n",
              "      <td>10/17/69</td>\n",
              "      <td>1.72</td>\n",
              "      <td>64.0</td>\n",
              "      <td>athletics</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>532037425</td>\n",
              "      <td>A Lam Shin</td>\n",
              "      <td>KOR</td>\n",
              "      <td>female</td>\n",
              "      <td>9/23/86</td>\n",
              "      <td>1.68</td>\n",
              "      <td>56.0</td>\n",
              "      <td>fencing</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>435962603</td>\n",
              "      <td>Aaron Brown</td>\n",
              "      <td>CAN</td>\n",
              "      <td>male</td>\n",
              "      <td>5/27/92</td>\n",
              "      <td>1.98</td>\n",
              "      <td>79.0</td>\n",
              "      <td>athletics</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>521041435</td>\n",
              "      <td>Aaron Cook</td>\n",
              "      <td>MDA</td>\n",
              "      <td>male</td>\n",
              "      <td>1/2/91</td>\n",
              "      <td>1.83</td>\n",
              "      <td>80.0</td>\n",
              "      <td>taekwondo</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>33922579</td>\n",
              "      <td>Aaron Gate</td>\n",
              "      <td>NZL</td>\n",
              "      <td>male</td>\n",
              "      <td>11/26/90</td>\n",
              "      <td>1.81</td>\n",
              "      <td>71.0</td>\n",
              "      <td>cycling</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          id            name nationality  ... gold silver  bronze\n",
              "0  736041664  A Jesus Garcia         ESP  ...    0      0       0\n",
              "1  532037425      A Lam Shin         KOR  ...    0      0       0\n",
              "2  435962603     Aaron Brown         CAN  ...    0      0       1\n",
              "3  521041435      Aaron Cook         MDA  ...    0      0       0\n",
              "4   33922579      Aaron Gate         NZL  ...    0      0       0\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WdQTGNT_Hb8u",
        "colab_type": "code",
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          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "363cc3b7-c8e4-4087-85c0-c366cab5fe8c"
      },
      "source": [
        "athletes.shape"
      ],
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(11538, 11)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 140
        }
      ]
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    {
      "cell_type": "code",
      "metadata": {
        "id": "T-eX4seuHcJ1",
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          "height": 297
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        "outputId": "05df382e-a031-4b95-cd5b-16f79ad1464a"
      },
      "source": [
        "athletes.describe()[['height', 'weight', 'gold', 'silver', 'bronze']]"
      ],
      "execution_count": 141,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>height</th>\n",
              "      <th>weight</th>\n",
              "      <th>gold</th>\n",
              "      <th>silver</th>\n",
              "      <th>bronze</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>11208.000000</td>\n",
              "      <td>10879.000000</td>\n",
              "      <td>11538.000000</td>\n",
              "      <td>11538.000000</td>\n",
              "      <td>11538.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.766282</td>\n",
              "      <td>72.068205</td>\n",
              "      <td>0.057722</td>\n",
              "      <td>0.056769</td>\n",
              "      <td>0.061016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.112719</td>\n",
              "      <td>16.177334</td>\n",
              "      <td>0.255910</td>\n",
              "      <td>0.239147</td>\n",
              "      <td>0.243320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.210000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.690000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.760000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.840000</td>\n",
              "      <td>81.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2.210000</td>\n",
              "      <td>170.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             height        weight          gold        silver        bronze\n",
              "count  11208.000000  10879.000000  11538.000000  11538.000000  11538.000000\n",
              "mean       1.766282     72.068205      0.057722      0.056769      0.061016\n",
              "std        0.112719     16.177334      0.255910      0.239147      0.243320\n",
              "min        1.210000     31.000000      0.000000      0.000000      0.000000\n",
              "25%        1.690000     60.000000      0.000000      0.000000      0.000000\n",
              "50%        1.760000     70.000000      0.000000      0.000000      0.000000\n",
              "75%        1.840000     81.000000      0.000000      0.000000      0.000000\n",
              "max        2.210000    170.000000      5.000000      2.000000      2.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 141
        }
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6bGxPBm_GxNb",
        "colab_type": "text"
      },
      "source": [
        "## Questão 1\n",
        "\n",
        "Considerando uma amostra de tamanho 3000 da coluna `height` obtida com a função `get_sample()`, execute o teste de normalidade de Shapiro-Wilk com a função `scipy.stats.shapiro()`. Podemos afirmar que as alturas são normalmente distribuídas com base nesse teste (ao nível de significância de 5%)? Responda com um boolean (`True` ou `False`)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9QQ4AIqbItLD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "amostra_altura = get_sample(athletes, 'height', n=3000, seed=42)"
      ],
      "execution_count": 142,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pF3jGnJCItSt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "bbc5cc4e-fe2f-482e-9ba3-78ac03944766"
      },
      "source": [
        "amostra_altura.head()"
      ],
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1811     1.79\n",
              "7962     1.81\n",
              "10280    1.83\n",
              "317      1.68\n",
              "6989     2.01\n",
              "Name: height, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 143
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qRWnC8HJItbU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "1ab711c8-cc73-49f5-f9ac-81a6e2d8dceb"
      },
      "source": [
        "#aplicando o teste Shapiro na amostra \n",
        "estatistica, p_valor = sct.shapiro(amostra_altura)\n",
        "print(estatistica)\n",
        "print(p_valor)"
      ],
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9961519837379456\n",
            "5.681722541339695e-07\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EUKSbsTwJ-AC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#criando a significância\n",
        "significancia = 0.05"
      ],
      "execution_count": 145,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dzyxW9LPJvdz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9039af5b-2613-402b-d2a4-71efb6f12d3c"
      },
      "source": [
        "#O teste retorna a hipótese nula de que os dados derivam de uma distribuição normal. Caso o p_valor seja menor que a significância, devemos rejeitar a hipótese nula\n",
        "\n",
        "p_valor <= significancia"
      ],
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 146
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_3BJ62PRGxNc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#aqui usamos a fórmula para aceitar a hipótese nula, portanto usamos o contrário da fórmula de rejeição.\n",
        "def q1():\n",
        "  return bool(p_valor > significancia)"
      ],
      "execution_count": 198,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BjFv32nMb-bG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f961caf7-bced-4ff3-9b03-75dbb3f1877c"
      },
      "source": [
        "q1()"
      ],
      "execution_count": 199,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 199
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qyb3RNn9GxNf",
        "colab_type": "text"
      },
      "source": [
        "__Para refletir__:\n",
        "\n",
        "* Plote o histograma dessa variável (com, por exemplo, `bins=25`). A forma do gráfico e o resultado do teste são condizentes? Por que?\n",
        "* Plote o qq-plot para essa variável e a analise.\n",
        "* Existe algum nível de significância razoável que nos dê outro resultado no teste? (Não faça isso na prática. Isso é chamado _p-value hacking_, e não é legal)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8D28mibFLi63",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "9a35e82c-1625-437b-e334-3c3889500200"
      },
      "source": [
        "#vendo o histograma, apesar há uma diferença entre as caudas, e não há uma média clara.\n",
        "amostra_altura.plot(kind='hist', bins=25)"
      ],
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6c88825080>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 148
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2pJgBbe7L9qc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "6bc0a24e-eabb-48b1-80b4-6645485e9a95"
      },
      "source": [
        "#Visualizando o QQPlot percebemos que apesar de se assemelhar a uma normal, ele não segue a linha da distribuição\n",
        "sct.probplot(amostra_altura,plot=plt)\n",
        "plt.show()"
      ],
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "itG9wl6eMqaX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "2b988a0d-5da4-47e1-8514-aa5f68e1ee07"
      },
      "source": [
        "#visualizando o BoxPlot\n",
        "sns.boxplot(amostra_altura)"
      ],
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6c87307828>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 150
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j0q85FPEM0ae",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5c77ab07-7a92-4215-b87f-8fbf610e16b1"
      },
      "source": [
        "#verificando Média, Mediana e Moda para confirmar para que lado pende a curva, que graficamente mostra uma leve assimetria à esquerda\n",
        "amostra_altura.mean()"
      ],
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.766779999999997"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 151
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yzsb4XjZNY62",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2f971cc3-6c97-4f48-adcb-ee5254db80d0"
      },
      "source": [
        "amostra_altura.median()"
      ],
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.76"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 152
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pPepLlb5NbWl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "381ee678-23ca-4f01-c4e9-5ce6acaa7009"
      },
      "source": [
        "amostra_altura.mode()"
      ],
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1.8\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 153
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nsmqYWOtGxNg",
        "colab_type": "text"
      },
      "source": [
        "## Questão 2\n",
        "\n",
        "Repita o mesmo procedimento acima, mas agora utilizando o teste de normalidade de Jarque-Bera através da função `scipy.stats.jarque_bera()`. Agora podemos afirmar que as alturas são normalmente distribuídas (ao nível de significância de 5%)? Responda com um boolean (`True` ou `False`)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HLoqwa5aOxRl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "ae9ce285-8138-4098-fc78-5443ad3f1b0a"
      },
      "source": [
        "estatistica_JB, p_valor_JB = sct.jarque_bera(amostra_altura)\n",
        "print(estatistica_JB)\n",
        "print(p_valor_JB)"
      ],
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "13.03363513594265\n",
            "0.001478366424594868\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AB0_ReZsOxab",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b89792a9-7ed1-4240-f2d4-d113d6601c5d"
      },
      "source": [
        "#verificando a hipótese nula - The Jarque-Bera test tests whether the sample data has the skewness and kurtosis matching a normal distribution.\n",
        "#faz o teste considerando o Skew e a Kurtose (o terceiro e o quarto momentos), com o de uma distribuição normal\n",
        "#aqui usamos a fórmula para rejeitar a hipótese nula, portanto na questão vamos colocar o valor contrário\n",
        "p_valor_JB <= significancia"
      ],
      "execution_count": 155,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 155
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gC0QC5f7GxNg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def q2():\n",
        "    # Retorne aqui o resultado da questão 2.\n",
        "    return bool(p_valor_JB > significancia)"
      ],
      "execution_count": 200,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bXjNdDRWcIOM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3910450d-7c99-4c8e-a5aa-1de977a4837b"
      },
      "source": [
        "q2()"
      ],
      "execution_count": 201,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 201
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X_WpyrEHGxNj",
        "colab_type": "text"
      },
      "source": [
        "__Para refletir__:\n",
        "\n",
        "* Esse resultado faz sentido?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dD8veY7GGxNj",
        "colab_type": "text"
      },
      "source": [
        "## Questão 3\n",
        "\n",
        "Considerando agora uma amostra de tamanho 3000 da coluna `weight` obtida com a função `get_sample()`. Faça o teste de normalidade de D'Agostino-Pearson utilizando a função `scipy.stats.normaltest()`. Podemos afirmar que os pesos vêm de uma distribuição normal ao nível de significância de 5%? Responda com um boolean (`True` ou `False`)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2BdrHKXzOvf3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#criando a amostra de peso\n",
        "amostra_peso = get_sample(athletes, 'weight', n=3000, seed=42)"
      ],
      "execution_count": 157,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Rm9JOptQPEo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "aa5173ba-4aac-42d6-c169-85583db98db3"
      },
      "source": [
        "#visualizando graficamente\n",
        "sns.distplot(amostra_peso, bins=25)"
      ],
      "execution_count": 158,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6c872782e8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 158
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bDOceSRbQPIc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "51dda46d-54e9-422c-ac96-4bd20200dfb1"
      },
      "source": [
        "sct.probplot(amostra_peso, plot=plt)"
      ],
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((array([-3.50184707, -3.25811987, -3.12333914, ...,  3.12333914,\n",
              "          3.25811987,  3.50184707]),\n",
              "  array([ 37.,  39.,  39., ..., 159., 160., 160.])),\n",
              " (15.945260855959946, 72.12266666666666, 0.9733842945053901))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 159
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NiMnQSTBOv6U",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "ff6e5546-ba8f-47ec-d5fc-d840b8a4075e"
      },
      "source": [
        "sns.boxplot(amostra_peso)"
      ],
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6c871e6f60>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 160
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dswx0tHsQ3aT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "691344e6-d897-4a6c-a0f6-7400c8b5fc8c"
      },
      "source": [
        "#fazendo o teste de normalidade\n",
        "estatistica_pearson, p_valor_pearson = sct.normaltest(amostra_peso)\n",
        "print(estatistica_pearson)\n",
        "print(p_valor_pearson)"
      ],
      "execution_count": 161,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "510.24655809881176\n",
            "1.5898922918029537e-111\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "so97ACrPRL7O",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "bed6a8bc-b8c8-418a-dc55-ab6af51e9e65"
      },
      "source": [
        "#chamando a significancia\n",
        "significancia"
      ],
      "execution_count": 162,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.05"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 162
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ig6TYQ0JRW2v",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e32f3a88-a67e-45d6-c738-680c62231da1"
      },
      "source": [
        "#fazendo o teste da hipótese nula - This function tests the null hypothesis that a sample comes from a normal distribution\n",
        "#aqui usamos a fórmula para rejeitar a hipótese nula, portanto na questão vamos colocar o valor contrário\n",
        "p_valor_pearson <= significancia"
      ],
      "execution_count": 163,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 163
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AUXM95p2GxNk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def q3():\n",
        "    # Retorne aqui o resultado da questão 3.\n",
        "    return bool(p_valor_pearson > significancia)"
      ],
      "execution_count": 202,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fzk3wprOcQu6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d42f431e-e29c-47b9-a3cf-8659e505a18b"
      },
      "source": [
        "q3()"
      ],
      "execution_count": 203,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 203
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SAimOVX4GxNm",
        "colab_type": "text"
      },
      "source": [
        "__Para refletir__:\n",
        "\n",
        "* Plote o histograma dessa variável (com, por exemplo, `bins=25`). A forma do gráfico e o resultado do teste são condizentes? Por que?\n",
        "* Um _box plot_ também poderia ajudar a entender a resposta."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1UUJh-yOGxNn",
        "colab_type": "text"
      },
      "source": [
        "## Questão 4\n",
        "\n",
        "Realize uma transformação logarítmica em na amostra de `weight` da questão 3 e repita o mesmo procedimento. Podemos afirmar a normalidade da variável transformada ao nível de significância de 5%? Responda com um boolean (`True` ou `False`)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yf2egZZOSygp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "49458228-a6a1-4255-f940-49a5b2fe5eaf"
      },
      "source": [
        "#transformando o array usando o numpy\n",
        "amostra_peso_log = np.log(amostra_peso)\n",
        "print(amostra_peso.head())\n",
        "print(amostra_peso_log.head())"
      ],
      "execution_count": 165,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2162    80.0\n",
            "3450    70.0\n",
            "6886    69.0\n",
            "3871    66.0\n",
            "6383    82.0\n",
            "Name: weight, dtype: float64\n",
            "2162    4.382027\n",
            "3450    4.248495\n",
            "6886    4.234107\n",
            "3871    4.189655\n",
            "6383    4.406719\n",
            "Name: weight, dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vq3yltVmTbLU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "9b042810-ce46-4a78-8a00-22ebc7e7da9a"
      },
      "source": [
        "#visualizando graficamente\n",
        "sns.distplot(amostra_peso_log, bins=25)"
      ],
      "execution_count": 166,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6c8884ab00>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 166
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YlbnuM2nTboO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "16f4e36f-8edb-4708-e4b8-585c839c32c5"
      },
      "source": [
        "sns.boxplot(amostra_peso_log)"
      ],
      "execution_count": 167,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6c88bc2908>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 167
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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UXLvjlmUsUXk1ttwvYA/UwykItJIjkv5a0nfS27cr+dFGxyRdZ2Y9kmRmG81sS81jfyDpz8zs5ellOt+eYb3fKbkkJRCCAKOVHFFyrvaou/9SyY8IOuLuv1JyZPz59Gp2R1VzjteTa8D+o5IrYD0kaUbJTwtZzHFJ5yz5Qa18Ew6542poeNEwsw5Pfuhqm5ILcX/K3b8SPRewEI6A8WIylP7Mvx8rOW/81eB5gEVxBAwAQTgCBoAgBBgAghBgAAhCgAEgCAEGgCD/D15sGWMevb+mAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jZchjfDvUPqD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "94becde3-863b-4805-ff83-57469b3e61b5"
      },
      "source": [
        "sct.probplot(amostra_peso_log, plot=plt)\n",
        "plt.show()"
      ],
      "execution_count": 168,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yvXO3aU4UXvX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "c7716222-9d9a-4fc3-af28-2b5fabcda8c7"
      },
      "source": [
        "#aplicando o teste de normalidade\n",
        "stats_pearson_log, p_valor_pearson_log = sct.normaltest(amostra_peso_log)\n",
        "print(stats_pearson_log)\n",
        "print(p_valor_pearson_log)"
      ],
      "execution_count": 169,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "45.91534331977353\n",
            "1.0705572101256944e-10\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uoP03mFUUX8M",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9992a469-8110-451d-b90f-9ff7beb549c8"
      },
      "source": [
        "#fazendo o teste da hipótese nula - This function tests the null hypothesis that a sample comes from a normal distribution\n",
        "#aqui usamos a fórmula para rejeitar a hipótese nula, portanto na questão vamos colocar o valor contrário\n",
        "p_valor_pearson_log <= significancia"
      ],
      "execution_count": 170,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 170
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pBwnbp8QGxNn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def q4():\n",
        "    # Retorne aqui o resultado da questão 4.\n",
        "    return bool(p_valor_pearson_log > significancia)"
      ],
      "execution_count": 204,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tTAoUgIScWt2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ab9c4315-85b1-4b81-8017-6d002402ddca"
      },
      "source": [
        "q4()"
      ],
      "execution_count": 205,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 205
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KzNgFhWsGxNq",
        "colab_type": "text"
      },
      "source": [
        "__Para refletir__:\n",
        "\n",
        "* Plote o histograma dessa variável (com, por exemplo, `bins=25`). A forma do gráfico e o resultado do teste são condizentes? Por que?\n",
        "* Você esperava um resultado diferente agora?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pz7BFD0pGxNr",
        "colab_type": "text"
      },
      "source": [
        "> __Para as questão 5 6 e 7 a seguir considere todos testes efetuados ao nível de significância de 5%__."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rrJUuTtdGxNs",
        "colab_type": "text"
      },
      "source": [
        "## Questão 5\n",
        "\n",
        "Obtenha todos atletas brasileiros, norte-americanos e canadenses em `DataFrame`s chamados `bra`, `usa` e `can`,respectivamente. Realize um teste de hipóteses para comparação das médias das alturas (`height`) para amostras independentes e variâncias diferentes com a função `scipy.stats.ttest_ind()` entre `bra` e `usa`. Podemos afirmar que as médias são estatisticamente iguais? Responda com um boolean (`True` ou `False`)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DGf2Bg_7PEgt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "02e7cca7-0e6b-4cb2-ec4a-97498397e796"
      },
      "source": [
        "#criando os DFs\n",
        "bra, usa, can = athletes.query('nationality ==\"BRA\"'), athletes.query('nationality ==\"USA\"'), athletes.query('nationality ==\"CAN\"')\n",
        "bra.head()"
      ],
      "execution_count": 172,
      "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>id</th>\n",
              "      <th>name</th>\n",
              "      <th>nationality</th>\n",
              "      <th>sex</th>\n",
              "      <th>dob</th>\n",
              "      <th>height</th>\n",
              "      <th>weight</th>\n",
              "      <th>sport</th>\n",
              "      <th>gold</th>\n",
              "      <th>silver</th>\n",
              "      <th>bronze</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>236523132</td>\n",
              "      <td>Adam Imer</td>\n",
              "      <td>BRA</td>\n",
              "      <td>male</td>\n",
              "      <td>8/18/89</td>\n",
              "      <td>1.82</td>\n",
              "      <td>88.0</td>\n",
              "      <td>hockey</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>106</th>\n",
              "      <td>993710181</td>\n",
              "      <td>Adenizia da Silva</td>\n",
              "      <td>BRA</td>\n",
              "      <td>female</td>\n",
              "      <td>12/18/86</td>\n",
              "      <td>1.87</td>\n",
              "      <td>65.0</td>\n",
              "      <td>volleyball</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109</th>\n",
              "      <td>930618704</td>\n",
              "      <td>Adilson da Silva</td>\n",
              "      <td>BRA</td>\n",
              "      <td>male</td>\n",
              "      <td>1/24/72</td>\n",
              "      <td>1.70</td>\n",
              "      <td>79.0</td>\n",
              "      <td>golf</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>114</th>\n",
              "      <td>787208883</td>\n",
              "      <td>Adrian Baches</td>\n",
              "      <td>BRA</td>\n",
              "      <td>male</td>\n",
              "      <td>4/7/90</td>\n",
              "      <td>1.84</td>\n",
              "      <td>83.0</td>\n",
              "      <td>aquatics</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128</th>\n",
              "      <td>80367651</td>\n",
              "      <td>Adriana Aparecida da Silva</td>\n",
              "      <td>BRA</td>\n",
              "      <td>female</td>\n",
              "      <td>7/22/81</td>\n",
              "      <td>1.66</td>\n",
              "      <td>52.0</td>\n",
              "      <td>athletics</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            id                        name nationality  ... gold silver  bronze\n",
              "79   236523132                   Adam Imer         BRA  ...    0      0       0\n",
              "106  993710181           Adenizia da Silva         BRA  ...    0      0       0\n",
              "109  930618704            Adilson da Silva         BRA  ...    0      0       0\n",
              "114  787208883               Adrian Baches         BRA  ...    0      0       0\n",
              "128   80367651  Adriana Aparecida da Silva         BRA  ...    0      0       0\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 172
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xmrdo3koPElP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "fb9c2266-05f2-473c-e99f-61a8431b4634"
      },
      "source": [
        "#visualizando usa\n",
        "usa.head()"
      ],
      "execution_count": 173,
      "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>id</th>\n",
              "      <th>name</th>\n",
              "      <th>nationality</th>\n",
              "      <th>sex</th>\n",
              "      <th>dob</th>\n",
              "      <th>height</th>\n",
              "      <th>weight</th>\n",
              "      <th>sport</th>\n",
              "      <th>gold</th>\n",
              "      <th>silver</th>\n",
              "      <th>bronze</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>266237702</td>\n",
              "      <td>Aaron Russell</td>\n",
              "      <td>USA</td>\n",
              "      <td>male</td>\n",
              "      <td>6/4/93</td>\n",
              "      <td>2.05</td>\n",
              "      <td>98.0</td>\n",
              "      <td>volleyball</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>376068084</td>\n",
              "      <td>Abbey D'Agostino</td>\n",
              "      <td>USA</td>\n",
              "      <td>female</td>\n",
              "      <td>5/25/92</td>\n",
              "      <td>1.61</td>\n",
              "      <td>49.0</td>\n",
              "      <td>athletics</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>162792594</td>\n",
              "      <td>Abbey Weitzeil</td>\n",
              "      <td>USA</td>\n",
              "      <td>female</td>\n",
              "      <td>12/3/96</td>\n",
              "      <td>1.78</td>\n",
              "      <td>68.0</td>\n",
              "      <td>aquatics</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>884912136</td>\n",
              "      <td>Abigail Johnston</td>\n",
              "      <td>USA</td>\n",
              "      <td>female</td>\n",
              "      <td>11/16/89</td>\n",
              "      <td>1.66</td>\n",
              "      <td>61.0</td>\n",
              "      <td>aquatics</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>259002430</td>\n",
              "      <td>Adeline Maria Gray</td>\n",
              "      <td>USA</td>\n",
              "      <td>female</td>\n",
              "      <td>1/15/91</td>\n",
              "      <td>1.73</td>\n",
              "      <td>77.0</td>\n",
              "      <td>wrestling</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            id                name nationality  ... gold silver  bronze\n",
              "6    266237702       Aaron Russell         USA  ...    0      0       1\n",
              "13   376068084    Abbey D'Agostino         USA  ...    0      0       0\n",
              "14   162792594      Abbey Weitzeil         USA  ...    1      1       0\n",
              "64   884912136    Abigail Johnston         USA  ...    0      0       0\n",
              "104  259002430  Adeline Maria Gray         USA  ...    0      0       0\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 173
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l_ERFFgnPEpf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "cb4e1def-45c1-4b39-ddec-84e6d95ee363"
      },
      "source": [
        "#visualizando Canadá\n",
        "can.head()"
      ],
      "execution_count": 174,
      "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>id</th>\n",
              "      <th>name</th>\n",
              "      <th>nationality</th>\n",
              "      <th>sex</th>\n",
              "      <th>dob</th>\n",
              "      <th>height</th>\n",
              "      <th>weight</th>\n",
              "      <th>sport</th>\n",
              "      <th>gold</th>\n",
              "      <th>silver</th>\n",
              "      <th>bronze</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>435962603</td>\n",
              "      <td>Aaron Brown</td>\n",
              "      <td>CAN</td>\n",
              "      <td>male</td>\n",
              "      <td>5/27/92</td>\n",
              "      <td>1.98</td>\n",
              "      <td>79.0</td>\n",
              "      <td>athletics</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>136977152</td>\n",
              "      <td>Adam Froese</td>\n",
              "      <td>CAN</td>\n",
              "      <td>male</td>\n",
              "      <td>8/13/91</td>\n",
              "      <td>1.82</td>\n",
              "      <td>81.0</td>\n",
              "      <td>hockey</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>983411298</td>\n",
              "      <td>Adam van Koeverden</td>\n",
              "      <td>CAN</td>\n",
              "      <td>male</td>\n",
              "      <td>1/29/82</td>\n",
              "      <td>1.82</td>\n",
              "      <td>85.0</td>\n",
              "      <td>canoe</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>238</th>\n",
              "      <td>769580282</td>\n",
              "      <td>Akeem Haynes</td>\n",
              "      <td>CAN</td>\n",
              "      <td>male</td>\n",
              "      <td>3/11/92</td>\n",
              "      <td>1.68</td>\n",
              "      <td>71.0</td>\n",
              "      <td>athletics</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>332</th>\n",
              "      <td>84230211</td>\n",
              "      <td>Alena Sharp</td>\n",
              "      <td>CAN</td>\n",
              "      <td>female</td>\n",
              "      <td>3/7/81</td>\n",
              "      <td>1.68</td>\n",
              "      <td>69.0</td>\n",
              "      <td>golf</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            id                name nationality  ... gold silver  bronze\n",
              "2    435962603         Aaron Brown         CAN  ...    0      0       1\n",
              "77   136977152         Adam Froese         CAN  ...    0      0       0\n",
              "92   983411298  Adam van Koeverden         CAN  ...    0      0       0\n",
              "238  769580282        Akeem Haynes         CAN  ...    0      0       1\n",
              "332   84230211         Alena Sharp         CAN  ...    0      0       0\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 174
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zH0RswLsPEtL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "c45b1853-1ac9-4bd7-eac1-c3c1b0b968d8"
      },
      "source": [
        "#verificando o tamanho das amostras\n",
        "print(f'A amostra de atletas brasileiros é: {bra.shape[0]}')\n",
        "print(f'A amostra de atletas estadunidenses é: {usa.shape[0]}')\n",
        "print(f'A amostra de atletas canadenses é: {can.shape[0]}')"
      ],
      "execution_count": 175,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "A amostra de atletas brasileiros é: 485\n",
            "A amostra de atletas estadunidenses é: 567\n",
            "A amostra de atletas canadenses é: 321\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JE9PCs20PExM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "24850b1c-00db-481c-90a0-ef8650538bc2"
      },
      "source": [
        "bra.height.mean()"
      ],
      "execution_count": 176,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.764033613445379"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 176
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qEAaawEUPE1A",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "046971ff-4ad7-4070-959b-fec9ec940f3b"
      },
      "source": [
        "usa.height.mean()"
      ],
      "execution_count": 177,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.7877678571428557"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 177
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iP1DMhb7PE7E",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "0ba1a3a7-5301-45e1-f7cc-b9e8f3e0ac14"
      },
      "source": [
        "amostra_bra = np.array(get_sample(bra, 'height'))\n",
        "amostra_bra"
      ],
      "execution_count": 178,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1.86, 1.85, 1.83, 1.59, 1.88, 1.7 , 1.53, 1.7 , 1.67, 1.92, 1.8 ,\n",
              "       1.58, 2.09, 1.76, 1.59, 1.96, 1.63, 1.63, 1.85, 1.94, 1.83, 1.65,\n",
              "       1.8 , 1.56, 1.82, 1.67, 1.73, 1.91, 1.88, 1.83, 1.71, 1.76, 1.96,\n",
              "       1.81, 1.73, 1.7 , 1.74, 1.8 , 1.33, 1.66, 1.68, 1.54, 1.67, 1.93,\n",
              "       1.88, 1.75, 1.71, 2.08, 1.82, 1.77, 1.72, 1.93, 1.69, 1.94, 1.79,\n",
              "       1.69, 1.72, 1.65, 1.6 , 1.9 , 1.85, 1.8 , 1.81, 1.75, 1.56, 1.73,\n",
              "       1.82, 1.65, 1.73, 1.86, 1.63, 1.78, 1.85, 1.99, 1.61, 1.62, 1.62,\n",
              "       1.76, 1.81, 1.76, 1.74, 1.88, 1.63, 1.64, 1.73, 1.75, 1.68, 1.61,\n",
              "       1.58, 1.75, 1.85, 1.64, 1.83, 1.91, 1.68, 1.82, 1.7 , 1.58, 1.9 ,\n",
              "       1.6 ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 178
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TACOvM9FTbUb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "77f1871e-3128-4a5c-94d6-5305d8993b85"
      },
      "source": [
        "#conferindo a média\n",
        "amostra_bra.mean()"
      ],
      "execution_count": 179,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.7519000000000002"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 179
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q1Uv6qx-ThGj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "7388fd5d-ddd5-4aeb-bc7f-b4dcf5262c7d"
      },
      "source": [
        "#conferindo a variancia\n",
        "amostra_bra.var()"
      ],
      "execution_count": 180,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.01618339"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 180
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hDIQrVIMPE-w",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "bdaaabba-330c-4bf5-fb2a-131dcce491ae"
      },
      "source": [
        "amostra_usa = np.array(get_sample(usa, 'height'))\n",
        "amostra_usa"
      ],
      "execution_count": 181,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1.8 , 1.68, 1.63, 1.91, 1.81, 1.73, 1.88, 1.83, 1.76, 1.75, 1.83,\n",
              "       1.81, 1.73, 1.73, 1.7 , 1.86, 1.71, 1.82, 1.83, 1.83, 2.06, 1.96,\n",
              "       1.76, 1.45, 1.73, 1.68, 1.73, 1.73, 1.89, 1.86, 1.86, 1.73, 1.83,\n",
              "       1.73, 1.87, 2.01, 1.91, 1.73, 1.91, 1.76, 1.81, 1.88, 1.66, 1.73,\n",
              "       1.78, 1.73, 1.83, 1.81, 1.99, 1.91, 1.83, 1.78, 1.61, 1.73, 1.68,\n",
              "       1.78, 1.86, 1.71, 1.63, 1.96, 1.83, 1.73, 1.81, 1.93, 1.78, 1.73,\n",
              "       1.65, 2.03, 1.75, 1.81, 1.78, 1.83, 1.71, 1.78, 1.66, 1.76, 1.63,\n",
              "       1.68, 1.73, 1.91, 1.88, 1.81, 1.7 , 1.91, 1.55, 1.94, 1.88, 1.66,\n",
              "       1.94, 1.76, 1.83, 1.73, 1.78, 1.88, 1.83, 1.71, 1.73, 1.76, 1.94,\n",
              "       1.88])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 181
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mrsZql-HTeg_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "fc13e945-00db-4d4e-bc12-a3acae381e08"
      },
      "source": [
        "#conferindo a média\n",
        "amostra_usa.mean()"
      ],
      "execution_count": 182,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.7913999999999999"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 182
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BAtAgL6XTj22",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "656387a9-da06-4dd1-b5f7-daae0ad685c8"
      },
      "source": [
        "#conferindo a variância\n",
        "amostra_usa.var()"
      ],
      "execution_count": 183,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.010694039999999998"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 183
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8h313NGSPFCe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "fa62dac6-951e-48d9-c88e-faa90abcb4b4"
      },
      "source": [
        "#este teste testa a seguinte hipótese nula: This is a two-sided test for the null hypothesis that 2 independent samples have identical average (expected) values. \n",
        "#This test assumes that the populations have identical variances by default. - Como ele presume que a variância é idêntica, vamos passar o equal_var False para mudar o tipo de teste\n",
        "stats_bra_usa, p_valor_bra_usa = sct.ttest_ind(amostra_bra, amostra_usa, equal_var=False)\n",
        "print(stats_bra_usa)\n",
        "print(p_valor_bra_usa)"
      ],
      "execution_count": 184,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "-2.397291376770201\n",
            "0.01748528165217272\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9cpaNlKCPE4D",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "fce4eb35-13a6-495f-99a3-03c784d78081"
      },
      "source": [
        "#conferindo o teste de rejeição da hipótese nula\n",
        "#como a pergunta pede para confirmar a hipótese nula, vamos usar o contrário na resposta\n",
        "p_valor_bra_usa <= significancia"
      ],
      "execution_count": 185,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 185
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_YEagkhDGxNs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def q5():\n",
        "    # Retorne aqui o resultado da questão 5.\n",
        "    return bool(p_valor_bra_usa > significancia)"
      ],
      "execution_count": 206,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "igbWh7Iacqc1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8a5953d8-c97c-4038-d2f2-1f4a23dfdac3"
      },
      "source": [
        "q5()"
      ],
      "execution_count": 218,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 218
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vmeZVqm-GxNv",
        "colab_type": "text"
      },
      "source": [
        "## Questão 6\n",
        "\n",
        "Repita o procedimento da questão 5, mas agora entre as alturas de `bra` e `can`. Podemos afimar agora que as médias são estatisticamente iguais? Reponda com um boolean (`True` ou `False`)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wafwLsNhTKRS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "4c913b9c-ec3a-4cd6-ed1c-26abe3c034bb"
      },
      "source": [
        "#vamos criar uma amostra para o Canadá. Para este teste vou aproveitar a amostra criada para o Brasil\n",
        "amostra_can = np.array(get_sample(can, 'height'))\n",
        "amostra_can\n"
      ],
      "execution_count": 207,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1.66, 1.75, 1.74, 2.05, 1.55, 1.76, 1.67, 1.58, 1.74, 1.8 , 1.72,\n",
              "       1.96, 1.6 , 1.9 , 1.73, 1.71, 1.66, 1.6 , 1.8 , 1.8 , 1.7 , 1.85,\n",
              "       1.57, 1.94, 1.65, 1.8 , 1.93, 1.8 , 1.75, 1.83, 1.63, 1.65, 1.69,\n",
              "       1.72, 1.68, 1.65, 1.85, 1.96, 1.73, 1.72, 1.6 , 1.65, 1.71, 1.95,\n",
              "       1.63, 1.68, 1.65, 1.78, 1.72, 1.79, 1.82, 1.7 , 1.87, 1.65, 1.75,\n",
              "       1.55, 1.73, 1.88, 1.75, 1.78, 1.85, 1.9 , 2.03, 1.9 , 1.72, 1.95,\n",
              "       1.7 , 1.72, 1.72, 1.83, 1.74, 1.7 , 1.68, 1.72, 1.8 , 2.  , 1.8 ,\n",
              "       1.85, 1.65, 1.85, 1.75, 1.65, 1.62, 2.05, 1.69, 1.83, 1.6 , 1.9 ,\n",
              "       1.95, 1.88, 1.62, 1.73, 1.74, 1.57, 1.64, 1.64, 1.78, 1.55, 2.01,\n",
              "       1.67])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 207
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8CKapkcJTKWu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b3c114b4-eaea-4637-91c0-142630efed8d"
      },
      "source": [
        "#conferindo a média\n",
        "amostra_can.mean()"
      ],
      "execution_count": 208,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.755"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 208
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kvGicChVTKcv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f38db7e1-4c85-48e1-8358-59fffd2cd447"
      },
      "source": [
        "#conferindo a variância\n",
        "amostra_can.var()"
      ],
      "execution_count": 209,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.014406999999999996"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 209
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2sfyQHjgUHeV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "a2705369-ef30-4629-945e-e6340e0f6594"
      },
      "source": [
        "#aplicando o teste\n",
        "#este teste testa a seguinte hipótese nula: This is a two-sided test for the null hypothesis that 2 independent samples have identical average (expected) values. \n",
        "#This test assumes that the populations have identical variances by default. - Como ele presume que a variância é idêntica, vamos passar o equal_var False para mudar o tipo de teste\n",
        "stats_bra_can, p_valor_bra_can = sct.ttest_ind(amostra_bra, amostra_can, equal_var=False)\n",
        "print(stats_bra_can)\n",
        "print(p_valor_bra_can)"
      ],
      "execution_count": 210,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "-0.1763545967450964\n",
            "0.8601962262079699\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_7kOJp9HUfng",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3ee2fffc-66d5-418e-e3cf-2e8b12998e00"
      },
      "source": [
        "#recuperando a significância:\n",
        "significancia"
      ],
      "execution_count": 211,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.05"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 211
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z1U0dhO8UUfs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "97577f62-1961-490f-ede2-28b855e49e77"
      },
      "source": [
        "#Como nas questões anteriores vamos colocar o teste de rejeição aqui e o teste contrário na resposta\n",
        "p_valor_bra_can <= significancia"
      ],
      "execution_count": 212,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 212
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9ehdUQ7DGxNw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def q6():\n",
        "    # Retorne aqui o resultado da questão 6.\n",
        "    return bool(p_valor_bra_can > significancia)\n"
      ],
      "execution_count": 221,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s9r_b7hXcvO5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "acc5c53b-cbab-439f-d0fc-b6eb94c410fb"
      },
      "source": [
        "q6()"
      ],
      "execution_count": 222,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 222
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZKbYI9-6GxNy",
        "colab_type": "text"
      },
      "source": [
        "## Questão 7\n",
        "\n",
        "Repita o procedimento da questão 6, mas agora entre as alturas de `usa` e `can`. Qual o valor do p-valor retornado? Responda como um único escalar arredondado para oito casas decimais."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vpHsSRrfUuhG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "_, p_valor_usa_can = sct.ttest_ind(usa.height, can.height, equal_var=False, nan_policy='omit')\n"
      ],
      "execution_count": 263,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "89GFheAzU9tl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "330b8b81-e17a-4735-8551-43f9a01886d9"
      },
      "source": [
        "p_valor_usa_can.round(8)"
      ],
      "execution_count": 264,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.00046601"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 264
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JQcjIibEGxNz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def q7():\n",
        "  # Retorne aqui o resultado da questão 7.\n",
        "    return float(p_valor_usa_can.round(8))\n"
      ],
      "execution_count": 266,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "trnlCujfktbw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a9cabfb5-dd4d-4e25-86d8-c0d282195c98"
      },
      "source": [
        "q7()"
      ],
      "execution_count": 267,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.00046601"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 267
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4quEOTOmGxN1",
        "colab_type": "text"
      },
      "source": [
        "__Para refletir__:\n",
        "\n",
        "* O resultado faz sentido?\n",
        "* Você consegue interpretar esse p-valor?\n",
        "* Você consegue chegar a esse valor de p-valor a partir da variável de estatística?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7t3YEuROccpA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 216,
      "outputs": []
    }
  ]
}