{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Aula 4 - Amostragem e Correlações"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l01TFfxQBxjd"
      },
      "source": [
        "## **Amostragem**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "4QPMcQbnPdkc"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "frRZfscdPwic"
      },
      "outputs": [],
      "source": [
        "world_happiness = pd.read_csv('data/world_happiness.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "afCZzUPqP8fr",
        "outputId": "280a1e2a-441c-4d36-e0fa-c5f268570dfb"
      },
      "outputs": [
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              "      <td>Netherlands</td>\n",
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            "text/plain": [
              "   Unnamed: 0      country  social_support  freedom  corruption  generosity  \\\n",
              "0           1      Finland             2.0      5.0         4.0        47.0   \n",
              "1           2      Denmark             4.0      6.0         3.0        22.0   \n",
              "2           3       Norway             3.0      3.0         8.0        11.0   \n",
              "3           4      Iceland             1.0      7.0        45.0         3.0   \n",
              "4           5  Netherlands            15.0     19.0        12.0         7.0   \n",
              "\n",
              "   gdp_per_cap  life_exp  happiness_score  \n",
              "0        42400      81.8              155  \n",
              "1        48300      81.0              154  \n",
              "2        66300      82.6              153  \n",
              "3        47900      83.0              152  \n",
              "4        50500      81.8              151  "
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "world_happiness.head()"
      ]
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        },
        "id": "kw0azfT-QMuo",
        "outputId": "d9efbfdb-4bb1-4858-c71e-bc331d0d9398"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 143 entries, 0 to 142\n",
            "Data columns (total 9 columns):\n",
            " #   Column           Non-Null Count  Dtype  \n",
            "---  ------           --------------  -----  \n",
            " 0   Unnamed: 0       143 non-null    int64  \n",
            " 1   country          143 non-null    object \n",
            " 2   social_support   142 non-null    float64\n",
            " 3   freedom          142 non-null    float64\n",
            " 4   corruption       135 non-null    float64\n",
            " 5   generosity       142 non-null    float64\n",
            " 6   gdp_per_cap      143 non-null    int64  \n",
            " 7   life_exp         143 non-null    float64\n",
            " 8   happiness_score  143 non-null    int64  \n",
            "dtypes: float64(5), int64(3), object(1)\n",
            "memory usage: 10.2+ KB\n"
          ]
        }
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      "source": [
        "world_happiness.info()"
      ]
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QPmkIME4QwJ7"
      },
      "outputs": [],
      "source": [
        "np.random.seed(3)"
      ]
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              "      <th>11</th>\n",
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              "      <th>79</th>\n",
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              "      <td>46.0</td>\n",
              "      <td>128.0</td>\n",
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              "      <td>71.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>95.0</td>\n",
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              "          const docLink = document.createElement('div');\n",
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              "  "
            ],
            "text/plain": [
              "     Unnamed: 0       country  social_support  freedom  corruption  \\\n",
              "25           26     Guatemala            78.0     25.0        82.0   \n",
              "115         116  Sierra Leone           135.0    116.0       112.0   \n",
              "26           27  Saudi Arabia            62.0     68.0         NaN   \n",
              "76           77      Mongolia            10.0    112.0       119.0   \n",
              "11           12    Costa Rica            42.0     16.0        58.0   \n",
              "30           31        Brazil            43.0     84.0        71.0   \n",
              "79           80       Algeria           101.0    149.0        46.0   \n",
              "131         132       Burundi           152.0    135.0        23.0   \n",
              "70           71       Croatia            79.0    118.0       139.0   \n",
              "46           47       Ecuador            71.0     42.0        68.0   \n",
              "\n",
              "     generosity  gdp_per_cap  life_exp  happiness_score  \n",
              "25         78.0         7610      73.1              129  \n",
              "115        79.0         1460      61.3               27  \n",
              "26         82.0        48100      77.1              128  \n",
              "76         38.0        12800      69.3               73  \n",
              "11         75.0        15800      79.8              144  \n",
              "30        108.0        14300      75.9              124  \n",
              "79        128.0        14000      78.1               68  \n",
              "131       149.0          644      62.3               11  \n",
              "70         81.0        24500      78.8               81  \n",
              "46         95.0        10200      77.2              106  "
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "world_happiness.sample(10, replace=True)"
      ]
    },
    {
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              "     Unnamed: 0     country  social_support  freedom  corruption  generosity  \\\n",
              "130         131     Lesotho            98.0     97.0        59.0       151.0   \n",
              "76           77    Mongolia            10.0    112.0       119.0        38.0   \n",
              "125         126        Togo           149.0    120.0        72.0       131.0   \n",
              "38           39  Uzbekistan            11.0      1.0        18.0        29.0   \n",
              "16           17     Germany            39.0     44.0        17.0        19.0   \n",
              "67           68       Libya            73.0     79.0        31.0        87.0   \n",
              "31           32     Uruguay            35.0     30.0        33.0        80.0   \n",
              "111         112     Tunisia           121.0    143.0       101.0       144.0   \n",
              "61           62    Portugal            47.0     37.0       135.0       122.0   \n",
              "1             2     Denmark             4.0      6.0         3.0        22.0   \n",
              "\n",
              "     gdp_per_cap  life_exp  happiness_score  \n",
              "130         2920      56.1               12  \n",
              "76         12800      69.3               73  \n",
              "125         1610      65.0               17  \n",
              "38          6490      70.7              115  \n",
              "16         46200      80.9              139  \n",
              "67         14800      73.3               84  \n",
              "31         20900      77.3              123  \n",
              "111        11100      78.7               32  \n",
              "61         29300      81.8               90  \n",
              "1          48300      81.0              154  "
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          "execution_count": 15,
          "metadata": {},
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        "world_happiness.sample(10)"
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dsakq9Z_dTUq"
      },
      "source": [
        "## **Correlação e Design de Experimentos**\n",
        "Neste capítulo vamos aprender como quantificar a força de uma relação linear entre duas variáveis e explorar como variáveis confusas podem afetar a relação entre elas. Também veremos como o plano de um estudo pode influenciar seus resultados, modificar o modo como os dados devem ser analisados, e potencialmente afetar a confiança de suas conclusões."
      ]
    },
    {
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          "height": 281
        },
        "id": "IbJjTcxeS_uW",
        "outputId": "94f476ed-de44-43dc-e510-7340d154c6b3"
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      "outputs": [
        {
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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.scatterplot(x='life_exp', y='happiness_score', data=world_happiness)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "id": "VGkF3p9_TWMF",
        "outputId": "8c829ed2-863d-4eed-975a-cf2dc53e738f"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.lmplot(x='life_exp', y='happiness_score', data=world_happiness)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "07ip6HkYUEgl",
        "outputId": "7ca903e4-a0a9-483c-9384-d2d451f19451"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.7802249053272062\n"
          ]
        }
      ],
      "source": [
        "cor = world_happiness['life_exp'].corr(world_happiness['happiness_score'])\n",
        "print(cor)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "87yemObSiYvX"
      },
      "source": [
        "#### Correlação de Pearson, Kendall e Spearman\n",
        "Qual método utilizar?\n",
        "\n",
        "**Pearson**\n",
        "- Paramétrico\n",
        "- Valores numéricos contínuos (flutuantes);\n",
        "- Distribuição normal (histograma simétrico);\n",
        "- Relação linear (formando uma reta onde os pontos se acumulam);\n",
        "- Ausência de outliers (estes devem ser tratados previamente, pois afetam diretamente o coeficiente de correlação);\n",
        "\n",
        "**Kendall e Spearman**\n",
        "- Não paramétrico\n",
        "- Valores numéricos contínuos (flutuantes) ou discretos (integrais);\n",
        "- Distribuição não-normal, presença de outliers;\n",
        "- Não requer consistência de intervalos;\n",
        "- O primeiro é mais robusto que o segundo;"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "1IQEV6T8UkFI",
        "outputId": "2b2d7f54-7b6f-4d99-d74c-d1e6827df126"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-40262376-3c9e-43e5-a693-b6a50d1df4d9\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>social_support</th>\n",
              "      <th>freedom</th>\n",
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            "text/plain": [
              "                 Unnamed: 0  social_support   freedom  corruption  generosity  \\\n",
              "Unnamed: 0         1.000000        0.823994  0.570802    0.221393    0.501447   \n",
              "social_support     0.823994        1.000000  0.470893    0.154848    0.449225   \n",
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              "corruption         0.221393        0.154848  0.387234    1.000000    0.305043   \n",
              "generosity         0.501447        0.449225  0.493207    0.305043    1.000000   \n",
              "gdp_per_cap       -0.729362       -0.687268 -0.444420   -0.335716   -0.531636   \n",
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              "\n",
              "                 gdp_per_cap  life_exp  happiness_score  \n",
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              "gdp_per_cap         1.000000  0.701955         0.727973  \n",
              "life_exp            0.701955  1.000000         0.780225  \n",
              "happiness_score     0.727973  0.780225         1.000000  "
            ]
          },
          "execution_count": 5,
          "metadata": {},
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      ],
      "source": [
        "cor = world_happiness.corr(method='pearson')\n",
        "cor"
      ]
    },
    {
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        "id": "B85WFoS3VfgW",
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              "                 Unnamed: 0  social_support   freedom  corruption  generosity  \\\n",
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        "cor = world_happiness.corr(method='kendall')\n",
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        },
        "id": "Sci014XAWML4",
        "outputId": "75b19ae6-d35c-47d9-af48-2da6a271438c"
      },
      "outputs": [
        {
          "data": {
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              "      <th>happiness_score</th>\n",
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              "      <td>-0.571958</td>\n",
              "      <td>-0.211389</td>\n",
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            "text/plain": [
              "                 Unnamed: 0  social_support   freedom  corruption  generosity  \\\n",
              "Unnamed: 0         1.000000        0.822838  0.571958    0.211389    0.495946   \n",
              "social_support     0.822838        1.000000  0.474982    0.158307    0.446150   \n",
              "freedom            0.571958        0.474982  1.000000    0.385718    0.489324   \n",
              "corruption         0.211389        0.158307  0.385718    1.000000    0.293566   \n",
              "generosity         0.495946        0.446150  0.489324    0.293566    1.000000   \n",
              "gdp_per_cap       -0.822026       -0.795307 -0.416685   -0.163732   -0.475764   \n",
              "life_exp          -0.789545       -0.703266 -0.387847   -0.156077   -0.440401   \n",
              "happiness_score   -1.000000       -0.822838 -0.571958   -0.211389   -0.495946   \n",
              "\n",
              "                 gdp_per_cap  life_exp  happiness_score  \n",
              "Unnamed: 0         -0.822026 -0.789545        -1.000000  \n",
              "social_support     -0.795307 -0.703266        -0.822838  \n",
              "freedom            -0.416685 -0.387847        -0.571958  \n",
              "corruption         -0.163732 -0.156077        -0.211389  \n",
              "generosity         -0.475764 -0.440401        -0.495946  \n",
              "gdp_per_cap         1.000000  0.862144         0.822026  \n",
              "life_exp            0.862144  1.000000         0.789545  \n",
              "happiness_score     0.822026  0.789545         1.000000  "
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "cor = world_happiness.corr(method='spearman')\n",
        "cor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 325
        },
        "id": "J5NJW2lYWdyZ",
        "outputId": "83599a0c-eecb-4071-9903-69227b325b09"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(15, 5))\n",
        "heatmap = sns.heatmap(world_happiness.corr(), vmin=-1, vmax=1, annot=True, cmap='YlGnBu')\n",
        "heatmap.set_title('Matriz de Correlação', fontdict={'fontsize':12}, pad=12)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_RJCm779dgj2"
      },
      "source": [
        "### **Correlação**\n",
        "O coeficiente de correlação\n",
        "- Quantifica a relação linear entre duas variáveis;\n",
        "- É um valor flutuante entre -1 e 1;\n",
        "- Sua magnitude corresponde à força da relação;\n",
        "- O valor (positivo ou negativo) corresponde à direção da relação"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9ZFc5MLkjhgS"
      },
      "source": [
        "#### **Relações Entre Variáveis**\n",
        "Neste capítulo, trabalharemos com a base de dados world_happiness, contendo os resultados do Relatório de Felicidade Mundial 2019. O relatório pontua a felicidade das pessoas em cada país, a partir de uma série de aspectos como apoio social, liberdade, corrupção, entre outros. A base de dados também inclui o GDP per capita e a expectativa de vida de cada país. Neste exercício, vamos examinar a relação entre expectativa de vida e felicidade."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NfB0sxRidlnc"
      },
      "source": [
        "### **Precauções**\n",
        "- A correlação considera apenas relações lineares.\n",
        "\n",
        "- Transformação\n",
        "  - Alguns métodos estatísticos dependem da relação linear entre variáveis.\n",
        "     - Coeficiente de correlação\n",
        "     - Regressão linear\n",
        "\n",
        "- Correlação não implica causalidade\n",
        "   - a correlação entre x e y não significa que x cause y, ou vice-versa.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EEODe_qmerNq"
      },
      "source": [
        "### O que não pode ser medido pela correlação?\n",
        "Embora a correlação seja uma maneira conveniente de quantificar a força de uma relação entre duas variáveis, está longe da perfeição. Neste exercício, vamos explorar uma das precauções do coeficiente de correlação examinando a correlação entre o GDP percapita e a expectativa de vida."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "nKFo6wYPYwxW",
        "outputId": "941a2844-9c87-411b-8acb-23fc1aef69af"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.7019547642148012\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.scatterplot(x='gdp_per_cap', y='life_exp', data=world_happiness)\n",
        "cor = world_happiness['gdp_per_cap'].corr(world_happiness['life_exp'])\n",
        "print(cor)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "LutVbpdwZpFH",
        "outputId": "32fda05a-e9ca-46f4-9721-04f78a4f2b74"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.727973301222298\n"
          ]
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.scatterplot(x='gdp_per_cap', y='happiness_score', data=world_happiness)\n",
        "cor = world_happiness['gdp_per_cap'].corr(world_happiness['happiness_score'])\n",
        "print(cor)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8Ws0AjZkak64"
      },
      "outputs": [],
      "source": [
        "world_happiness['log_gdp_per_cap'] = np.log(world_happiness['gdp_per_cap'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 250
        },
        "id": "5PdhLICsa7n2",
        "outputId": "c9a405e5-192e-4402-8f5d-e65bc3a045c2"
      },
      "outputs": [
        {
          "data": {
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              "      <div>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-87104771-a24a-4e07-a47e-69c7013faf42');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "     Unnamed: 0      country  social_support  freedom  corruption  generosity  \\\n",
              "127         128      Liberia           127.0     94.0       126.0       110.0   \n",
              "27           28        Qatar             NaN      NaN         NaN         NaN   \n",
              "57           58      Bolivia            93.0     35.0        91.0       104.0   \n",
              "45           46       Cyprus            90.0     81.0       115.0        39.0   \n",
              "142         143  South Sudan           148.0    154.0        61.0        85.0   \n",
              "\n",
              "     gdp_per_cap  life_exp  happiness_score  log_gdp_per_cap  \n",
              "127         1140      65.2               15         7.038784  \n",
              "27        113000      80.5              127        11.635143  \n",
              "57          7150      73.3               95         8.874868  \n",
              "45         34500      82.0              107        10.448715  \n",
              "142         1860      59.7                0         7.528332  "
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "world_happiness.sample(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "UjxUEu9mbaC_",
        "outputId": "85ecac9d-23a1-43d6-dfd0-f1dc72cf1a18"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.8043146004918288\n"
          ]
        },
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.scatterplot(x='log_gdp_per_cap', y='happiness_score', data=world_happiness)\n",
        "cor = world_happiness['log_gdp_per_cap'].corr(world_happiness['happiness_score'])\n",
        "print(cor)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "id": "91FqjC4RbreD",
        "outputId": "43fdd174-c859-46b5-9d82-94d8016a47ce"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.lmplot(x='log_gdp_per_cap', y='happiness_score', data=world_happiness)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MKQDc_NVliHs"
      },
      "source": [
        "### Transformando Variáveis\n",
        "Quando variáveis possuem distribuições assimétricas, normalmente é necessário que se faça algum tipo de transformação nos dados para que se possa medir a correlação. No exercício abaixo, faremos a transformação "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "664xaZGvmhpW"
      },
      "source": [
        "### Açúcar Aumenta a Felicidade?\n",
        "Adicionamos uma nova coluna chamada gramas de açúcar por dia, que contém a média de açúcar consumido por pessoa/dia em cada país. Nesse exercício vamos examinar a relação do consumo de açúcar e a felicidade"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "m9OkQwFcb8iu"
      },
      "outputs": [],
      "source": [
        "world_sugar_happy = pd.read_csv('/content/drive/MyDrive/Analise_Estatistica/dataset/world_happiness_sugar.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "OEhOnAcPcFdo",
        "outputId": "6b5e0c45-3a57-4ac6-b365-a555b318467d"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-e4ac60ec-cb97-4004-8ae4-2eccb77fb730\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>country</th>\n",
              "      <th>social_support</th>\n",
              "      <th>freedom</th>\n",
              "      <th>corruption</th>\n",
              "      <th>generosity</th>\n",
              "      <th>gdp_per_cap</th>\n",
              "      <th>life_exp</th>\n",
              "      <th>happiness_score</th>\n",
              "      <th>grams_sugar_per_day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>73</td>\n",
              "      <td>Nigeria</td>\n",
              "      <td>111</td>\n",
              "      <td>75</td>\n",
              "      <td>114.0</td>\n",
              "      <td>59</td>\n",
              "      <td>5300</td>\n",
              "      <td>65.2</td>\n",
              "      <td>71</td>\n",
              "      <td>29.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127</th>\n",
              "      <td>127</td>\n",
              "      <td>Malawi</td>\n",
              "      <td>150</td>\n",
              "      <td>65</td>\n",
              "      <td>64.0</td>\n",
              "      <td>109</td>\n",
              "      <td>1180</td>\n",
              "      <td>64.2</td>\n",
              "      <td>6</td>\n",
              "      <td>22.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85</th>\n",
              "      <td>85</td>\n",
              "      <td>Nepal</td>\n",
              "      <td>87</td>\n",
              "      <td>67</td>\n",
              "      <td>65.0</td>\n",
              "      <td>46</td>\n",
              "      <td>2880</td>\n",
              "      <td>71.5</td>\n",
              "      <td>56</td>\n",
              "      <td>13.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>112</th>\n",
              "      <td>112</td>\n",
              "      <td>Chad</td>\n",
              "      <td>141</td>\n",
              "      <td>142</td>\n",
              "      <td>80.0</td>\n",
              "      <td>106</td>\n",
              "      <td>1740</td>\n",
              "      <td>60.6</td>\n",
              "      <td>24</td>\n",
              "      <td>29.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>114</th>\n",
              "      <td>114</td>\n",
              "      <td>Ethiopia</td>\n",
              "      <td>119</td>\n",
              "      <td>106</td>\n",
              "      <td>53.0</td>\n",
              "      <td>99</td>\n",
              "      <td>1900</td>\n",
              "      <td>69.1</td>\n",
              "      <td>22</td>\n",
              "      <td>18.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>11</td>\n",
              "      <td>Costa Rica</td>\n",
              "      <td>42</td>\n",
              "      <td>16</td>\n",
              "      <td>58.0</td>\n",
              "      <td>75</td>\n",
              "      <td>15800</td>\n",
              "      <td>79.8</td>\n",
              "      <td>144</td>\n",
              "      <td>144.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>Netherlands</td>\n",
              "      <td>15</td>\n",
              "      <td>19</td>\n",
              "      <td>12.0</td>\n",
              "      <td>7</td>\n",
              "      <td>50500</td>\n",
              "      <td>81.8</td>\n",
              "      <td>151</td>\n",
              "      <td>122.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>89</td>\n",
              "      <td>South Africa</td>\n",
              "      <td>63</td>\n",
              "      <td>85</td>\n",
              "      <td>102.0</td>\n",
              "      <td>89</td>\n",
              "      <td>12000</td>\n",
              "      <td>66.9</td>\n",
              "      <td>50</td>\n",
              "      <td>101.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>92</td>\n",
              "      <td>Cambodia</td>\n",
              "      <td>109</td>\n",
              "      <td>2</td>\n",
              "      <td>94.0</td>\n",
              "      <td>61</td>\n",
              "      <td>4080</td>\n",
              "      <td>70.4</td>\n",
              "      <td>47</td>\n",
              "      <td>51.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>107</th>\n",
              "      <td>107</td>\n",
              "      <td>Iraq</td>\n",
              "      <td>124</td>\n",
              "      <td>130</td>\n",
              "      <td>66.0</td>\n",
              "      <td>73</td>\n",
              "      <td>15700</td>\n",
              "      <td>77.1</td>\n",
              "      <td>30</td>\n",
              "      <td>52.8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e4ac60ec-cb97-4004-8ae4-2eccb77fb730')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "      \n",
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              "    .colab-df-container {\n",
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              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e4ac60ec-cb97-4004-8ae4-2eccb77fb730');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "     Unnamed: 0       country  social_support  freedom  corruption  \\\n",
              "73           73       Nigeria             111       75       114.0   \n",
              "127         127        Malawi             150       65        64.0   \n",
              "85           85         Nepal              87       67        65.0   \n",
              "112         112          Chad             141      142        80.0   \n",
              "114         114      Ethiopia             119      106        53.0   \n",
              "11           11    Costa Rica              42       16        58.0   \n",
              "4             4   Netherlands              15       19        12.0   \n",
              "89           89  South Africa              63       85       102.0   \n",
              "92           92      Cambodia             109        2        94.0   \n",
              "107         107          Iraq             124      130        66.0   \n",
              "\n",
              "     generosity  gdp_per_cap  life_exp  happiness_score  grams_sugar_per_day  \n",
              "73           59         5300      65.2               71                 29.4  \n",
              "127         109         1180      64.2                6                 22.6  \n",
              "85           46         2880      71.5               56                 13.0  \n",
              "112         106         1740      60.6               24                 29.1  \n",
              "114          99         1900      69.1               22                 18.1  \n",
              "11           75        15800      79.8              144                144.0  \n",
              "4             7        50500      81.8              151                122.0  \n",
              "89           89        12000      66.9               50                101.0  \n",
              "92           61         4080      70.4               47                 51.5  \n",
              "107          73        15700      77.1               30                 52.8  "
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "world_sugar_happy.sample(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "QroS7HbqcRDb",
        "outputId": "a584637e-b3ae-4718-acce-776f9d6f5832"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.6939100021829633\n"
          ]
        },
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.scatterplot(x='grams_sugar_per_day', y='happiness_score', data=world_sugar_happy)\n",
        "cor = world_sugar_happy['grams_sugar_per_day'].corr(world_sugar_happy['happiness_score'])\n",
        "print(cor)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kKOgTsySmatp"
      },
      "source": [
        "### Atividade 04\n",
        "\n",
        "- Importar base de dados world_happiness_sugar\n",
        "- Verificar a distribuição dos dados (histograma) de 'social_support' e 'grams_sugar_per_day' \n",
        "- Calcular a correlação destas variáveis com o 'happiness_score' utilizando o método que julgar mais adequado;\n",
        "- Plotar os gráficos de dispersão com a linha de tendência para conferir a linearidade dos dados;\n",
        "- Caso necessário, transformar os dados utilizando np.log e plotar os gráficos novamente;\n",
        "- Descrever os resultados observados;"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "kX6W7AREoWkZ",
        "outputId": "c8a2d683-3e7c-489c-8b0a-8bb560fc897b"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.histplot(x='social_support', data=world_sugar_happy, kde=True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "Ypg6fclkp0Ku",
        "outputId": "845c4eb1-c6de-4ce3-f824-b2331f8b10f0"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.histplot(x='grams_sugar_per_day', data=world_sugar_happy, kde=True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GBBjL5GWs38N"
      },
      "source": [
        "Como os dados não apresentam distribuição normal, optou-se pelos métodos de correlação não-paramétricos"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "NLbV4uCBq7fW",
        "outputId": "436285e1-a394-46c4-e1d2-da4e1e99e7b6"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-c7ff9e63-c5a5-4063-b8b7-9839a0d7e003\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>social_support</th>\n",
              "      <th>freedom</th>\n",
              "      <th>corruption</th>\n",
              "      <th>generosity</th>\n",
              "      <th>gdp_per_cap</th>\n",
              "      <th>life_exp</th>\n",
              "      <th>happiness_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.625612</td>\n",
              "      <td>0.408850</td>\n",
              "      <td>0.134992</td>\n",
              "      <td>0.352312</td>\n",
              "      <td>-0.627291</td>\n",
              "      <td>-0.583063</td>\n",
              "      <td>-1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>social_support</th>\n",
              "      <td>0.625612</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.339327</td>\n",
              "      <td>0.098286</td>\n",
              "      <td>0.302767</td>\n",
              "      <td>-0.598221</td>\n",
              "      <td>-0.512139</td>\n",
              "      <td>-0.625612</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freedom</th>\n",
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              "                 Unnamed: 0  social_support   freedom  corruption  generosity  \\\n",
              "Unnamed: 0         1.000000        0.625612  0.408850    0.134992    0.352312   \n",
              "social_support     0.625612        1.000000  0.339327    0.098286    0.302767   \n",
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              "corruption         0.134992        0.098286  0.264566    1.000000    0.190050   \n",
              "generosity         0.352312        0.302767  0.335731    0.190050    1.000000   \n",
              "gdp_per_cap       -0.627291       -0.598221 -0.286871   -0.093895   -0.327638   \n",
              "life_exp          -0.583063       -0.512139 -0.269684   -0.098167   -0.298714   \n",
              "happiness_score   -1.000000       -0.625612 -0.408850   -0.134992   -0.352312   \n",
              "\n",
              "                 gdp_per_cap  life_exp  happiness_score  \n",
              "Unnamed: 0         -0.627291 -0.583063        -1.000000  \n",
              "social_support     -0.598221 -0.512139        -0.625612  \n",
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              "gdp_per_cap         1.000000  0.672098         0.627291  \n",
              "life_exp            0.672098  1.000000         0.583063  \n",
              "happiness_score     0.627291  0.583063         1.000000  "
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "cor = world_happiness.corr(method='kendall')\n",
        "cor"
      ]
    },
    {
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      "execution_count": 24,
      "metadata": {
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              "                 Unnamed: 0  social_support   freedom  corruption  generosity  \\\n",
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          },
          "execution_count": 24,
          "metadata": {},
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      ],
      "source": [
        "cor = world_happiness.corr(method='spearman')\n",
        "cor"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AR1CgIEdtBcw"
      },
      "source": [
        "Para continuidade da análise, preferimos adotar o método de spearman, pois enfatiza melhor a correlação entre as variáveis que o método kendall"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
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        "outputId": "4b7230f9-c5ac-4b88-bdd5-068cd551f2fa"
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          "name": "stdout",
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            "0.6891139124272058\n"
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",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.lmplot(x='grams_sugar_per_day', y='happiness_score', data=world_sugar_happy)\n",
        "cor = world_sugar_happy['grams_sugar_per_day'].corr(world_sugar_happy['happiness_score'], method='spearman')\n",
        "print(cor)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cVKH4pv9rraf"
      },
      "source": [
        "Existe uma correlação linear positiva entre as variáveis happiness_score e grams_sugar_per_day. Isto sugere que em países onde o consumo de açúcar é maior, a felicidade das pessoas também é maior. Alguns estudos sugerem que o consumo de açúcar conduz ao estímulo direto do cérebro ativando a liberação da dopamina, neurotransmissora do bem-estar. Entretanto, com base nos dados observados, é impossível avaliar se existe de fato algum impacto de uma variável na outra, sendo necessário um estudo mais aprofundado para verificar a relação de causalidade entre as duas variáveis."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "UpUeXnNVqVT1",
        "outputId": "bdc83b73-cf40-48b6-d843-de3be7a6c2cb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "-0.8111323083828975\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.lmplot(x='social_support', y='happiness_score', data=world_sugar_happy)\n",
        "cor = world_sugar_happy['social_support'].corr(world_sugar_happy['happiness_score'], method='spearman')\n",
        "print(cor)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G4Z40kSzszsC"
      },
      "source": [
        "Existe uma correlação linear negativa entre as variáveis happiness_score e social_support. Isto sugere que em países onde o apoio social é maior, a felicidade é menor. É possível que a causa da infelicidade das pessoas esteja atrelada às questões que demandam apoio social. Porém, também é possível que as populações mais felizes percebam menos o apoio social que possuem. Assim, seriam necessários estudos mais aprofundados para avaliar se de fato o apoio social tem impacto negativo na felicidade das pessoas."
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3.10.6 64-bit",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.10.6"
    },
    "vscode": {
      "interpreter": {
        "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}