Desafio_semanas_10_11.ipynb

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        "import pandas as pd\n",
        "import numpy as np\n",
        "from scipy import stats\n",
        "from scipy.stats import binom\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CvkPQtfOG-fW"
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      "source": [
        "#Contexto\n",
        "Considerando a base de dados de populacao_brasileira.json responda as\n",
        "questões abaixo (os dados são fictícios).\n",
        "Você é uma pesquisadora desenvolvendo uma análise sobre as\n",
        "características da força de trabalho nos estados brasileiros. Responda as\n",
        "perguntas abaixo:"
      ]
    },
    {
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      "execution_count": 2,
      "metadata": {
        "id": "lDKWYin7Gabn"
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      "source": [
        "df = pd.read_csv('/content/drive/MyDrive/WoMakersCode/RRLNbvGQQtu5fPdxzFPI_populacao_brasileira.csv.csv')"
      ]
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            "text/plain": [
              "   Unnamed: 0 estado  idade escolaridade nível de proficiência em inglês  \\\n",
              "0           0     MA     56  Fundamental                   Intermediário   \n",
              "1           1     BA     46     Superior                          Básico   \n",
              "2           2     AM     32  Fundamental                   Intermediário   \n",
              "3           3     CE     60     Superior                          Básico   \n",
              "4           4     SE     25  Fundamental                        Avançado   \n",
              "5           5     AM     38  Fundamental                        Avançado   \n",
              "6           6     DF     56        Médio                   Intermediário   \n",
              "7           7     DF     36     Superior                        Avançado   \n",
              "8           8     PI     40  Fundamental                          Básico   \n",
              "9           9     AP     28     Superior                        Avançado   \n",
              "\n",
              "     renda sexo  \n",
              "0  4006.29    F  \n",
              "1  2423.11    M  \n",
              "2  3835.69    M  \n",
              "3  1870.29    M  \n",
              "4  3529.80    M  \n",
              "5  4441.57    F  \n",
              "6   528.36    F  \n",
              "7  2203.10    F  \n",
              "8  3577.07    F  \n",
              "9  2796.95    M  "
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
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      "source": [
        "df.head(10)"
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        {
          "data": {
            "text/plain": [
              "(1000, 7)"
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        "df.shape"
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1000 entries, 0 to 999\n",
            "Data columns (total 7 columns):\n",
            " #   Column                           Non-Null Count  Dtype  \n",
            "---  ------                           --------------  -----  \n",
            " 0   Unnamed: 0                       1000 non-null   int64  \n",
            " 1   estado                           1000 non-null   object \n",
            " 2   idade                            1000 non-null   int64  \n",
            " 3   escolaridade                     1000 non-null   object \n",
            " 4   nível de proficiência em inglês  1000 non-null   object \n",
            " 5   renda                            1000 non-null   float64\n",
            " 6   sexo                             1000 non-null   object \n",
            "dtypes: float64(1), int64(2), object(4)\n",
            "memory usage: 54.8+ KB\n"
          ]
        }
      ],
      "source": [
        "df.info() #Não temos valores nulos"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kHFW9l5dHE0u"
      },
      "source": [
        "###1. Considere pessoas fluentes em inglês. Qual a probabilidade complementar? Ou seja, qual a probabilidade de escolhermos uma pessoa aleatória e ela não ser fluente em inglês? Considere fluente quem tem o nível avançado.\n",
        "\n",
        "**Resposta**: 65,6%"
      ]
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      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "656"
            ]
          },
          "execution_count": 38,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "\n",
        "amostra = df[df['nível de proficiência em inglês'] != 'Avançado'].shape[0]\n",
        "amostra"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bg-G8foeGw6L",
        "outputId": "9abab6b6-3669-4d9c-8a46-da96fe742c1b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1000"
            ]
          },
          "execution_count": 39,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "espaco_amostral = df.shape[0]\n",
        "espaco_amostral"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2A_qLSd7I2cA",
        "outputId": "6730eb09-9f00-4f1b-827d-1f77652c3931"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "65.60000000000001"
            ]
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "probab_complementar = (amostra/espaco_amostral)*100\n",
        "probab_complementar"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PiCDxlgKWkM3",
        "outputId": "a9330835-e925-4f97-efde-97f674a9ed01"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.656"
            ]
          },
          "execution_count": 45,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Utilizando a função de massa da probabilidade da distribuição binomial\n",
        "binom.pmf(1, 1, amostra/espaco_amostral)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iUatGtvXJikZ"
      },
      "source": [
        "###2. Se uma pessoa escolhida aleatoriamente for de Alagoas ou do Pará, qual é a probabilidade de ela ter uma renda superior a 5 mil reais?\n",
        "**Resposta**: 5,1%"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mnCgtsxgJkts",
        "outputId": "e5a94a4f-7eeb-4f7b-965f-6e917bcad596"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "79"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#espaço amostral: pessoas do AL e do PA\n",
        "#condição: ter renda superior a 5 mil\n",
        "\n",
        "espaco_amostral = df[(df['estado'] == 'AL') | (df['estado'] == 'PA')]\n",
        "espaco_amostral.shape[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8gAsmKuvOCVv",
        "outputId": "09874c33-c18b-4cf2-d3b1-292dc05c38e8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "amostra = espaco_amostral[espaco_amostral['renda'] > 5000]\n",
        "amostra.shape[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3jtWdxfIPaHZ",
        "outputId": "46d889f4-c30f-4b8a-954f-b9bb1e1a05f6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "5.063291139240507"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "probab = (amostra.shape[0]/espaco_amostral.shape[0])*100\n",
        "probab"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iJzmcXKYP2dK"
      },
      "source": [
        "##3. Descubra a probabilidade de uma pessoa ter ensino superior completo no estado do Amazonas. Qual a probabilidade da quinta pessoa que você conversar, que é amazonense, ter ensino superior completo?\n",
        "\n",
        "**Resposta**: 0,08"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y2TtxvLXQhrB",
        "outputId": "9473d46c-2f62-41bd-f917-b68117a72ead"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['Fundamental', 'Superior', 'Médio', 'Pós-graduação'], dtype=object)"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.escolaridade.unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a6qcHcFsQCoZ",
        "outputId": "7021714f-4b3f-41df-d073-bd571f66819a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.5526315789473685"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Probabilidade de uma pessoa ter ensino superior completo no Amazonas (considerei ensino superior apenas, não incluindo pós-graduação): 23,7%\n",
        "amostra = df[(df['estado'] == 'AM') & ((df['escolaridade'] == 'Superior') | (df['escolaridade'] == 'Pós-graduação'))].shape[0]\n",
        "#espaço amostral: população do estado do Amazonas\n",
        "espaco_amostral = df[df['estado'] == 'AM'].shape[0]\n",
        "\n",
        "probab = (amostra/espaco_amostral)\n",
        "probab"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CWyh__WUaqaC",
        "outputId": "aa91dd91-8e46-4439-c6b4-c6e6a4ddd479"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.022135890467222828"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "probabilidade = stats.geom.pmf(k=5, p=probab)\n",
        "probabilidade"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xAgf9QaLQO3O"
      },
      "source": [
        "##4. Considerando a renda das pessoas do nosso conjunto, podemos dizer que a renda de uma pessoa brasileira está na sua maioria em que faixa (faça faixa de 1.500 reais)? Qual é a sua função densidade de probabilidade?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8O6p7oRz5RVB"
      },
      "source": [
        "####Pelo gráfico plotado percebemos que a faixa de renda com maior densidade é a de 3.000 reais a 4.500 reais."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 474
        },
        "id": "O8NtVRzfyFnb",
        "outputId": "2d18ba6f-e719-459e-b6a4-a9e11ba45c2f"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "renda_min = df['renda'].min()\n",
        "renda_max = df['renda'].max()\n",
        "faixas = np.arange(0, renda_max + 1500, 1500)\n",
        "plt.hist(df['renda'], bins = faixas, color = 'purple', edgecolor = 'black')\n",
        "\n",
        "plt.xlabel('Faixas de renda')\n",
        "plt.ylabel('Quantidade de pessoas')\n",
        "plt.title('Densidade por faixa de renda', fontsize = 16)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SmvHc063QYKO"
      },
      "source": [
        "##5. Calcule a média e a variância da renda da amostra. Depois faça a distribuição normal, incluindo o gráfico."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GmZBEWDv5tdj",
        "outputId": "0ee73fab-8b13-41ec-9212-08e15821e5b9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "A média é 3082.54 e a variância é 993156.23.\n"
          ]
        }
      ],
      "source": [
        "media = df['renda'].mean()\n",
        "variancia = df['renda'].var()\n",
        "print(f'A média é {media:.2f} e a variância é {variancia:.2f}.')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 474
        },
        "id": "kNaKoOh158SU",
        "outputId": "f2acff47-4b46-4053-ae69-47b65f224743"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.hist(df['renda'], bins = 10, color = 'orange', edgecolor = 'black')\n",
        "\n",
        "plt.title('Distribuição da renda da população brasileira', fontsize = 16)\n",
        "plt.xlabel('faixas de renda')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kZKPNkQgSA1b"
      },
      "source": [
        "##6. Primeiro considere a probabilidade encontrada no nosso conjunto de pessoas com escolaridade de pós-graduação. Considerando a amostra de população brasileira com 1 milhão de habitantes, qual a probabilidade de encontrarmos 243 mil pessoas com pós-graduação?\n",
        "\n",
        "**Resposta:** Há 2,25% de chance de encontrar uma pessoa com pós-graduação em nosso dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4KVNRyAm9q3i"
      },
      "outputs": [],
      "source": [
        "prop = len(df[df['escolaridade'] == 'Pós-graduação'])/len(df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V3oswjHh956A",
        "outputId": "1f673bac-9fde-4891-d936-21ddd60f6d43"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.022515178317096114"
            ]
          },
          "execution_count": 84,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "stats.binom.pmf(243, 1000, prop)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "by6ZHidHXz8u"
      },
      "source": [
        "##7. Somando as densidades nós temos a função de densidade acumulada. Considerando a coluna ‘Escolaridade’ faça a função de densidade acumulada discreta para cada nível de escolaridade."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-GqcObpZttjK",
        "outputId": "d97ae483-6511-4e88-f01b-eb0b9c8eaeb6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['Fundamental', 'Superior', 'Médio', 'Pós-graduação'], dtype=object)"
            ]
          },
          "execution_count": 46,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.escolaridade.unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "RQhP6_f_t4CP"
      },
      "outputs": [],
      "source": [
        "prob_fundamental =len(df[df['escolaridade'] == 'Fundamental'])/len(df)\n",
        "prob_superior =len(df[df['escolaridade'] == 'Superior'])/len(df)\n",
        "prob_medio =len(df[df['escolaridade'] == 'Médio'])/len(df)\n",
        "prob_pg =len(df[df['escolaridade'] == 'Pós-graduação'])/len(df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 567
        },
        "id": "YTe3MXS4thtz",
        "outputId": "834b039c-5e20-4221-919e-5f0ae44109f7"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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efvllI8k0bNjQ8rXfrQjKWSwlKKf1uPbAf6tB2c/Pz/LD/9133xlJ5pFHHnFof/HFF40kEx4efqsv0xhj7F8UkyZNcmhv1KiRkWQiIyMtl2vevPkNz6Jd68KFC/aDwIIFC1JNP3r0qP1AcP2XS8oB1+oskzFX/wqXZNq0aZOhWoxx7stl8uTJRpLx8fFxaLcKksePHzeSTJUqVZyuJSNBeeTIkU6vJ6VOHx8fc/To0VTLzZ071x7Cr/0yyOygPHv2bPuZmeu/aG/GokWLjCTTrl07h/aUszk1atSwXO69994zkkzZsmWd2t6VK1fsZyHnzp3rMC3lbKjV7zDlj95x48ZlaDuZEZSv/4PDGGPGjx9vpKv/IbL6Eu/Xr5+RUv93JiuCcno6duxo+RrKlCljJJlPPvkk1TIXL160nwl1JjAaY0zhwoWNm5ubQ8BIz60cy9ITHx9vcuTIYfLnz5/hZYz53+/nRo9r/0M1ZswYy0CWlgkTJhjp6n/zrly5csP5U86c+/r6Wp6k+fvvv40k4+bmZg4dOmRvTy8opyet77GUfdPDw8Ps2bMnzeWtAuiKFSvs39FWZ/lnzZqVZlBOT1rHrZQTMvfff7/lcv3797esM+WkVtWqVc3ly5dTLZeUlGT/74HV5/5uxagXt0law8NlZqf/mjVrWo5SULFiRUlK1U954cKFkqRnn33Wqe2cOnVK8+bN05YtW3TmzBl7/7Ndu3ZJknbs2GGf98qVK1q5cqUkpdnvsHv37vrll1+0dOnSDG1/3bp1OnfunPLly6fHHnss1fSCBQuqSZMmmjNnjkP7yZMntXr1avn4+KQadSJFSl+z6/tmZ5aU/mQZGb8yf/78KlGihDZt2qSXX35Z3bt3z9SLJtu2bXvTyzZp0kQFCxZM1d68eXPlzZtXp06d0rp16/TAAw/cSolpStl3O3XqJH9//wwvl5CQoMWLF2vNmjU6fvy4EhISZIyxj/997b4ryT6maUREhOX6unfvrldeeUW7du3SkSNHVLhw4QzVsWDBAh05ckSFChXS448/7jCtW7dueuWVVzR16lSHz8yxY8e0YcMGubm5qXv37hl8xbcmR44c9iH0rlW2bFlJUrFixVS5cuU0p99sn11nHTlyRPPmzdP27dsVGxtrH7P8n3/+kXT199q0aVNJV4+Du3fvliQ9/fTTqdbl7e2t9u3ba+LEiWlub+PGjYqKitK+ffsUHx9v/1xfuXJFycnJ2r17t6pVq3bDum/2WHatVatW6Y8//tDBgwd14cIFGWMkXb3w+sSJEzpz5oxy5859w1qudaPh4YoVK2b/uVatWpKkMWPGKG/evGrevLny5MmT5rIpn93u3bvL3d39hrWkfAYfe+wxBQUFpZpeo0YNhYaGauPGjVq+fLmeeuqpG65Tcu577FrVqlVTqVKlMrQNq9eQciH8tVq2bKnAwEDFxsZaLn+zx6203ouIiAh98MEHqdrnzZsnSWrTpo1y5EgdD93c3PTQQw9py5YtWrVqleVn/25EUL5NbsfwcNcevK4VEBAg6epQVNdKuejHmbA+ZcoUvfTSS5YX6qS4dkSHU6dO2bdbsmRJy/lTRiFI74LDa/3777+SlO5A8lbb2rdvn4wxunjxory8vNLdxokTJzJUi7NOnjwpSel+kVzrq6++Utu2bTV+/HiNHz9eefLkUZ06dfToo4/qmWeeuaVRMm5lIP60fpcp6z116pT995QVbmbf/fPPP9WhQ4d0L5C6fjSSlH0yrdebK1cu5cmTR6dPn9a///6b4aCcMrJF586dU4WFZ555RoMGDdKKFSu0c+dOlStXTpLsdRcqVOi2jUZSqFAhyy/MlD9O0jrmpIynfv0xJyuMGDFCb7/9tuUFYymu/b2m7Jf58uVL84+stH7f8fHxeuaZZzRr1qx0a7p+P0rLzR7LJOn48eNq06aNVqxYccNanA3KzgwP16BBAw0YMEBjx45VRESEbDabypYtq3r16qlly5YKDw+Xm9v/xg1w9rN7o8+gdPU7ZOPGjRn+DnH2e+xaN3PcTPk9p/UaUi6c3bhxY6ppN3PcutH20mrfu3evJGnIkCEaMmRImtuTsu47MjsiKN9B0ru6VZLDwSgrrF27Vs8995zc3d317rvvKjw8XMWKFZOvr69sNps+++wzPffcc/YzGtlNyvvn7+/vsjugrVu3TpIUEhKSofkffPBB7d+/X/PmzdPy5cu1atUqLVq0SAsWLNCwYcM0a9asm76BiY+Pz00tl1HO7Ac32rdv1YULF9SqVSvFxMSoa9euev7551WmTBkFBATI3d1dO3fuVPny5W/LvhsTE6NffvlF0tUbjFgFHQ8PD12+fFnTpk3T6NGjs6yWWz2mZPYxx9n9YObMmRo+fLj8/f310Ucf6ZFHHlHhwoXl4+Mjm82m119/XaNGjcq03+ugQYM0a9YsVahQQaNHj1atWrWUL18++/CCDzzwgKKjo2/LftSjRw+tWLFCdevW1YgRIxQaGqrcuXPbxxwvXLiwjh49eltqGT16tHr16mXfn1euXKnp06dr+vTpqlWrlpYuXSo/P78sryMjbvV7LKuPm9e63cetlM9f/fr1bzgk53333Zcp27wTEJSzkZSDbVq3gb522KfMUKxYMe3YsUPbt29P866B1/rhhx9kjFG/fv302muvpZqe8i+ra+XNm1deXl5KSEjQ3r17VaVKlVTzpPwVW6RIkQzVnTJfylBmVqymBQcHS7r61/u0adOy/A+L6508eVKLFi2SJMt/Z6fFx8dHbdu2tXeVOHHihAYPHqzPPvtM3bp1y/T9IiP27duX5rSU975o0aL2tszet1POZG7fvj1D8//++++KiYlR9erVLYdHstp3pav72vbt2+376PViY2Ptw6RldP/96quv7F0Dtm7dmu68X375pd566y3lyJHD/pqPHj2q2NjYDJ1Vvt3HlBvJ7HpShud7++23LbuQWf1eU35PJ0+e1Pnz5y3PKqd1bEnZ3owZMyyPZWntR2m52WNZfHy85s+fLzc3N82fP1+5cuVKNf3YsWNO1XKrSpQooX79+qlfv36Srt4u/umnn9aaNWs0ZswYjRgxQtLVz+62bdu0ffv2DN0NM+U9SuszeO20jHwGb+Z77FZl5Pdste/f6nErre2l1Z7yHdmyZUu98soradZ6r2Ec5Wwk5cNkdWcfY4zl2JK3IqVP3PVj+qYlJRAUL1481bRLly7pp59+StWeI0cOe1+3tP6Vl3IAaNiwYYbqqFGjhvz9/XXy5EktXrw41fSYmBjL9sKFC6tKlSo6d+6cvZ/c7WKMUd++fXXx4kXlyZPnlvqY5s+fX2PGjJF09d/xZ86csU9LCSIpQSyrLF68WMePH0/VPn/+fJ06dco+xmmK9PZt6X994zIqZd/973//m+6/T1Ok7LtpdRX45ptvLNtT+qynjE99vZR9t2zZshkOyindLj755BOZqxdUp3pcuXJFhQoV0rFjx+xjexcsWFChoaFKTk7O0Fio0v/e97T+oHD2fb9VmX2MS++YdPz4cS1ZsiRVe9GiRe19TK8fa1u62h/0hx9+cHp7ixYtsnetyqibPZbFxsYqKSlJAQEBqUKydHV/dvV/9mrVqqXevXtLkjZs2GBvT/nsTps2TUlJSTdcT8pncOHChYqJiUk1ff369fa++w899NAN13cz32O36uGHH5Z09TWkbP9ac+bMsRz3+maPWynb+/bbby2nf/XVV5btKddLpPwxgasIytlIyl/XX3/9tcOZpsuXL2vAgAHpDp5/MyIjI5UzZ07NmTNHgwcPTtXH7/jx4w7/Fk65KPDLL790OCN06dIl9e7dO82zjC+//LKkq8EgKirKYdoXX3yhOXPmyMPDQ/37989Q3T4+PvazRy+99JKOHj1qn3bx4kU9//zzaQ7g/tZbb0m6ekOOuXPnpppujNFff/1l+eV0szZt2qSmTZtqxowZcnd31zfffGPvw5meAwcO6PPPP7fsK5dSe+7cue190KX/ncVNuYgpq1i9z0eOHLH/rnv16iVvb2/7tEceeURubm5atGiRli9fbm83xmjixIlOfzm1aNFC1apV05EjR9SuXbtUg+BfunTJIXSl7LtRUVGpzuJ+9tlnmjFjhuV2evbsqYCAAK1bt07vvPOOw5fH+vXr7fvTq6++mqG6V6xYoR07dsjLy0sdOnRIcz53d3f7hTjXhuJhw4ZJkt544w3L92zr1q0OIbR27dr2m5h8/fXXDvP+8MMP6V6wlhUy+xiX8nv97LPPlJiYaG+PjY1VREREmhdHvfjii5Ku3gjn2j8ikpKS9Morr6R5EWLK9j788EOH9h07dqhXr15O1S7d/LEsKChIuXPn1tmzZ1P9Xv/8808NGjTI6Vpu1qxZs/T777+n6jZz+fJl+wmJa0Npjx49VLRoUa1fv149e/ZM9YduXFycw00/6tevrzp16ujixYt67rnndOHCBfu0kydP6rnnnpMkPfnkk/Yzoum52e+xW/Hggw+qevXqOn/+vPr06aOEhAT7tEOHDqV59vZmj1vdu3eXv7+/oqOjU33Gly1bpsmTJ1su17JlS9WqVUurV69W165dLfshnzlzRpMnT87ykzHZyu0ZXOPe5cw4ysYY+7iZPj4+5tFHHzUtWrQwRYsWNQEBAfYhXdIaHs7ZIaCMuTq8TMrwREFBQaZVq1amXbt2pnbt2qluOHLmzBn768mbN69p1aqVadOmjSlQoIDJmTNnmvUZ43jDkfr165tOnTrZx7+8mRuOnD9/3tSuXdtIMv7+/iY8PNy0a9fOFCxY8IY3HPnggw9Mjhw5jHR1UP5mzZqZTp06mUcffdQ+IPyAAQMyXEvKkEqlS5e23+DjqaeeMs2aNXMYHrBkyZLmt99+s1yH1fBp69evtw9HVKtWLdO+fXvTvn17U61aNft7+fnnnzusZ+PGjcbNzc24ubmZxo0bm65du5ru3bubn3/+2T6PLIYFSus1pXfDkTx58piCBQuadu3amfDwcPsNR+rWrZtq3Fpj/jckkbu7u2nQoIFp3bq1KV26tPHw8DADBw50ang4Y66OI12+fHn70FFNmjQxHTt2NA899JDlDUdSPlspN5h48sknTYUKFYzNZrPfjMRqO3PnzrUP0VWhQgXTsWNH06hRI/s+1LVr13Tfy2t16dLFcjgnK5s2bTLS1ZtmXDss1ttvv21sNpu9ng4dOpgWLVqkOazc+++/b/+d161b17Rt29bcd999xmazmSFDhqQ7PFxaQ2vdaLi29I5JN3OMS2t7e/futd8wqUiRIvYbUQQGBppChQqZbt26WR4HkpKSTHh4uH1/CAsLM08++aQpWbKk8fb2to+lfH0dP/30k/29DwkJsd98xsPDwzzyyCPmgQcecHqIr5s9ll37e61Tp47p2LGjqVevnrHZbOaZZ56xH3v27duX4VqsjmVWjyFDhtiXSfmd5cuXzzz66KPmqaeeMi1atLAfS4sUKeIwbJsxxqxbt84+BF+uXLlMs2bNTIcOHcwDDzxwwxuOFChQwLRt29a0bNnSBAQEGMm5G47c7PfYjb5nU6R1fP3nn39M/vz5jSRTuHBh0759e9O8eXPj6+tr7r///jRvOHKzx63//ve/9huOhISE2I+NNpvNvPTSS2nWefjwYfswlH5+fuaBBx4wTz75pGndurWpWrWqfZ3X31TqbkZQzmLOBuVLly6ZwYMHm1KlShkPDw9ToEAB07FjR7N79+4bjqN8M0HZmKsDnvfv398eOqSr48J269Yt1Q0pTpw4YXr37m1Kly5tvLy8TOHChc3TTz9tdu3adcM6FixYYJo2bWry5s1rcuTIYQ9Zf/31V4bem+vFx8ebIUOG2O/MFRQUZJ566imzb9++G47bu3nzZvPss8+asmXLGm9vb+Pr62tKlSplwsLCzMSJE1PdNCU9VmOPenl5mQIFCpgaNWqYnj17mp9//tlyXMoUVvXGxcWZCRMmmCeeeMKULVvW+Pv7Gz8/P1OuXDnTuXNn8/fff1uua9asWaZevXomZ86c9i/1a9ebGUF52LBhZu/evaZjx44mKCjIeHp6mjJlypihQ4ea+Ph4y3UmJyebcePGmYoVKxpPT0+TJ08eEx4ebtauXXtTd+Yzxphz586Zd999134nyZQv3hYtWpjvvvvOYd7ExEQzduxYExISYnx9fU2ePHlMkyZNzOLFi2+4na1bt5qIiAhTtGhR4+HhYXLlymUaNmyYahvpiYuLs/8x8csvv2RomZQvrHfffdehPTo62nTs2NEUKVLEeHh4mDx58pjQ0FDz2muvmQMHDqRaz5dffmmqV69uvL29TUBAgHnkkUfMkiVLMnRnPiu3EpRv5hiX3vb27dtnnnrqKVOsWDHj5eVlihcvbnr16mWOHTuW7nHg8uXLZty4caZSpUrGy8vL5M2b17Rs2dJs2LAh3fp///1306hRI5MvXz7j6+trKleubN5++22TkJBgHn74YaeDsjE3fyybPXu2eeCBB0yuXLmMv7+/qVmzpvn4449NcnLyLQXlGz1CQ0Pty6xfv94MHDjQ1K9f3xQpUsR4enqa/Pnzmxo1aph33nknzbsDnjhxwgwePNiEhIQYPz8/4+PjY0qVKmU6dOhgFi5cmGr+U6dOmUGDBpmKFSvaj9nVqlUzo0ePtvzDPL19+Ga+x241KBtz9bu2S5cu9mNmqVKlzIABA0x8fHya+86tHLf++OMPExYWZgICAuzv16effnrDOi9dumQmT55sGjZsaP++LlCggKlatarp06ePWbRoUbrvwd3GZgwdUfA/Fy9eVOXKlRUZGak+ffq4uhzAKZ06ddLly5fT7GMKAIAz6KMMBz4+PmrWrFmWDkkFZJW2bdvqxx9/1J49e1xdCgDgLsDwcJAkTZw4UVFRUfL09NT8+fMzdLEZkB1ER0dryJAhCgoKsl8omN7NJwAAyCiCMiRdvXvfn3/+qbNnz6pUqVIaO3asq0sCMsTPz0/79u3T77//rnz58untt9/O1FvDAwDuXfRRBgAAACzQRxkAAACwQFAGAAAALNxzfZSTk5N15MgR5cyZUzabzdXlAAAA4DrGGJ07d06FCxeWm5vrzuvec0H5yJEjGbrNJQAAAFzr0KFDKlq0qMu2f88F5ZRhzw4dOqSAgAAXVwMAAIDrxcXFKTg42OXD1d5zQTmlu0VAQABBGQAAIBtzdTdZLuYDAAAALBCUAQAAAAsEZQAAAMACQRkAAACwQFAGAAAALBCUAQAAAAsEZQAAAMACQRkAAACwQFAGAAAALBCUAQAAAAsEZQAAAMACQRkAAACwQFAGAAAALBCUAQAAAAsuDcq///67wsPDVbhwYdlsNs2ePfuGyyxbtkzVq1eXl5eXypQpoy+++CLL6wQAAMC9x6VBOT4+XqGhoZo0aVKG5t+3b5+aNWumhg0basOGDXrxxRfVo0cPLVq0KIsrBQAAwL0mhys3/vjjj+vxxx/P8PyTJ09WyZIlNW7cOElSxYoVtWLFCr3//vsKCwvLqjIBAADuWcZIFy5c/dnXV7LZXFvP7XRH9VGOjo5W48aNHdrCwsIUHR2d5jIJCQmKi4tzeAAAACBjLlyQ/P2vPlIC873ijgrKx44dU1BQkENbUFCQ4uLidPHiRctlRo0apcDAQPsjODj4dpQKAACAO9wdFZRvxqBBgxQbG2t/HDp0yNUlAQAA4A7g0j7KzipYsKBiYmIc2mJiYhQQECAfHx/LZby8vOTl5XU7ygMAAMBd5I46o1y3bl1FRUU5tC1ZskR169Z1UUUAAAC4W7k0KJ8/f14bNmzQhg0bJF0d/m3Dhg06ePCgpKvdJjp37myfv1evXtq7d69ee+01bd++XR9//LG+//57vfTSS64oHwAAAHcxlwblv//+W9WqVVO1atUkSZGRkapWrZqGDh0qSTp69Kg9NEtSyZIlNW/ePC1ZskShoaEaN26cPv/8c4aGAwAAQKazGWOMq4u4neLi4hQYGKjY2FgFBAS4uhwAAIBsLT7+6tBwknT+vOTnl/XbzC557Y7qowwAAADcLgRlAAAAwAJBGQAAALBAUAYAAAAsEJQBAAAACwRlAAAAwAJBGQAAALBAUAYAAAAsEJQBAAAACwRlAAAAwAJBGQAAALBAUAYAAAAsEJQBAAAACwRlAAAAwAJBGQAAALBAUAYAAAAsEJQBAAAACwRlAAAAwAJBGQAAALBAUAYAAAAsEJQBAAAACwRlAAAAwAJBGQAAALBAUAYAAAAsEJQBAAAACwRlAAAAwAJBGQAAALBAUAYAAAAsEJQBAAAACwRlAAAAwAJBGQAAALBAUAYAAAAsEJQBAAAACwRlAAAAwAJBGQAAALBAUAYAAAAsEJQBAAAACwRlAAAAwAJBGQAAALBAUAYAAAAsEJQBAAAACwRlAAAAwAJBGQAAALBAUAYAAAAsEJQBAAAACwRlAAAAwILLg/KkSZNUokQJeXt7q06dOlq9enWa816+fFkjR45U6dKl5e3trdDQUC1cuPA2VgsAAIB7hUuD8owZMxQZGalhw4Zp3bp1Cg0NVVhYmI4fP245/+DBg/Xpp5/qww8/1NatW9WrVy898cQTWr9+/W2uHAAAAHc7mzHGuGrjderUUa1atfTRRx9JkpKTkxUcHKx+/fpp4MCBqeYvXLiw3njjDfXp08fe1qZNG/n4+Oibb77J0Dbj4uIUGBio2NhYBQQEZM4LAQAAuEvFx0v+/ld/Pn9e8vPL+m1ml7zmsjPKiYmJWrt2rRo3bvy/Ytzc1LhxY0VHR1suk5CQIG9vb4c2Hx8frVixIs3tJCQkKC4uzuEBAAAA3IjLgvLJkyeVlJSkoKAgh/agoCAdO3bMcpmwsDCNHz9eu3btUnJyspYsWaKZM2fq6NGjaW5n1KhRCgwMtD+Cg4Mz9XUAAADg7uTyi/mc8cEHH6hs2bKqUKGCPD091bdvX3Xt2lVubmm/jEGDBik2Ntb+OHTo0G2sGAAAAHcqlwXlfPnyyd3dXTExMQ7tMTExKliwoOUy+fPn1+zZsxUfH68DBw5o+/bt8vf3V6lSpdLcjpeXlwICAhweAAAAwI24LCh7enqqRo0aioqKsrclJycrKipKdevWTXdZb29vFSlSRFeuXNFPP/2kli1bZnW5AAAAuMfkcOXGIyMjFRERoZo1a6p27dqaMGGC4uPj1bVrV0lS586dVaRIEY0aNUqS9Ndff+nw4cOqWrWqDh8+rOHDhys5OVmvvfaaK18GAAAA7kIuDcodOnTQiRMnNHToUB07dkxVq1bVwoUL7Rf4HTx40KH/8aVLlzR48GDt3btX/v7+atq0qb7++mvlypXLRa8AAAAAdyuXjqPsCtllXD4AAIA7AeMoAwAAAHBAUAYAAAAsEJQBAAAACwRlAAAAwAJBGQAAALBAUAYAAAAsEJQBAAAACwRlAAAAwAJBGQAAALBAUAYAAAAsEJQBAAAACwRlAAAAwAJBGQAAALBAUAYAAAAsEJQBAAAACwRlAAAAwAJBGQAAALBAUAYAAAAsEJQBAAAACwRlAAAAwAJBGQAAALBAUAYAAAAsEJQBAAAACwRlAAAAwAJBGQAAALBAUAYAAAAsEJQBAAAACwRlAAAAwAJBGQAAALBAUAYAAAAsEJQBAAAACwRlAAAAwAJBGQAAALBAUAYAAAAsEJQBAAAACwRlAAAAwAJBGQAAALBAUAYAAAAsEJQBAAAACwRlAAAAwAJBGQAAALBAUAYAAAAsEJQBAAAACwRlAAAAwAJBGQAAALBAUAYAAAAsuDwoT5o0SSVKlJC3t7fq1Kmj1atXpzv/hAkTVL58efn4+Cg4OFgvvfSSLl26dJuqBQAAwL3CpUF5xowZioyM1LBhw7Ru3TqFhoYqLCxMx48ft5z/P//5jwYOHKhhw4Zp27Ztmjp1qmbMmKHXX3/9NlcOAACAu51Lg/L48ePVs2dPde3aVZUqVdLkyZPl6+uradOmWc6/atUq1atXT506dVKJEiXUpEkTdezY8YZnoQEAAABnuSwoJyYmau3atWrcuPH/inFzU+PGjRUdHW25zAMPPKC1a9fag/HevXs1f/58NW3a9LbUDAAAgHtHDldt+OTJk0pKSlJQUJBDe1BQkLZv3265TKdOnXTy5EnVr19fxhhduXJFvXr1SrfrRUJCghISEuzP4+LiMucFAAAA4K7m8ov5nLFs2TK98847+vjjj7Vu3TrNnDlT8+bN05tvvpnmMqNGjVJgYKD9ERwcfBsrBgAAwJ3KZWeU8+XLJ3d3d8XExDi0x8TEqGDBgpbLDBkyRM8884x69OghSQoJCVF8fLyeffZZvfHGG3JzS537Bw0apMjISPvzuLg4wjIAAABuyGVnlD09PVWjRg1FRUXZ25KTkxUVFaW6detaLnPhwoVUYdjd3V2SZIyxXMbLy0sBAQEODwAAAOBGXHZGWZIiIyMVERGhmjVrqnbt2powYYLi4+PVtWtXSVLnzp1VpEgRjRo1SpIUHh6u8ePHq1q1aqpTp452796tIUOGKDw83B6YAQAAgMzg0qDcoUMHnThxQkOHDtWxY8dUtWpVLVy40H6B38GDBx3OIA8ePFg2m02DBw/W4cOHlT9/foWHh+vtt9921UsAAADAXcpm0uqzcJeKi4tTYGCgYmNj6YYBAABwA/Hxkr//1Z/Pn5f8/LJ+m9klr91Ro14AAAAAtwtBGQAAALBAUAYAAAAsOH0xX1JSkt5//319//33OnjwoBITEx2mnz59OtOKAwAAAFzF6TPKI0aM0Pjx49WhQwfFxsYqMjJSrVu3lpubm4YPH54FJQIAAAC3n9NB+dtvv9WUKVP08ssvK0eOHOrYsaM+//xzDR06VH/++WdW1AgAAADcdk4H5WPHjikkJESS5O/vr9jYWElS8+bNNW/evMytDgAAAHARp4Ny0aJFdfToUUlS6dKltXjxYknSmjVr5OXllbnVAQAAAC7idFB+4oknFBUVJUnq16+fhgwZorJly6pz587q1q1bphcIAAAAuMIt35kvOjpa0dHRKlu2rMLDwzOrriyTXe70AgAAcCe4l+/M5/TwcNerW7eu6tatmxm1AAAAANlGhoLynDlzMrzCFi1a3HQxAAAAQHaRoaDcqlUrh+c2m03X99iw2WySrt6QBAAAALjTZehivuTkZPtj8eLFqlq1qhYsWKCzZ8/q7NmzWrBggapXr66FCxdmdb0AAADAbeF0H+UXX3xRkydPVv369e1tYWFh8vX11bPPPqtt27ZlaoEAAACAKzg9PNyePXuUK1euVO2BgYHav39/JpQEAAAAuJ7TQblWrVqKjIxUTEyMvS0mJkavvvqqateunanFAQAAAK7idFCeNm2ajh49qmLFiqlMmTIqU6aMihUrpsOHD2vq1KlZUSMAAABw2zndR7lMmTLatGmTlixZou3bt0uSKlasqMaNG9tHvgAAAADudDd1wxGbzaYmTZqoSZMmmV0PAAAAkC3cVFCOj4/X8uXLdfDgQSUmJjpMe+GFFzKlMAAAAMCVnA7K69evV9OmTXXhwgXFx8crT548OnnypHx9fVWgQAGCMgAAAO4KTgfll156SeHh4Zo8ebICAwP1559/ysPDQ08//bT69++fFTUCAJDtGCNduODqKoCsFx/v6gpcx+mgvGHDBn366adyc3OTu7u7EhISVKpUKY0ZM0YRERFq3bp1VtQJAEC2YYxUv760apWrKwGQlZweHs7Dw0NublcXK1CggA4ePCjp6g1HDh06lLnVAQCQDV24QEjGvadePcnX19VV3F5On1GuVq2a1qxZo7Jly+rhhx/W0KFDdfLkSX399deqXLlyVtQIAEC2FRMj+fm5ugog6/n6SvfaSMBOB+V33nlH586dkyS9/fbb6ty5s55//nmVLVtW06ZNy/QCAQDIzvz8CMrA3crpoFyzZk37zwUKFNDChQsztSAAAAAgO3C6jzIAAABwL8jQGeVq1apl+PbU69atu6WCAAAAgOwgQ0G5VatWWVwGAAAAkL1kKCgPGzYsq+sAAAAAshX6KAMAAAAWnB71ws3NLd3+yklJSbdUEAAAAJAdOB2UZ82a5fD88uXLWr9+vb788kuNGDEi0woDAAAAXMnpoNyyZctUbW3bttV9992nGTNmqHv37plSGAAAAOBKmdZH+f7771dUVFRmrQ4AAABwqUwJyhcvXtTEiRNVpEiRzFgdAAAA4HJOd73InTu3w8V8xhidO3dOvr6++uabbzK1OAAAAMBVnA7KEyZMcHju5uam/Pnzq06dOsqdO3dm1QUAAAC4lNNBOSIiIivqAAAAALIVp4OyJF26dEmbNm3S8ePHlZyc7DCtRYsWmVIYAAAA4EpOB+WFCxfqmWee0alTp1JNs9ls3HAEAAAAdwWnR73o16+f2rdvr6NHjyo5OdnhQUgGAADA3cLpoBwTE6PIyEgFBQVlRT0AAABAtuB0UG7btq2WLVuWBaUAAAAA2YfTfZQ/+ugjtWvXTn/88YdCQkLk4eHhMP2FF15wuohJkyZp7NixOnbsmEJDQ/Xhhx+qdu3alvM2aNBAy5cvT9XetGlTzZs3z+ltAwAAAFacDsr//e9/tXjxYnl7e2vZsmUONx+x2WxOB+UZM2YoMjJSkydPVp06dTRhwgSFhYVpx44dKlCgQKr5Z86cqcTERPvzU6dOKTQ0VO3atXP2pQAAAABpshljjDMLFCxYUC+88IIGDhwoN7dbvwN2nTp1VKtWLX300UeSpOTkZAUHB6tfv34aOHDgDZefMGGChg4dqqNHj8rPz++G88fFxSkwMFCxsbEKCAi45foBAPee+HjJ3//qz+fPSxn4+gHghOyS15xOuomJierQoUOmhOTExEStXbtWjRs3/l9Bbm5q3LixoqOjM7SOqVOn6sknn8xQSAYAAAAyyum0GxERoRkzZmTKxk+ePKmkpKRUI2gEBQXp2LFjN1x+9erV2rJli3r06JHmPAkJCYqLi3N4AAAAADfidB/lpKQkjRkzRosWLVKVKlVSXcw3fvz4TCvuRqZOnaqQkJA0L/yTpFGjRmnEiBG3rSYAAADcHZwOyps3b1a1atUkSVu2bHGYdu2FfRmRL18+ubu7KyYmxqE9JiZGBQsWTHfZ+Ph4fffddxo5cmS68w0aNEiRkZH253FxcQoODnaqTgAAANx7nA7KS5cuzbSNe3p6qkaNGoqKilKrVq0kXb2YLyoqSn379k132R9++EEJCQl6+umn053Py8tLXl5emVUyAAAA7hFOB+XMFhkZqYiICNWsWVO1a9fWhAkTFB8fr65du0qSOnfurCJFimjUqFEOy02dOlWtWrVS3rx5XVE2AAAA7nJOB+WGDRum28Xit99+c2p9HTp00IkTJzR06FAdO3ZMVatW1cKFC+0X+B08eDDVCBs7duzQihUrtHjxYmfLBwAAADLE6aBctWpVh+eXL1/Whg0btGXLFkVERNxUEX379k2zq4XV7bLLly8vJ4d/BgAAAJzidFB+//33LduHDx+u8+fP33JBAAAAQHZw63cN+X9PP/20pk2bllmrAwAAAFwq04JydHS0vL29M2t1AAAAgEs53fWidevWDs+NMTp69Kj+/vtvDRkyJNMKAwAAAFzJ6aAcGBjo8NzNzU3ly5fXyJEj1aRJk0wrDAAAAHAlp4Py9OnTs6IOAAAAIFtxuo/ymjVr9Ndff6Vq/+uvv/T3339nSlEAAACAqzkdlPv06aNDhw6laj98+LD69OmTKUUBAAAAruZ0UN66dauqV6+eqr1atWraunVrphQFAAAAuJrTQdnLy0sxMTGp2o8ePaocOZzu8gwAAABkS04H5SZNmmjQoEGKjY21t509e1avv/66Hn300UwtDgAAAHAVp08Bv/fee3rooYdUvHhxVatWTZK0YcMGBQUF6euvv870AgEAAABXcDooFylSRJs2bdK3336rjRs3ysfHR127dlXHjh3l4eGRFTUCAAAAt91NdSr28/PTs88+69C2bds2TZ06Ve+9916mFAYAAAC4ktN9lK8VHx+vqVOn6oEHHtB9992nhQsXZlZdAAAAgEvdVFBeuXKlunXrpqCgID377LN64IEHtHXrVm3ZsiWz6wMAAABcIsNB+fjx4xozZowqVKigtm3bKleuXFq2bJnc3NzUrVs3VahQISvrBAAAAG6rDPdRLl68uNq2basPPvhAjz76qNzcbqnXBgAAAJCtZTjtFi9eXCtWrNDvv/+unTt3ZmVNAAAAgMtlOChv375d33zzjY4ePapatWqpRo0aev/99yVJNpstywoEAAAAXMGp/hP16tXTtGnTdPToUfXq1Us//PCDkpKS1Lt3b02ZMkUnTpzIqjoBAACA28pmjDG3soKU8ZO//vprnT59WpcvX86s2rJEXFycAgMDFRsbq4CAAFeXAwC4A8XHS/7+V38+f17y83NtPcDdJrvktVu+Iq9ixYp67733dPjwYc2YMSMzagIAAABcLtOGrsiRI4dat26dWasDAAAAXIox3gAAAAALBGUAAADAAkEZAAAAsHDTQXn37t1atGiRLl68KEm6xcEzAAAAgGzF6aB86tQpNW7cWOXKlVPTpk119OhRSVL37t318ssvZ3qBAAAAgCs4HZRfeukl5ciRQwcPHpSvr6+9vUOHDlq4cGGmFgcAAAC4Sg5nF1i8eLEWLVqkokWLOrSXLVtWBw4cyLTCAAAAAFdy+oxyfHy8w5nkFKdPn5aXl1emFAUAAAC4mtNB+cEHH9RXX31lf26z2ZScnKwxY8aoYcOGmVocAAAA4CpOd70YM2aMGjVqpL///luJiYl67bXX9M8//+j06dNauXJlVtQIAAAA3HZOn1GuXLmydu7cqfr166tly5aKj49X69attX79epUuXToragQAAABuO6fPKEtSYGCg3njjjcyuBQAAAMg2MhSUN23alOEVVqlS5aaLAQAAALKLDAXlqlWrymazyRgjm81mb0+5G9+1bUlJSZlcIgAAAHD7ZaiP8r59+7R3717t27dPP/30k0qWLKmPP/5YGzZs0IYNG/Txxx+rdOnS+umnn7K6XgAAAOC2yNAZ5eLFi9t/bteunSZOnKimTZva26pUqaLg4GANGTJErVq1yvQiAQAAgNvN6VEvNm/erJIlS6ZqL1mypLZu3ZopRQEAAACu5nRQrlixokaNGqXExER7W2JiokaNGqWKFStmanEAAACAqzg9PNzkyZMVHh6uokWL2ke42LRpk2w2m+bOnZvpBQIAAACu4HRQrl27tvbu3atvv/1W27dvlyR16NBBnTp1kp+fX6YXCAAAALjCTd1wxM/PT88++2xm1wIAAABkGzcVlCVp69atOnjwoENfZUlq0aLFLRcFAAAAuJrTQXnv3r164okntHnzZvtNSKT/3XSEG44AAADgbuD0qBf9+/dXyZIldfz4cfn6+uqff/7R77//rpo1a2rZsmVOFzBp0iSVKFFC3t7eqlOnjlavXp3u/GfPnlWfPn1UqFAheXl5qVy5cpo/f77T2wUAAADS4/QZ5ejoaP3222/Kly+f3Nzc5Obmpvr162vUqFF64YUXtH79+gyva8aMGYqMjNTkyZNVp04dTZgwQWFhYdqxY4cKFCiQav7ExEQ9+uijKlCggH788UcVKVJEBw4cUK5cuZx9GQAAAEC6nA7KSUlJypkzpyQpX758OnLkiMqXL6/ixYtrx44dTq1r/Pjx6tmzp7p27Srp6tBz8+bN07Rp0zRw4MBU80+bNk2nT5/WqlWr5OHhIUkqUaKEsy8BAAAAuCGnu15UrlxZGzdulCTVqVNHY8aM0cqVKzVy5EiVKlUqw+tJTEzU2rVr1bhx4/8V4+amxo0bKzo62nKZOXPmqG7duurTp4+CgoJUuXJlvfPOO+n2i05ISFBcXJzDAwAAALgRp4Py4MGDlZycLEkaOXKk9u3bpwcffFDz58/XxIkTM7yekydPKikpSUFBQQ7tQUFBOnbsmOUye/fu1Y8//qikpCTNnz9fQ4YM0bhx4/TWW2+luZ1Ro0YpMDDQ/ggODs5wjQAAALh3Od31IiwszP5zmTJltH37dp0+fVq5c+e2j3yRVZKTk1WgQAF99tlncnd3V40aNXT48GGNHTtWw4YNs1xm0KBBioyMtD+Pi4sjLAMAAOCGbnoc5WvlyZPH6WXy5csnd3d3xcTEOLTHxMSoYMGClssUKlRIHh4ecnd3t7dVrFhRx44dU2Jiojw9PVMt4+XlJS8vL6frAwAAwL0tQ0G5devWGV7hzJkzMzSfp6enatSooaioKLVq1UrS1TPGUVFR6tu3r+Uy9erV03/+8x8lJyfLze1qr5GdO3eqUKFCliEZAAAAuFkZ6qN8bR/fgIAARUVF6e+//7ZPX7t2raKiohQYGOjUxiMjIzVlyhR9+eWX2rZtm55//nnFx8fbR8Ho3LmzBg0aZJ//+eef1+nTp9W/f3/t3LlT8+bN0zvvvKM+ffo4tV0AAADgRjJ0Rnn69On2nwcMGKD27dtr8uTJ9i4QSUlJ6t27twICApzaeIcOHXTixAkNHTpUx44dU9WqVbVw4UL7BX4HDx60nzmWpODgYC1atEgvvfSSqlSpoiJFiqh///4aMGCAU9sFAAAAbsRmUu5BnUH58+fXihUrVL58eYf2HTt26IEHHtCpU6cytcDMFhcXp8DAQMXGxjod7AEAkKT4eMnf/+rP589Lfn6urQe422SXvOb08HBXrlzR9u3bU7Vv377dPmwcAAAAcKdzetSLrl27qnv37tqzZ49q164tSfrrr780evRoe99iAAAA4E7ndFB+7733VLBgQY0bN05Hjx6VdHXYtldffVUvv/xyphcIAAAAuILTfZSvlXI76Dupr2926fMCALhz0UcZyFrZJa/d0g1HCJoAAAC4W2UoKFevXl1RUVHKnTu3qlWrlu6tqtetW5dpxQEAAACukqGg3LJlS/ttoFPuogcAAADczW6pj/KdKLv0eQEA3LnoowxkreyS15weRxkAAAC4F2So60Xu3LnT7Zd8rdOnT99SQQAAAEB2kKGgPGHChCwuAwAAAMheMhSUIyIisroOAAAAIFvJUFCOi4uzd6ROuclIWrhADgAAAHeDDPdRPnr0qAoUKKBcuXJZ9lc2xshmsykpKSnTiwQAAAButwwF5d9++0158uSRJC1dujRLCwIAAACygwwF5YcfftjyZwAAAOBulaGgfL0zZ85o6tSp2rZtmySpUqVK6tq1q/2sMwAAAHCnc/qGI7///rtKlCihiRMn6syZMzpz5owmTpyokiVL6vfff8+KGgEAAIDbzukzyn369FGHDh30ySefyN3dXZKUlJSk3r17q0+fPtq8eXOmFwkAAADcbk6fUd69e7defvlle0iWJHd3d0VGRmr37t2ZWhwAAADgKk4H5erVq9v7Jl9r27ZtCg0NzZSiAAAAAFfLUNeLTZs22X9+4YUX1L9/f+3evVv333+/JOnPP//UpEmTNHr06KypEgAAALjNbMYYc6OZ3NzcZLPZdKNZ74QbjsTFxSkwMFCxsbHcRRAAcFPi4yV//6s/nz8v+fm5th7gbpNd8lqGzijv27cvq+sAAAAAspUMBeXixYtndR0AAABAtnJTNxyRpK1bt+rgwYNKTEx0aG/RosUtFwUAAAC4mtNBee/evXriiSe0efNmh37LNptNkrJ9H2UAAAAgI5weHq5///4qWbKkjh8/Ll9fX/3zzz/6/fffVbNmTS1btiwLSgQAAABuP6fPKEdHR+u3335Tvnz55ObmJjc3N9WvX1+jRo3SCy+8oPXr12dFnQAAAMBt5fQZ5aSkJOXMmVOSlC9fPh05ckTS1Qv+duzYkbnVAQAAAC7i9BnlypUra+PGjSpZsqTq1KmjMWPGyNPTU5999plKlSqVFTUCAAAAt53TQXnw4MGKj4+XJI0cOVLNmzfXgw8+qLx582rGjBmZXiAAAADgCk4H5bCwMPvPZcqU0fbt23X69Gnlzp3bPvIFAAAAcKe76XGUJenQoUOSpODg4EwpBgAAAMgunL6Y78qVKxoyZIgCAwNVokQJlShRQoGBgRo8eLAuX76cFTUCAAAAt53TZ5T79eunmTNnasyYMapbt66kq0PGDR8+XKdOndInn3yS6UUCAAAAt5vNpNxaL4MCAwP13Xff6fHHH3donz9/vjp27KjY2NhMLTCzxcXFKTAwULGxsQoICHB1OQCAO1B8vOTvf/Xn8+clPz/X1gPcbbJLXnO664WXl5dKlCiRqr1kyZLy9PTMjJoAAAAAl3M6KPft21dvvvmmEhIS7G0JCQl6++231bdv30wtDgAAAHCVDPVRbt26tcPzX3/9VUWLFlVoaKgkaePGjUpMTFSjRo0yv0IAAADABTIUlAMDAx2et2nTxuE5w8MBAADgbpOhoDx9+vSsrgMAAADIVm76hiMnTpzQjh07JEnly5dX/vz5M60oAAAAwNWcvpgvPj5e3bp1U6FChfTQQw/poYceUuHChdW9e3dduHAhK2oEAAAAbjung3JkZKSWL1+uuXPn6uzZszp79qx+/vlnLV++XC+//HJW1AgAAADcdk7fcCRfvnz68ccf1aBBA4f2pUuXqn379jpx4kRm1pfpsssA1gCAOxc3HAGyVnbJa073Ub5w4YKCgoJStRcoUOCmu15MmjRJY8eO1bFjxxQaGqoPP/xQtWvXtpz3iy++UNeuXR3avLy8dOnSpZvaNoDMY4xEDyzcC+LjXV0BgNvB6aBct25dDRs2TF999ZW8vb0lSRcvXtSIESNUt25dpwuYMWOGIiMjNXnyZNWpU0cTJkxQWFiYduzYoQIFClguExAQYL+QUJJsNpvT2wWQuYyR6teXVq1ydSUAAGQOp4PyhAkT9Nhjj6W64Yi3t7cWLVrkdAHjx49Xz5497WeJJ0+erHnz5mnatGkaOHCg5TI2m00FCxZ0elsAss6FC4Rk3Hvq1ZN8fV1dBYCs4nRQDgkJ0a5du/Ttt99q+/btkqSOHTvqqaeeko+Pj1PrSkxM1Nq1azVo0CB7m5ubmxo3bqzo6Og0lzt//ryKFy+u5ORkVa9eXe+8847uu+8+y3kTEhIcbrcdFxfnVI0AnBcTQ59N3Bt8fSX+qQncvZwKypcvX1aFChX0yy+/qGfPnre88ZMnTyopKSlVn+egoCB7CL9e+fLlNW3aNFWpUkWxsbF677339MADD+iff/5R0aJFU80/atQojRgx4pZrBZBxfn4EZQDAnc+p4eE8PDxcftFc3bp11blzZ1WtWlUPP/ywZs6cqfz58+vTTz+1nH/QoEGKjY21Pw4dOnSbKwYAAMCdyOlxlPv06aN3331XV65cueWN58uXT+7u7oqJiXFoj4mJyXAfZA8PD1WrVk27d++2nO7l5aWAgACHBwAAAHAjTvdRXrNmjaKiorR48WKFhITI77r/r86cOTPD6/L09FSNGjUUFRWlVq1aSZKSk5MVFRWlvn37ZmgdSUlJ2rx5s5o2bZrh7QIAAAA34nRQzpUrl9q0aZNpBURGRioiIkI1a9ZU7dq1NWHCBMXHx9tHwejcubOKFCmiUaNGSZJGjhyp+++/X2XKlNHZs2c1duxYHThwQD169Mi0mgAAAACng/L06dMztYAOHTroxIkTGjp0qI4dO6aqVatq4cKF9gv8Dh48KDe3//UQOXPmjHr27Kljx44pd+7cqlGjhlatWqVKlSplal0AAAC4t2X4FtbJyckaO3as5syZo8TERDVq1EjDhg1zekg4V8sut0QE7jbc0hcAkFmyS17L8MV8b7/9tl5//XX5+/urSJEi+uCDD9SnT5+srA0AAABwmQwH5a+++koff/yxFi1apNmzZ2vu3Ln69ttvlZycnJX1AQAAAC6R4aB88OBBh5ElGjduLJvNpiNHjmRJYQAAAIArZTgoX7lyRd7e3g5tHh4eunz5cqYXBQAAALhahke9MMaoS5cu8vLysrddunRJvXr1chhL2ZlxlAEAAIDsKsNBOSIiIlXb008/nanFAAAAANlFhoNyZo+fDAAAAGRnGe6jDAAAANxLCMoAAACABYIyAAAAYIGgDAAAAFggKAMAAAAWCMoAAACABYIyAAAAYIGgDAAAAFggKAMAAAAWCMoAAACABYIyAAAAYIGgDAAAAFggKAMAAAAWCMoAAACABYIyAAAAYIGgDAAAAFggKAMAAAAWCMoAAACABYIyAAAAYIGgDAAAAFggKAMAAAAWCMoAAACABYIyAAAAYIGgDAAAAFggKAMAAAAWCMoAAACABYIyAAAAYIGgDAAAAFggKAMAAAAWCMoAAACABYIyAAAAYIGgDAAAAFggKAMAAAAWCMoAAACABYIyAAAAYIGgDAAAAFggKAMAAAAWCMoAAACABYIyAAAAYCFbBOVJkyapRIkS8vb2Vp06dbR69eoMLffdd9/JZrOpVatWWVsgAAAA7jkuD8ozZsxQZGSkhg0bpnXr1ik0NFRhYWE6fvx4usvt379fr7zyih588MHbVCkAAADuJS4PyuPHj1fPnj3VtWtXVapUSZMnT5avr6+mTZuW5jJJSUl66qmnNGLECJUqVeo2VgsAAIB7hUuDcmJiotauXavGjRvb29zc3NS4cWNFR0enudzIkSNVoEABde/e/XaUCQAAgHtQDldu/OTJk0pKSlJQUJBDe1BQkLZv3265zIoVKzR16lRt2LAhQ9tISEhQQkKC/XlcXNxN1wsAAIB7h8u7Xjjj3LlzeuaZZzRlyhTly5cvQ8uMGjVKgYGB9kdwcHAWVwkAAIC7gUvPKOfLl0/u7u6KiYlxaI+JiVHBggVTzb9nzx7t379f4eHh9rbk5GRJUo4cObRjxw6VLl3aYZlBgwYpMjLS/jwuLo6wDAAAgBtyaVD29PRUjRo1FBUVZR/iLTk5WVFRUerbt2+q+StUqKDNmzc7tA0ePFjnzp3TBx98YBmAvby85OXllSX1AwAA4O7l0qAsSZGRkYqIiFDNmjVVu3ZtTZgwQfHx8erataskqXPnzipSpIhGjRolb29vVa5c2WH5XLlySVKqdgAAAOBWuDwod+jQQSdOnNDQoUN17NgxVa1aVQsXLrRf4Hfw4EG5ud1RXakBAABwF7AZY4yri7id4uLiFBgYqNjYWAUEBLi6HOCuER8v+ftf/fn8ecnPz7X1AADuXNklr3GqFgAAALBAUAYAAAAsEJQBAAAACwRlAAAAwAJBGQAAALBAUAYAAAAsEJQBAAAACwRlAAAAwAJBGQAAALBAUAYAAAAsEJQBAAAACwRlAAAAwAJBGQAAALBAUAYAAAAsEJQBAAAACwRlAAAAwAJBGQAAALBAUAYAAAAsEJQBAAAACwRlAAAAwAJBGQAAALBAUAYAAAAsEJQBAAAACwRlAAAAwAJBGQAAALBAUAYAAAAsEJQBAAAACwRlAAAAwAJBGQAAALBAUAYAAAAsEJQBAAAACwRlAAAAwAJBGQAAALBAUAYAAAAsEJQBAAAACwRlAAAAwAJBGQAAALBAUAYAAAAsEJQBAAAACwRlAAAAwAJBGQAAALBAUAYAAAAsEJQBAAAACwRlAAAAwAJBGQAAALBAUAYAAAAsZIugPGnSJJUoUULe3t6qU6eOVq9enea8M2fOVM2aNZUrVy75+fmpatWq+vrrr29jtQAAALgXuDwoz5gxQ5GRkRo2bJjWrVun0NBQhYWF6fjx45bz58mTR2+88Yaio6O1adMmde3aVV27dtWiRYtuc+UAAAC4m9mMMcaVBdSpU0e1atXSRx99JElKTk5WcHCw+vXrp4EDB2ZoHdWrV1ezZs305ptv3nDeuLg4BQYGKjY2VgEBAbdUO4D/iY+X/P2v/nz+vOTn59p6AAB3ruyS11x6RjkxMVFr165V48aN7W1ubm5q3LixoqOjb7i8MUZRUVHasWOHHnrooawsFQAAAPeYHK7c+MmTJ5WUlKSgoCCH9qCgIG3fvj3N5WJjY1WkSBElJCTI3d1dH3/8sR599FHLeRMSEpSQkGB/HhcXlznFAwAA4K7m0qB8s3LmzKkNGzbo/PnzioqKUmRkpEqVKqUGDRqkmnfUqFEaMWLE7S8SAAAAdzSXBuV8+fLJ3d1dMTExDu0xMTEqWLBgmsu5ubmpTJkykqSqVatq27ZtGjVqlGVQHjRokCIjI+3P4+LiFBwcnDkvAAAAAHctl/ZR9vT0VI0aNRQVFWVvS05OVlRUlOrWrZvh9SQnJzt0r7iWl5eXAgICHB4AAADAjbi860VkZKQiIiJUs2ZN1a5dWxMmTFB8fLy6du0qSercubOKFCmiUaNGSbralaJmzZoqXbq0EhISNH/+fH399df65JNPXPkyAAAAcJdxeVDu0KGDTpw4oaFDh+rYsWOqWrWqFi5caL/A7+DBg3Jz+9+J7/j4ePXu3Vv//vuvfHx8VKFCBX3zzTfq0KGDq14CAAAA7kIuH0f5dssu4/IBdxvGUQYAZJbsktdcfmc+AAAAIDsiKAMAAAAWCMoAAACABYIyAAAAYIGgDAAAAFggKAMAAAAWCMoAAACABYIyAAAAYIGgDAAAAFggKAMAAAAWCMoAAACAhRyuLuBuZox04YKrqwBuj/h4V1cAAEDmIihnoQsXJH9/V1cBAACAm0HXCwCZql49ydfX1VUAAHDrOKOchXx9pfPnXV0FcHv5+ko2m6urAADg1hGUs5DNJvn5uboKAAAA3Ay6XgAAAAAWCMoAAACABYIyAAAAYIGgDAAAAFggKAMAAAAWCMoAAACABYIyAAAAYIGgDAAAAFggKAMAAAAWCMoAAACABYIyAAAAYIGgDAAAAFggKAMAAAAWCMoAAACABYIyAAAAYIGgDAAAAFggKAMAAAAWcri6gNvNGCNJiouLc3ElAAAAsJKS01Jym6vcc0H53LlzkqTg4GAXVwIAAID0nDt3ToGBgS7bvs24OqrfZsnJyTpy5Ihy5swpm82W5duLi4tTcHCwDh06pICAgCzfHuBK7O+4l7C/415yu/d3Y4zOnTunwoULy83NdT2F77kzym5ubipatOht325AQAAHUtwz2N9xL2F/x73kdu7vrjyTnIKL+QAAAAALBGUAAADAAkE5i3l5eWnYsGHy8vJydSlAlmN/x72E/R33knt1f7/nLuYDAAAAMoIzygAAAIAFgjIAAABggaAM4JZ88MEHio6OdnUZAABkOoJyBtlsNs2ePdvVZWSpLl26qFWrVq4uA3eQcePGaebMmapevXq68y1btkw2m01nz56VJH3xxRfKlStX1hcIZDP79++XzWbThg0bXF0KsqGTJ09qxIgROnnypKtLwf+7I4Nyly5dZLPZUj12797t6tKyhQYNGujFF190dRm4g6R8pnr16pVqWp8+fWSz2dSlSxeH9pUrV+rrr7/Wzz//7PRV0B06dNDOnTtvpWQgTSdOnNDzzz+vYsWKycvLSwULFlRYWJhWrlzp6tIUHByso0ePqnLlyq4uBdmMMUbPPPOMjDHKly+fq8vJMFf98ffnn38qb9686tGjh7Zt26ZmzZplyXbuyKAsSY899piOHj3q8ChZsqSrywLuWMHBwfruu+908eJFe9ulS5f0n//8R8WKFUs1f7169bRhw4abOjPs4+OjAgUK3Eq5QJratGmj9evX68svv9TOnTs1Z84cNWjQQKdOnXJpXYmJiXJ3d1fBggWVI8fN3xg3MTExE6vC7XDtCT5PT0+VKVNGI0eO1JUrV+zzvPPOOypYsKCGDx/uukLvIHPmzNG7776rfPnyqWnTpnruueeyZDt3bFBOOUtw7aN79+6pug68+OKLatCggf15gwYN9MILL+i1115Tnjx5LHfKXbt26aGHHpK3t7cqVaqkJUuWpNr+gAEDVK5cOfn6+qpUqVIaMmSILl++bJ8+fPhwVa1aVdOmTVOxYsXk7++v3r17KykpSWPGjFHBggVVoEABvf322w7rPXv2rHr06KH8+fMrICBAjzzyiDZu3JhqvV9//bVKlCihwMBAPfnkkzp37pykqx/G5cuX64MPPrB/KPfv36+kpCR1795dJUuWlI+Pj8qXL68PPvjgJt993I2qV6+u4OBgzZw50942c+ZMFStWTNWqVbO3JScna9SoUfZ9KTQ0VD/++KPDuubPn69y5crJx8dHDRs21P79+x2mW3W9+OSTT1S6dGl5enqqfPny+vrrrzP9NeLud/bsWf3xxx9699131bBhQxUvXly1a9fWoEGD1KJFC8uzX2fPnpXNZtOyZcsk/a+r0Lx581SlShV5e3vr/vvv15YtWxy2tWLFCj344IPy8fFRcHCwXnjhBcXHx9unlyhRQm+++aY6d+6sgIAAPfvss5bbX758uWrXri0vLy8VKlRIAwcOdAhQDRo0UN++ffXiiy8qX758CgsLy5L3Dlkr5QTfrl279PLLL2v48OEaO3asffobb7yh6dOnu6S2O/GPr3feeUc9evTQ6NGjtW/fPrVo0SJLtnPHBuVb8eWXX8rPz09//fWXxowZo5EjR9rDcHJyslq3bi1PT0/99ddfmjx5sgYMGJBqHTlz5tQXX3yhrVu36oMPPtCUKVP0/vvvO8yzZ88eLViwQAsXLtR///tfTZ06Vc2aNdO///6r5cuX691339XgwYP1119/2Zdp166djh8/rgULFmjt2rWqXr26GjVqpNOnTzusd/bs2frll1/0yy+/aPny5Ro9erSkqxdW1a1bVz179rSfaQ8ODlZycrKKFi2qH374QVu3btXQoUP1+uuv6/vvv8+Ktxh3qG7dujkcqKdNm6auXbs6zDNq1Ch99dVXmjx5sv755x+99NJLevrpp7V8+XJJ0qFDh9S6dWuFh4drw4YN6tGjhwYOHJjudmfNmqX+/fvr5Zdf1pYtW/Tcc8+pa9euWrp0aea/SNzV/P395e/vr9mzZyshIeGW1vXqq69q3LhxWrNmjfLnz6/w8HD7CZE9e/boscceU5s2bbRp0ybNmDFDK1asUN++fR3W8d577yk0NFTr16/XkCFDUm3j8OHDatq0qWrVqqWNGzfqk08+0dSpU/XWW285zPfll1/K09NTK1eu1OTJk2/pdcE1Uk7wFS9eXM8//7waN26sOXPm6MyZM+rcubNy584tX19fPf7449q1a5d9uQMHDig8PFy5c+eWn5+f7rvvPs2fPz/dbU2ZMkXBwcHy9fXVE088ofHjxzucnEg56fb555+rZMmS8vb2liQtXLhQ9evXV65cuZQ3b141b95ce/bscVj36tWrVa1aNXl7e6tmzZpav369w3SrEyGzZ8+WzWazP9+zZ49atmypoKAg+fv7q1atWvr1118dlklISNCAAQMUHBwsLy8vlSlTRlOnTpWkDJ38S05O1siRI1W0aFF5eXmpatWqWrhwYbrvmyVzB4qIiDDu7u7Gz8/P/mjbtq2JiIgwLVu2dJi3f//+5uGHH7Y/f/jhh039+vUd5qlVq5YZMGCAMcaYRYsWmRw5cpjDhw/bpy9YsMBIMrNmzUqzprFjx5oaNWrYnw8bNsz4+vqauLg4e1tYWJgpUaKESUpKsreVL1/ejBo1yhhjzB9//GECAgLMpUuXHNZdunRp8+mnn6a53ldffdXUqVPH4TX2798/zVpT9OnTx7Rp08b+3Or9w70h5Xd//Phx4+XlZfbv32/2799vvL29zYkTJ0zLli1NRESEuXTpkvH19TWrVq1yWL579+6mY8eOxhhjBg0aZCpVquQwfcCAAUaSOXPmjDHGmOnTp5vAwED79AceeMD07NnTYZl27dqZpk2bZv6LxV3vxx9/NLlz5zbe3t7mgQceMIMGDTIbN240xhizb98+I8msX7/ePv+ZM2eMJLN06VJjjDFLly41ksx3331nn+fUqVPGx8fHzJgxwxhzdZ9/9tlnHbb7xx9/GDc3N3Px4kVjjDHFixc3rVq1cpjn+u2//vrrpnz58iY5Odk+z6RJk4y/v7/9u+Lhhx821apVu/U3Bi5j9f3aokULU716ddOiRQtTsWJF8/vvv5sNGzaYsLAwU6ZMGZOYmGiMMaZZs2bm0UcfNZs2bTJ79uwxc+fONcuXL09zWytWrDBubm5m7NixZseOHWbSpEkmT548DsfcYcOGGT8/P/PYY4+ZdevW2T8fP/74o/npp5/Mrl27zPr16014eLgJCQmx74vnzp0z+fPnN506dTJbtmwxc+fONaVKlXLYp68/vhtjzKxZs8y1kXPDhg1m8uTJZvPmzWbnzp1m8ODBxtvb2xw4cMA+T/v27U1wcLCZOXOm2bNnj/n111/tn8nExEQzdOhQs2bNGrN3717zzTffGF9fX/vn0xhjxo8fbwICAsx///tfs337dvPaa68ZDw8Ps3Pnzoz90v7fzXeScrGGDRvqk08+sT/38/PToEGDMrRslSpVHJ4XKlRIx48flyRt27ZNwcHBKly4sH163bp1U61jxowZmjhxovbs2aPz58/rypUrCggIcJinRIkSypkzp/15UFCQ3N3d5ebm5tCWsu2NGzfq/Pnzyps3r8N6Ll686PAX3fXrvbb+9EyaNEnTpk3TwYMHdfHiRSUmJqpq1ao3XA73jvz586tZs2b64osvZIxRs2bNHC4q2b17ty5cuKBHH33UYbnExER794xt27apTp06DtOtPkPX2rZtm5599lmHtnr16tE9CDelTZs2atasmf744w/9+eefWrBggcaMGaPPP//coSvejVy73+bJk0fly5fXtm3bJF09Xm/atEnffvutfR5jjJKTk7Vv3z5VrFhRklSzZs10t7Ft2zbVrVvX4WxbvXr1dP78ef3777/26wNq1KiR4bqRvRljFBUVpUWLFunxxx/X7NmztXLlSj3wwAOSpG+//VbBwcGaPXu22rVrp4MHD6pNmzYKCQmRJJUqVSrd9X/44Yd6/PHH9corr0iSypUrp1WrVumXX35xmC8xMVFfffWV8ufPb29r06aNwzzTpk1T/vz5tXXrVlWuXFn/+c9/lJycrKlTp8rb21v33Xef/v33Xz3//PNOvQehoaEKDQ21P3/zzTc1a9YszZkzR3379tXOnTv1/fffa8mSJWrcuHGq1+3h4aERI0bYn5csWVLR0dH6/vvv1b59e0lX/5szYMAAPfnkk5Kkd999V0uXLtWECRM0adKkDNd6xwZlPz8/lSlTxqHNzc1N5ro7cl/bbziFh4eHw3Obzabk5OQMbzs6OlpPPfWURowYobCwMAUGBuq7777TuHHjbrid9LZ9/vx5FSpUyN5P7lrX/hvjZur/7rvv9Morr2jcuHGqW7eucubMqbFjxzp0+wCkq90vUv59fP3B5Pz585KkefPmqUiRIg7TnB35AshK3t7eevTRR/Xoo49qyJAh6tGjh4YNG6Y//vhDkhy+K6y+J27k/Pnzeu655/TCCy+kmnbtxa9+fn43UX1qmbUeuM4vv/wif39/Xb58WcnJyerUqZNat26tX375xeHkQt68eR3+KHvhhRf0/PPPa/HixWrcuLHatGljP+F333336cCBA5KkBx98UAsWLNCOHTv0xBNPOGy7du3aqYJy8eLFHUKydPUaraFDh+qvv/7SyZMn7dni4MGDqly5srZt22bvt5/iRidCrJw/f17Dhw/XvHnzdPToUV25ckUXL17UwYMHJUkbNmyQu7u7Hn744TTXkd7Jv7i4OB05ckT16tVzWKZevXoO131lxB0blK3kz58/1cUWGzZsSBUs01OxYkUdOnRIR48eVaFChSRdHYLkWqtWrVLx4sX1xhtv2NtSdtRbUb16dR07dkw5cuRQiRIlbno9np6eSkpKcmhL+Wu1d+/e9rbr+x0B0tULThITE2Wz2VJdNFSpUiV5eXnp4MGDaR7AKlasqDlz5ji0Xf8Zslpm5cqVioiIsLetXLlSlSpVuslXATiqVKmSZs+ebQ8GR48etf8XJK1hrf7880976D1z5ox27txpP1NcvXp1bd26NdUJG2dVrFhRP/30k4wx9rPKK1euVM6cOVW0aNFbWjeyl5T/hHt6eqpw4cLKkSNHqmOllR49eigsLEzz5s3T4sWLNWrUKI0bN079+vXT/Pnz7X/o+fj4OFWP1R9f4eHhKl68uKZMmaLChQsrOTlZlStXdupiv4yctHzllVe0ZMkSvffeeypTpox8fHzUtm1b+3Zu9Fpu58m/u+pivkceeUR///23vvrqK+3atUvDhg1LFZxvpHHjxipXrpwiIiK0ceNG/fHHHw6BWJLKli2rgwcP6rvvvtOePXs0ceJEzZo165brb9y4serWratWrVpp8eLF2r9/v1atWqU33nhDf//9d4bXU6JECf3111/av3+//S/CsmXL6u+//9aiRYu0c+dODRkyRGvWrLnlmnH3cXd317Zt27R161a5u7s7TMuZM6deeeUVvfTSS/ryyy+1Z88erVu3Th9++KG+/PJLSVKvXr20a9cuvfrqq9qxY4f+85//6Isvvkh3m6+++qq++OILffLJJ9q1a5fGjx+vmTNn2v91CGTUqVOn9Mgjj+ibb77Rpk2btG/fPv3www8aM2aMWrZsKR8fH91///0aPXq0tm3bpuXLl2vw4MGW6xo5cqSioqK0ZcsWdenSRfny5bOPrDRgwACtWrVKffv21YYNG7Rr1y79/PPPqS7mu5HevXvr0KFD6tevn7Zv366ff/5Zw4YNU2RkpEM3Pdz5Uv4TXqxYMfvwgBUrVtSVK1ccAt6pU6e0Y8cOhxMFwcHB6tWrl2bOnKmXX35ZU6ZMkXT1rHCZMmVUpkwZ+3/5ypcvn+r7PSPf9ynbHTx4sBo1aqSKFSvqzJkzDvNUrFhRmzZt0qVLl+xt158IyZ8/v86dO+cwAsz1f4yuXLlSXbp00RNPPKGQkBAVLFjQYXSkkJAQJScn2y8Sv961J/+qVaumMmXKOJz8CwgIUOHChVONnX4zJ2Duqk9hWFiYhgwZotdee021atXSuXPn1LlzZ6fW4ebmplmzZunixYuqXbu2evTokWoItxYtWuill15S3759VbVqVa1atcryamZn2Ww2zZ8/Xw899JC6du2qcuXK6cknn9SBAwcUFBSU4fW88sorcnd3V6VKlZQ/f34dPHhQzz33nFq3bq0OHTqoTp06OnXqlMPZZeBaAQEBqfrcp3jzzTc1ZMgQjRo1ShUrVtRjjz2mefPm2ccxL1asmH766SfNnj1boaGhmjx5st555510t9eqVSt98MEHeu+993Tffffp008/1fTp053qTwpIV0e9qFOnjt5//3099NBDqly5soYMGaKePXvqo48+knS13+WVK1dUo0YNvfjii6lGmEgxevRo9e/fXzVq1NCxY8c0d+5ceXp6Srp6rcvy5cu1c+dOPfjgg6pWrZqGDh3qcH1LRhQpUkTz58/X6tWrFRoaql69eql79+5phnfcXcqWLauWLVuqZ8+eWrFihTZu3Kinn35aRYoUUcuWLSVdHeZ20aJF2rdvn9atW6elS5fa/7NhJeVM8/jx47Vr1y59+umnWrBggUM/eCu5c+dW3rx59dlnn2n37t367bffFBkZ6TBPp06dZLPZ1LNnT23dulXz58/Xe++95zBPnTp15Ovrq9dff1179uyxPFlStmxZzZw5Uxs2bNDGjRvVqVMnhy6kJUqUUEREhLp166bZs2dr3759WrZsmX2kroyc/Hv11Vf17rvvasaMGdqxY4cGDhyoDRs2qH///um+D6k4dekfAAB3uZRRL1JGaQFuVXqjSp0+fdo888wzJjAw0Pj4+JiwsDCHkRn69u1rSpcubby8vEz+/PnNM888Y06ePJnu9j777DNTpEgR4+PjY1q1amXeeustU7BgQfv0YcOGmdDQ0FTLLVmyxFSsWNF4eXmZKlWqmGXLlqUa9Ss6OtqEhoYaT09PU7VqVfPTTz+lGklm1qxZpkyZMsbHx8c0b97cfPbZZw6jXuzbt880bNjQ+Pj4mODgYPPRRx+lGrHr4sWL5qWXXjKFChUykkyZMmXMtGnTjDHGXLp0yXTp0sUEBgaaXLlymeeff94MHDjQ4TUlJSWZ4cOHmyJFihgPDw8TGhpqFixYkO77ZsVmzHUdSQAAuIctW7ZMDRs21JkzZ27qzpNAdtOzZ09t377dfjHrnea5555T+/bt1ahRo9u+7buq6wUAAMC97r333tPGjRu1e/du+zUk114sfaeIjY3Vnj175OnpmaELH7PCXTXqBQAAt6pBgwaprtoH7iSrV6/WmDFjdO7cOZUqVUoTJ05Ujx49XF2W0w4fPqz7779f3t7e+uabb1xSA10vAAAAAAt0vQAAAAAsEJQBAAAACwRlAAAAwAJBGQAAALBAUAaAbKhBgwZ68cUXb3k9w4cPV9WqVdOdp0uXLvZbM9+s/fv3y2azpbpVLQDcyQjKAJCGLl26yGazpXo89thjri4tw1555RVFRUW5ugwAuCMxjjIApOOxxx7T9OnTHdq8vLxcVE3GGWOUlJQkf39/+fv7u7ocALgjcUYZANLh5eWlggULOjxy584tY4yGDx+uYsWKycvLS4ULF9YLL7xgXy4hIUEDBgxQcHCwvLy8VKZMGU2dOtU+ffny5apdu7a8vLxUqFAhDRw4UFeuXEmzjq+//lo1a9ZUzpw5VbBgQXXq1EnHjx+3T1+2bJlsNpsWLFigGjVqyMvLSytWrEjV9SIpKUmRkZHKlSuX8ubNq9deey3VzTUWLlyo+vXr2+dp3ry59uzZ4zDP6tWrVa1aNXl7e6tmzZpav359qpq3bNmixx9/XP7+/goKCtIzzzyjkydPZvi9BwBXIygDwE346aef9P777+vTTz/Vrl27NHv2bIWEhNind+7cWf/97381ceJEbdu2TZ9++qn9zO7hw4fVtGlT1apVSxs3btQnn3yiqVOn6q233kpze5cvX9abb76pjRs3avbs2dq/f7+6dOmSar6BAwdq9OjR2rZtm6pUqZJq+rhx4/TFF19o2rRpWrFihU6fPq1Zs2Y5zBMfH6/IyEj9/fffioqKkpubm5544gklJydLks6fP6/mzZurUqVKWrt2rYYPH65XXnnFYR1nz57VI488omrVqunvv//WwoULFRMTo/bt22f4PQYAlzMAAEsRERHG3d3d+Pn5OTzefvttM27cOFOuXDmTmJiYarkdO3YYSWbJkiWW63399ddN+fLlTXJysr1t0qRJxt/f3yQlJRljjHn44YdN//7906xtzZo1RpI5d+6cMcaYpUuXGklm9uzZDvMNGzbMhIaG2p8XKlTIjBkzxv788uXLpmjRoqZly5ZpbuvEiRNGktm8ebMxxphPP/3U5M2b11y8eNE+zyeffGIkmfXr1xtjjHnzzTdNkyZNHNZz6NAhI8ns2LEjzW0BQHbCGWUASEfDhg21YcMGh0evXr3Url07Xbx4UaVKlVLPnj01a9Yse9eJDRs2yN3dXQ8//LDlOrdt26a6devKZrPZ2+rVq6fz58/r33//tVxm7dq1Cg8PV7FixZQzZ077ug8ePOgwX82aNdN8LbGxsTp69Kjq1Kljb8uRI0eqZXbt2qWOHTuqVKlSCggIUIkSJRy2lXK22tvb275M3bp1HdaxceNGLV261N5H2t/fXxUqVJCkVN04ACC74mI+AEiHn5+fypQpk6o9T5482rFjh3799VctWbJEvXv31tixY7V8+XL5+Phkag3x8fEKCwtTWFiYvv32W+XPn18HDx5UWFiYEhMTU9V7q8LDw1W8eHFNmTJFhQsXVnJysipXrpxqW+k5f/68wsPD9e6776aaVqhQoVuuEQBuB84oA8BN8vHxUXh4uCZOnKhly5YpOjpamzdvVkhIiJKTk7V8+XLL5SpWrKjo6GiHi+hWrlypnDlzqmjRoqnm3759u06dOqXRo0frwQcfVIUKFRwu5MuowMBAFSpUSH/99Ze97cqVK1q7dq39+alTp7Rjxw4NHjxYjRo1UsWKFXXmzJlU9W/atEmXLl2yt/35558O81SvXl3//POPSpQooTJlyjg8MiPMA8DtQFAGgHQkJCTo2LFjDo+TJ0/qiy++0NSpU7Vlyxbt3btX33zzjXx8fFS8eHGVKFFCERER6tatm2bPnq19+/Zp2bJl+v777yVJvXv31qFDh9SvXz9t375dP//8s4YNG6bIyEi5uaU+LBcrVkyenp768MMPtXfvXs2ZM0dvvvnmTb2e/v37a/To0Zo9e7a2b9+u3r176+zZs/bpuXPnVt68efXZZ59p9+7d+u233xQZGemwjk6dOslms6lnz57aunWr5s+fr/fee89hnj59+uj06dPq2LGj1qxZoz179mjRokXq2rWrkpKSbqp2ALjdCMoAkI6FCxeqUKFCDo+UodOmTJmievXqqUqVKvr11181d+5c5c2bV5L0ySefqG3bturdu7cqVKignj17Kj4+XpJUpEgRzZ8/X6tXr1ZoaKh69eql7t27a/DgwZY15M+fX1988YV++OEHVapUSaNHj04VTDPq5Zdf1jPPPKOIiAjVrVtXOXPm1BNPPGGf7ubmpu+++05r165V5cqV9dJLL2ns2LEO6/D399fcuXO1efNmVatWTW+88UaqLhaFCxfWypUrlZSUpCZNmigkJEQvvviicuXKZfnHAABkRzZjrhtAEwAAAABnlAEAAAArBGUAAADAAkEZAAAAsEBQBgAAACwQlAEAAAALBGUAAADAAkEZAAAAsEBQBgAAACwQlAEAAAALBGUAAADAAkEZAAAAsEBQBgAAACz8H98Xs1795UBMAAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "escolaridades = ['Fundamental', 'Médio', 'Superior', 'Pós-graduação']\n",
        "probabilidades = np.array([prob_fundamental, prob_medio, prob_superior, prob_pg])\n",
        "\n",
        "# Calculando a CDF usando a soma cumulativa das probabilidades\n",
        "cdf_probabilidades = np.cumsum(probabilidades)\n",
        "\n",
        "# Criando o gráfico de CDF\n",
        "plt.figure(figsize = (8,6))\n",
        "plt.step(escolaridades, cdf_probabilidades, where='post', color='blue') #O parâmetro where='post' indica que o degrau é colocado após o ponto de dados, alinhando a função degrau com os valores discretos.\n",
        "\n",
        "\n",
        "# Adicionando título e rótulos aos eixos\n",
        "\n",
        "plt.title('Função de Distribuição Acumulada da Escolaridade', fontsize = 16)\n",
        "plt.xlabel('Escolaridade')\n",
        "plt.ylabel('Probabilidade Acumulada')\n",
        "\n",
        "# Exibindo o gráfico\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZcwwAkhUX6kM"
      },
      "source": [
        "##8. Qual a margem de erro amostral da proporção populacional, considerando a proporção de pessoas com nível de inglês intermediário?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wxLNh5IOnq59",
        "outputId": "2d21bf65-40cd-4625-d75f-408d4a1d8880"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.329"
            ]
          },
          "execution_count": 41,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Encontrando a proporção de pessoas com nível de inglês intermediário\n",
        "\n",
        "proporcao_amostral = len(df[df['nível de proficiência em inglês'] == 'Intermediário'])/len(df)\n",
        "proporcao_amostral"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZchXmWLkjveO",
        "outputId": "c2603f8d-7412-4164-f4e7-95cdf9ade9d9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.029121068108804226"
            ]
          },
          "execution_count": 45,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "\n",
        "#Proporção amostral (p)\n",
        "p = proporcao_amostral\n",
        "\n",
        "# O tamanho da amostra (n)\n",
        "n = len(df)\n",
        "\n",
        "# Nível de confiança desejado: 95%\n",
        "confianca = 0.95\n",
        "\n",
        "# Encontrando o escore Z correspondente ao nível de confiança\n",
        "# Para um nível de confiança de 95%, o escore Z é aproximadamente 1.96\n",
        "z = stats.norm.ppf((confianca + 1) / 2)\n",
        "\n",
        "# Calculando a margem de erro\n",
        "margem_de_erro = z * np.sqrt((p * (1 - p) / n))\n",
        "\n",
        "margem_de_erro"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5XbOqP9dYDCH"
      },
      "source": [
        "##9. Calcula a renda da população. Qual a probabilidade de encontrar 60 pessoas com uma renda mil reais superior à média?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ke9jgHsYe3KI",
        "outputId": "a5067987-64dc-4123-e32f-264f5558d0f1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "3082.5371800000003"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "media_renda = df.renda.mean()\n",
        "media_renda\n",
        "\n",
        "#p = pop_alvo.shape[0]/df.shape[0]\n",
        "#p\n",
        "#probab = stats.binom.pmf(60, len(df), p)\n",
        "#probab"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QZyIkpnWgljo",
        "outputId": "5edb0f9c-977d-4021-cb88-b1fd62804282"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pop_alvo = df[df['renda'] == (round(media_renda) + 1000)]\n",
        "len(pop_alvo)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "46481M4jhFf6"
      },
      "source": [
        "####Como podemos ver, não existem pessoas com renda exatamente igual a 1000 reais acima da média. Então a probabilidade de encontrá-las é 0. Como alternativa ao que é proposto no exercício, vou calcular a probabilidade de encontrar pessoas com renda superior à média."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uCdNa78bhbNu",
        "outputId": "4329e222-911e-4e0e-895c-e075511c40d6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "161"
            ]
          },
          "execution_count": 85,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pop_alvo = df[df['renda'] > ((round(media_renda)) + 1000)]\n",
        "len(pop_alvo)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pHUukQEWiE0c",
        "outputId": "4d5dfa9d-d614-4094-9c77-dd9426207588"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1000"
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qq0aHj-DhrA0",
        "outputId": "45963083-3eec-4c84-c969-20c04163e0dd"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1.099520565163774e-22"
            ]
          },
          "execution_count": 86,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#len(pop_alvo)/len(df)\n",
        "\n",
        "probab = stats.binom.pmf(60, len(df), len(pop_alvo)/len(df))\n",
        "probab"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oRn4L-LrijfA"
      },
      "source": [
        "####A probabilidade é um número muuuuito pequeno: 2.0371005405985087e-201"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XpLBbmg2YJTE"
      },
      "source": [
        "###10. Qual a probabilidade de escolhermos alguém do Sudeste que seja homem, com ensino fundamental e com renda maior que 2 mil reais por mês?\n",
        "\n",
        "**Resposta:** 1,9%"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZnJv7f_Ca_hu",
        "outputId": "1ec80666-3658-4ec2-9a8a-18fc276cb73f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['Unnamed: 0', 'estado', 'idade', 'escolaridade',\n",
              "       'nível de proficiência em inglês', 'renda', 'sexo'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OhNQldLUbn70",
        "outputId": "2aa17ac4-ed5b-4342-dfec-6837d6204516"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['MA', 'BA', 'AM', 'CE', 'SE', 'DF', 'PI', 'AP', 'AC', 'SC', 'SP',\n",
              "       'GO', 'RO', 'PA', 'MS', 'RN', 'RS', 'RR', 'PR', 'MG', 'MT', 'TO',\n",
              "       'PB', 'PE', 'RJ', 'AL', 'ES'], dtype=object)"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.estado.unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "PFm6cP41R4OL"
      },
      "outputs": [],
      "source": [
        "# P(A ∩ B ∩ C ∩ D) = P(A)×P(B)×P(C)×P(D)\n",
        "df_se =  df[(df['estado'] == 'RJ')|(df['estado'] == 'SP')|(df['estado'] == 'MG')|(df['estado'] == 'ES')]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "ziUzSIrZcD4u"
      },
      "outputs": [],
      "source": [
        "x = len(df_se[(df_se['sexo'] == 'M') & (df_se['renda'] > 2000) & (df_se['escolaridade'] == 'Fundamental')])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RIEKP3-cdu5X",
        "outputId": "9b83755c-3a95-451b-a8b9-78eefb3a53ec"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1.9"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "prob = (x/len(df))*100\n",
        "prob"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ay2xBjWteR5a"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}