Estatistica.ipynb

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  "metadata": {
    "colab": {
      "name": "Estatistica.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPARPbGfT8E7WdVPU2kw7fe",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/paulo-santos-ds/Estatistica/blob/main/Estatistica.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Introdução \n",
        "\n",
        "Existem muitos conceitos fundamentais de estatística que são essenciais para Data Science, a seguir segue os mais usados:\n",
        "\n",
        "1 - População e Amostra: Uma população é um grupo completo de pessoas, objetos ou eventos que você deseja estudar, enquanto uma amostra é uma seleção representativa da população.\n",
        "\n",
        "2 - Medidas de tendência central: As medidas de tendência central, como a média, a mediana e a moda, ajudam a descrever a tendência central de um conjunto de dados.\n",
        "\n",
        "3 - Medidas de dispersão: As medidas de dispersão, como o desvio padrão e a amplitude interquartil, ajudam a descrever a variabilidade em um conjunto de dados.\n",
        "\n",
        "4 - Distribuições de probabilidade: As distribuições de probabilidade, como a distribuição normal, são modelos matemáticos que descrevem como os dados são distribuídos.\n",
        "\n",
        "5 - Testes de hipóteses: Os testes de hipóteses são usados para determinar se uma hipótese nula é rejeitada ou não em relação a uma hipótese alternativa com base em um conjunto de dados.\n",
        "\n",
        "6 - Regressão linear: A regressão linear é uma técnica estatística usada para modelar a relação entre uma variável dependente e uma ou mais variáveis independentes.\n",
        "\n",
        "7 - Análise de variância (ANOVA): A ANOVA é uma técnica estatística usada para determinar se existe uma diferença significativa entre as médias de três ou mais grupos de dados.\n",
        "\n",
        "8 - Estatística descritiva versus inferencial: A estatística descritiva é usada para descrever e resumir um conjunto de dados, enquanto a estatística inferencial é usada para fazer inferências ou conclusões sobre uma população com base em uma amostra de dados.\n",
        "Dominar esses conceitos é crucial para qualquer cientista de dados. Compreender a estatística e como ela é aplicada na análise de dados é fundamental para tomar decisões informadas com base em dados."
      ],
      "metadata": {
        "id": "5uOlfwWreiI0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "YVgsIGSLeaKM"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JhaVaS1GPVSb"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd \n",
        "import matplotlib.pyplot as plt\n",
        "import statistics as stat\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Media"
      ],
      "metadata": {
        "id": "L0U7K14WP8HV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "a =[1,2,3,4,5]\n",
        "np.median(a)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eCpi8_KxP_Db",
        "outputId": "d85b9302-3d24-4342-86b9-f8ad2afa21a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.0"
            ]
          },
          "metadata": {},
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.mean([10,25,55])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BM7vN8gCUIBe",
        "outputId": "0c11c0a5-d5ee-4599-b3e8-2dba615abdcd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "30.0"
            ]
          },
          "metadata": {},
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Mediana"
      ],
      "metadata": {
        "id": "sEs8qw0lUOhi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "np.median([10,25,55])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KdbHZxk3UQG7",
        "outputId": "ee2fcbb9-7752-4f38-f7c7-286988832377"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "25.0"
            ]
          },
          "metadata": {},
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Moda"
      ],
      "metadata": {
        "id": "as62ZrFmUY3E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "a = [1,2,2,3,3,3,3,3,3,3,3,3,3,3]\n",
        "stat.mode(a) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cBk9hTBgUagb",
        "outputId": "c5954321-ab46-49f0-e89f-959e97262210"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {},
          "execution_count": 136
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Histograma"
      ],
      "metadata": {
        "id": "4IZaN-1KUfsK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "np.random.sample(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vE6lu3BVUi66",
        "outputId": "5623867a-5c9e-46bc-f297-c61d4ac0e1b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.72525428, 0.50132438, 0.95608363, 0.6439902 , 0.42385505])"
            ]
          },
          "metadata": {},
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a = np.random.randint(0,1001,size=250)"
      ],
      "metadata": {
        "id": "2tjdR9UQUk5B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(a)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "O6OsRoQVUo0D",
        "outputId": "d377f36e-fba5-4330-e4cd-3e2d66842f19"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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zNHPmTO3du1eSXXMOBAIqLCz827EsxT/HuOdvMsj8+fNNTU2NMcaYmpoaM3/+/DSPKHGtra3m+++/7/z3W2+9ZV577TUTDofN1KlTzcGDB40xxlRVVZnS0lJjjOl2WSZpaGgwCxcuNI899pg5duxYn5hzRUWF2bBhg4lEIsYYY5qbm40x3R/bmXzcRyIRU1BQYI4dO2aMMebo0aNm0qRJJhwOWzXngwcPmqamps5j+ZZ45xjv/DMm6BcvXjT5+fmmo6PDGGNMR0eHyc/PN6FQKM0jS649e/aY5557zvz888/mySef7Hw9FAqZSZMmGWNMt8syRXt7u3n66afN2bNnO38JbJ9zW1ubyc/PN21tbX97vbtjO9OP+0gkYh566CFz6NAhY4wxP/74o5k2bZq1c/7foMc7x0Tmn9DTFntTMBjUiBEj5HK5JEkul0vDhw9XMBi05pOpkUhEO3bsUGFhoYLBoEaNGtW5LDc3V5FIRJcuXep2mcfjScfQY/bOO+9o5syZGjNmTOdrts/57Nmz8ng8eu+99/TDDz9o4MCBWrZsmfr379/lsW2Myejj3uFwaMuWLVqyZImys7N15coV/fe//+329znT53xLvHNMZP4ZdQ3ddhUVFcrOzta8efPSPZSU+umnn9TQ0KDi4uJ0D6VXhcNhnT17Vvfee68+//xzlZSUaOnSpbp69Wq6h5YyHR0d2rZtm7Zu3ar9+/fr/fff1/Lly62eczplzBn6/z4IzOVyWfcgsEAgoNOnT6u6ulpOp1M+n09NTU2dy1taWuR0OuXxeLpdlgkOHjyokydPasqUKZKkc+fOaeHChZo/f761c5ZuHsP9+vVTUVGRJOn+++/X0KFD1b9//y6PbWNMRh/3R48e1YULF5Sfny9Jys/P14ABA5SVlWXtnG/prlndzTGR+WfMGbrNDwLbvHmzGhoaVFVVJbfbLUmaOHGirl+/rkOHDkmSdu7cqccffzzqskzw4osv6rvvvtO+ffu0b98+jRw5Uh999JEWLVpk7Zylm5eJJk+erAMHDki6eSdDKBRSXl5el8d2ph/3I0eO1Llz5/TLL79Iuvnsp1AopLFjx1o751u6m0e8y6LJqGe52PggsBMnTqioqEh5eXnq37+/JGnMmDGqqqrS4cOHVV5ervb2do0ePVqbNm3SnXfeKUndLss0hYWFqq6u1vjx462f89mzZ/X666/r0qVL6tevn5YvX65HH32022M704/7r776Sh988IEcjpvftPPyyy9r6tSpVs15/fr12rt3ry5evKihQ4fK4/Fo165dcc8x3vlnVNABAF3LmEsuAIDuEXQAsARBBwBLEHQAsARBBwBLEHQAsARBBwBLEHQAsMT/AVeL2aYqph6OAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.sort(a)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A6hfb9qeUt-A",
        "outputId": "6338cb6c-ba2a-4932-efe1-24e728dda8ba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([  4,  11,  13,  16,  24,  29,  32,  33,  36,  41,  43,  44,  61,\n",
              "        67,  69,  73,  80,  83,  87,  87,  88,  91,  93,  93, 106, 111,\n",
              "       114, 120, 121, 129, 129, 136, 136, 136, 138, 139, 146, 147, 148,\n",
              "       152, 160, 164, 166, 168, 174, 174, 176, 189, 191, 195, 197, 199,\n",
              "       201, 207, 207, 209, 209, 212, 216, 217, 219, 221, 227, 232, 234,\n",
              "       254, 256, 273, 274, 274, 276, 281, 282, 284, 287, 289, 290, 290,\n",
              "       291, 291, 297, 307, 307, 314, 322, 323, 324, 326, 329, 340, 341,\n",
              "       341, 347, 348, 349, 357, 368, 373, 376, 381, 393, 394, 405, 414,\n",
              "       421, 423, 423, 423, 426, 429, 433, 433, 437, 438, 446, 455, 459,\n",
              "       463, 469, 470, 480, 488, 498, 507, 508, 508, 509, 514, 517, 525,\n",
              "       534, 536, 537, 539, 539, 540, 541, 547, 551, 552, 555, 556, 563,\n",
              "       573, 575, 579, 584, 586, 591, 593, 595, 606, 616, 617, 623, 623,\n",
              "       628, 637, 645, 647, 653, 657, 662, 663, 668, 670, 694, 696, 701,\n",
              "       709, 710, 715, 716, 726, 727, 730, 732, 735, 739, 750, 753, 763,\n",
              "       765, 767, 768, 770, 771, 771, 777, 779, 782, 786, 786, 798, 799,\n",
              "       800, 802, 803, 804, 807, 809, 812, 814, 819, 820, 821, 823, 826,\n",
              "       832, 839, 855, 857, 859, 860, 865, 871, 873, 876, 884, 884, 885,\n",
              "       886, 890, 891, 894, 896, 899, 908, 919, 930, 932, 939, 941, 941,\n",
              "       946, 949, 949, 949, 951, 952, 954, 967, 968, 971, 978, 978, 979,\n",
              "       982, 987, 998])"
            ]
          },
          "metadata": {},
          "execution_count": 140
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Boxplot"
      ],
      "metadata": {
        "id": "xI2BCT-OUyfA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.boxplot(a)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "KZA3NrgVU0f4",
        "outputId": "45b7c0e9-8ada-4322-8027-9f77a4927938"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAD7CAYAAABnoJM0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAOH0lEQVR4nO3dX2ibdd/H8U+SYudcQ5qs6bLOx9GDzchAYcWeKaxidpB1ggeZRUXmFBFvtwOHRbAV3Um2IQ428Q+eCMWdCOoy7qXCBNGD4aYTajYqtfUpLGu6dKXdYBOT6zl4oHDfM+mfK/+67/t1tnybXL9A2bvX78ofj+M4jgAAZnnrvQAAQH0RAgAwjhAAgHGEAACMIwQAYBwhAADjCAEAGNdU7wWs1PXrN1Us8hYINJZQaJ3y+Rv1XgZwB6/Xo9bW+/5xtmpDUCw6hAANid9LrDZsDQGAcYQAAIwjBABg3KIhSCaT2rFjh7Zu3arR0dGF28fHx5VIJBSLxZRIJDQxMeF6BgCovUVD0NPTo6GhIXV0dPzH7YODg+rr61M6nVZfX58GBgZczwAAtbdoCLq6uhSJRP7jtnw+r0wmo3g8LkmKx+PKZDKamZlZ8QwAUB8revloNptVe3u7fD6fJMnn8ykcDiubzcpxnBXNgsHgstYQCq1bydKBqmtra6n3EoBlWbXvI8jnb/B6bVTdY4916/LlS1U9xoMPRvX99+eqegzA6/WU/AN6RSGIRCKamppSoVCQz+dToVBQLpdTJBKR4zgrmgGNaLn/QYfDfuVyc1VaDVAdK3r5aCgUUjQaVSqVkiSlUilFo1EFg8EVzwAA9eFZ7DuLDx06pOHhYV27dk2tra0KBAI6ffq0xsbG1N/fr7m5Ofn9fiWTSXV2dkrSimfLwdYQGhFnBGhU5baGFg1BoyIEaESEAI2qXAh4ZzEAGEcIAMA4QgAAxhECADCOEACAcYQAAIwjBABgHCEAAOMIAQAYRwgAwDhCAADGEQIAMI4QAIBxhAAAjCMEAGAcIQAA4wgBABhHCADAOEIAAMYRAgAwjhAAgHGEAACMIwQAYBwhAADjCAEAGEcIAMA4QgAAxhECADCOEACAcYQAAIxzHYLvvvtOTz31lHbv3q3e3l4NDw9LksbHx5VIJBSLxZRIJDQxMbFwn3IzAEBteRzHcVZ6Z8dx9Oijj2poaEhbtmzR5cuX9cwzz+jChQt64YUX9PTTT2v37t36+uuv9eWXX+rzzz+XJD3//PMlZ0uVz99QsbjipQNVEQ77lcvN1XsZwB28Xo9CoXX/PHP/4F7Nz89Lkubn5xUOh3X9+nVlMhnF43FJUjweVyaT0czMjPL5fMkZAKD2mtzc2ePx6IMPPtCrr76qtWvX6ubNm/rkk0+UzWbV3t4un88nSfL5fAqHw8pms3Icp+QsGAy6f0YAgGVxFYK///5bH3/8sT788ENt375dFy5c0IEDB3T48OFKra+kUqc4QL21tbXUewnAsrgKwaVLl5TL5bR9+3ZJ0vbt23XvvfequblZU1NTKhQK8vl8KhQKyuVyikQichyn5Gw5uEaARjU9PV/vJQB3qNo1gg0bNujq1av6448/JEljY2PK5/N64IEHFI1GlUqlJEmpVErRaFTBYFChUKjkDABQe65eNSRJ33zzjT799FN5PB5J0uuvv64nnnhCY2Nj6u/v19zcnPx+v5LJpDo7OyWp7GypOCNAI+JVQ2hU5c4IXIegXggBGhEhQKOq6stHAQCrm6uLxcBqsmXL/2h2drbqxwmH/VV9/EAgoNHR/63qMWALIYAZs7OzVd+2aWtrqfqrhqodGtjD1hAAGEcIAMA4QgAAxhECADCOEACAcYQAAIwjBABgHCEAAOMIAQAYRwgAwDhCAADGEQIAMI4QAIBxhAAAjCMEAGAcIQAA4wgBABhHCADAOEIAAMYRAgAwjhAAgHGEAACMIwQAYBwhAADjCAEAGEcIAMA4QgAAxhECADDOdQhu376twcFBPfnkk9q1a5fefvttSdL4+LgSiYRisZgSiYQmJiYW7lNuBgCoLdchOHLkiJqbm5VOp3Xq1Cnt379fkjQ4OKi+vj6l02n19fVpYGBg4T7lZgCA2nIVgps3b+qrr77S/v375fF4JEnr169XPp9XJpNRPB6XJMXjcWUyGc3MzJSdAQBqr8nNnScnJxUIBHT8+HGdO3dO9913n/bv3681a9aovb1dPp9PkuTz+RQOh5XNZuU4TslZMBh0/4wAAMviKgSFQkGTk5N66KGH9Oabb+rXX3/VK6+8omPHjlVqfSWFQuuqfgzcfdraWjgG8F9chSASiaipqWlhm+fhhx9Wa2ur1qxZo6mpKRUKBfl8PhUKBeVyOUUiETmOU3K2HPn8DRWLjpvlw6Dp6fmqPn5bW0vVjyFV/3ng7uP1ekr+Ae3qGkEwGFR3d7d+/PFHSf//aqB8Pq/NmzcrGo0qlUpJklKplKLRqILBoEKhUMkZAKD2PI7juPqzenJyUm+99ZZmZ2fV1NSkAwcO6PHHH9fY2Jj6+/s1Nzcnv9+vZDKpzs5OSSo7WyrOCLBc4bBfudxcVY9RizOCWjwP3H3KnRG4DkG9EAIsFyGAZVXbGgIArH6EAACMIwQAYBwhAADjCAEAGEcIAMA4QgAAxhECADCOEACAcYQAAIwjBABgHCEAAOMIAQAY5+qLaYDV5N//6tH8Jy9U9Ri1+LqYf/+rpwZHgSV8DDXM4GOoYRkfQw0AKIkQAIBxhAAAjCMEAGAcIQAA4wgBABhHCADAOEIAAMYRAgAwjhAAgHGEAACMIwQAYBwhAADjCAEAGEcIAMA4QgAAxlUsBMePH9fWrVs1OjoqSbp48aJ6e3sVi8W0d+9e5fP5hZ8tNwMA1FZFQvDbb7/p4sWL6ujokCQVi0UdPHhQAwMDSqfT6urq0tGjRxedAQBqz3UI/vrrL7377rt65513Fm4bGRlRc3Ozurq6JEl79uzRmTNnFp0BAGrPdQiOHTum3t5ebdq0aeG2bDarjRs3Lvw7GAyqWCxqdna27AwAUHtNbu78yy+/aGRkRG+88Ual1rNkpb6EGSinra2FYwD/xVUIfvrpJ42Njamnp0eSdPXqVb344ot67rnndOXKlYWfm5mZkdfrVSAQUCQSKTlbjnz+hopFx83yYdD09HxVH7+traXqx5Cq/zxw9/F6PSX/gHa1NfTyyy/rhx9+0NmzZ3X27Flt2LBBn332mfbt26dbt27p/PnzkqSTJ09q586dkqRt27aVnAEAas/VGUEpXq9Xhw8f1uDgoG7fvq2Ojg4dOXJk0RkAoPY8juOsyv0VtoawXOGwX7ncXFWPUYutoVo8D9x9qrY1BABY/QgBABhHCADAOEIAAMYRAgAwjhAAgHGEAACMIwQAYBwhAADjCAEAGEcIAMA4QgAAxhECADCOEACAcYQAAIwjBABgHCEAAOMIAQAYRwgAwDhCAADGEQIAMI4QAIBxhAAAjCMEAGAcIQAA4wgBABhHCADAOEIAAMYRAgAwrqneCwBqKRz213sJrgUCgXovAXcZQgAzcrm5qh8jHPbX5DhAJbnaGrp+/bpeeuklxWIx7dq1S6+99ppmZmYkSRcvXlRvb69isZj27t2rfD6/cL9yMwBAbbkKgcfj0b59+5ROp3Xq1Cndf//9Onr0qIrFog4ePKiBgQGl02l1dXXp6NGjklR2BgCoPVchCAQC6u7uXvj3I488oitXrmhkZETNzc3q6uqSJO3Zs0dnzpyRpLIzAEDtVewaQbFY1BdffKEdO3Yom81q48aNC7NgMKhisajZ2dmys+VcBAuF1lVq6UBFtbW11HsJwLJULATvvfee1q5dq2effVbffvttpR62pHz+hopFp+rHAZZrenq+3ksA7uD1ekr+AV2RECSTSf3555/66KOP5PV6FYlEdOXKlYX5zMyMvF6vAoFA2RkAoPZcv6Hs/fff18jIiE6cOKF77rlHkrRt2zbdunVL58+flySdPHlSO3fuXHQGAKg9j+M4K95f+f333xWPx7V582atWbNGkrRp0yadOHFCP//8swYHB3X79m11dHToyJEjWr9+vSSVnS0VW0NoRLyPAI2q3NaQqxDUEyFAIyIEaFTlQsBnDQGAcYQAAIwjBABgHCEAAOMIAQAYRwgAwDhCAADGEQIAMI4QAIBxhAAAjCMEAGAcIQAA4wgBABhHCADAOEIAAMYRAgAwjhAAgHGEAACMIwQAYBwhAADjCAEAGEcIAMA4QgAAxhECADCOEACAcYQAAIwjBABgHCEAAOMIAQAYRwgAwDhCAADG1S0E4+PjSiQSisViSiQSmpiYqNdSAMC0uoVgcHBQfX19SqfT6uvr08DAQL2WAgCmeRzHcWp90Hw+r1gspnPnzsnn86lQKKi7u1vDw8MKBoNLfIwbKhZrvnQY89hj3bp8+VJVj/Hgg1F9//25qh4D8Ho9CoXW/eOsqcZrkSRls1m1t7fL5/NJknw+n8LhsLLZ7JJDUOoJAZV06VKm3ksAqq4uIagEzgjQiNraWjQ9PV/vZQB3KHdGUJdrBJFIRFNTUyoUCpKkQqGgXC6nSCRSj+UAgGl1CUEoFFI0GlUqlZIkpVIpRaPRJW8LAQAqpy4XiyVpbGxM/f39mpubk9/vVzKZVGdn55Lvz9YQGhFbQ2hU5baG6hYCtwgBGhEhQKNquGsEAIDGQQgAwLhV+/JRr9dT7yUA/4jfTTSicr+Xq/YaAQCgMtgaAgDjCAEAGEcIAMA4QgAAxhECADCOEACAcYQAAIwjBABgHCEAAOMIAVAByWRSO3bs0NatWzU6Olrv5QDLQgiACujp6dHQ0JA6OjrqvRRg2Vbth84BjaSrq6veSwBWjDMCADCOEACAcYQAAIwjBABgHF9MA1TAoUOHNDw8rGvXrqm1tVWBQECnT5+u97KAJSEEAGAcW0MAYBwhAADjCAEAGEcIAMA4QgAAxhECADCOEACAcYQAAIz7P7P1OdwqzPj9AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.percentile(a,q=55)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_uuc9SpUU_lQ",
        "outputId": "28308e04-1639-44f6-e379-f66e64699547"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "546.7"
            ]
          },
          "metadata": {},
          "execution_count": 142
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.mean(a)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v0NwU7zlVBrk",
        "outputId": "5d43d11f-73a7-4eee-def1-bba67485d921"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "501.04"
            ]
          },
          "metadata": {},
          "execution_count": 143
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Localizando Outlier"
      ],
      "metadata": {
        "id": "J_Mv_hqLVFkj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "a = np.array(list(a)+[5000])"
      ],
      "metadata": {
        "id": "Ydf8PNQ3VKOr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.boxplot(a[a<=np.percentile(a,q=99)])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "BKL4N339VOI7",
        "outputId": "71bd87b9-0a1e-4c58-e562-6ebbb1d7aa31"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.percentile(a,q=99.9)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qRRYFzlcVWE0",
        "outputId": "82caac36-d571-4aac-bd47-55320a3e6c98"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3999.5000000001137"
            ]
          },
          "metadata": {},
          "execution_count": 146
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Grafico de Calor"
      ],
      "metadata": {
        "id": "J2kffZ7RVa65"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "np.random.seed(0)\n",
        "sns; sns.set_theme()\n",
        "uniform_data = np.random.rand(10,12)\n",
        "ax = sns.heatmap(uniform_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "btFmKnYNVeLp",
        "outputId": "25cd0cc2-7279-4f6b-90b4-c6251163410e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Importando dados da Planta (iris.csv)"
      ],
      "metadata": {
        "id": "2XUE-sP9dkA0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('iris.csv',sep=';',decimal=',')\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "A_PXGME5ViwZ",
        "outputId": "2882f70e-0137-40f4-afea-e01add7f58df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
              "0                5.1               3.5                1.4               0.2   \n",
              "1                4.9               3.0                1.4               0.2   \n",
              "2                4.7               3.2                1.3               0.2   \n",
              "3                4.6               3.1                1.5               0.2   \n",
              "4                5.0               3.6                1.4               0.2   \n",
              "\n",
              "   target target_names  \n",
              "0       0       setosa  \n",
              "1       0       setosa  \n",
              "2       0       setosa  \n",
              "3       0       setosa  \n",
              "4       0       setosa  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4b0b8888-15ef-45c7-bd9b-e5e02d2b1256\">\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>sepal length (cm)</th>\n",
              "      <th>sepal width (cm)</th>\n",
              "      <th>petal length (cm)</th>\n",
              "      <th>petal width (cm)</th>\n",
              "      <th>target</th>\n",
              "      <th>target_names</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4b0b8888-15ef-45c7-bd9b-e5e02d2b1256')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        \n",
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              "    .colab-df-container {\n",
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              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\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",
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              "\n",
              "      <script>\n",
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              "\n",
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              "                                                     [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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 148
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Descrição dos dados e Estatistica"
      ],
      "metadata": {
        "id": "kUwpdrE8dqFc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "VYQ5MJB0WjrF",
        "outputId": "305db96f-eaed-4127-a15f-c0a18c070aeb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       sepal length (cm)  sepal width (cm)  petal length (cm)  \\\n",
              "count         150.000000        150.000000         150.000000   \n",
              "mean            5.843333          3.057333           3.758000   \n",
              "std             0.828066          0.435866           1.765298   \n",
              "min             4.300000          2.000000           1.000000   \n",
              "25%             5.100000          2.800000           1.600000   \n",
              "50%             5.800000          3.000000           4.350000   \n",
              "75%             6.400000          3.300000           5.100000   \n",
              "max             7.900000          4.400000           6.900000   \n",
              "\n",
              "       petal width (cm)      target  \n",
              "count        150.000000  150.000000  \n",
              "mean           1.199333    1.000000  \n",
              "std            0.762238    0.819232  \n",
              "min            0.100000    0.000000  \n",
              "25%            0.300000    0.000000  \n",
              "50%            1.300000    1.000000  \n",
              "75%            1.800000    2.000000  \n",
              "max            2.500000    2.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-db9f06d6-727f-4c33-b622-3374c8d62833\">\n",
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              "\n",
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              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal length (cm)</th>\n",
              "      <th>sepal width (cm)</th>\n",
              "      <th>petal length (cm)</th>\n",
              "      <th>petal width (cm)</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>5.843333</td>\n",
              "      <td>3.057333</td>\n",
              "      <td>3.758000</td>\n",
              "      <td>1.199333</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.828066</td>\n",
              "      <td>0.435866</td>\n",
              "      <td>1.765298</td>\n",
              "      <td>0.762238</td>\n",
              "      <td>0.819232</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>4.300000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5.100000</td>\n",
              "      <td>2.800000</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>5.800000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.350000</td>\n",
              "      <td>1.300000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.400000</td>\n",
              "      <td>3.300000</td>\n",
              "      <td>5.100000</td>\n",
              "      <td>1.800000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>7.900000</td>\n",
              "      <td>4.400000</td>\n",
              "      <td>6.900000</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "\n",
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              "\n",
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              "  </style>\n",
              "\n",
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              "        const buttonEl =\n",
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              "\n",
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              "          const element = document.querySelector('#df-db9f06d6-727f-4c33-b622-3374c8d62833');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 149
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(df['sepal length (cm)'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "T2YwoZyrZuK6",
        "outputId": "14c61ad1-1ba0-42f8-a1d4-80f0affd6d42"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(df['sepal width (cm)'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "400em5NBZ8gd",
        "outputId": "f2780aca-1f52-469c-eaaa-be53824bdb2d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(df['sepal width (cm)'], df['sepal length (cm)'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "Pm7EDT-VXdkS",
        "outputId": "46803c19-9697-4303-feee-f5c79aaf28e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Correlação das Variaveis"
      ],
      "metadata": {
        "id": "kI4QSBVdbaKS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "f2CwF65ibWFc",
        "outputId": "0f052c6a-b045-4f8b-e035-0eab8d520dbf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   sepal length (cm)  sepal width (cm)  petal length (cm)  \\\n",
              "sepal length (cm)           1.000000         -0.117570           0.871754   \n",
              "sepal width (cm)           -0.117570          1.000000          -0.428440   \n",
              "petal length (cm)           0.871754         -0.428440           1.000000   \n",
              "petal width (cm)            0.817941         -0.366126           0.962865   \n",
              "target                      0.782561         -0.426658           0.949035   \n",
              "\n",
              "                   petal width (cm)    target  \n",
              "sepal length (cm)          0.817941  0.782561  \n",
              "sepal width (cm)          -0.366126 -0.426658  \n",
              "petal length (cm)          0.962865  0.949035  \n",
              "petal width (cm)           1.000000  0.956547  \n",
              "target                     0.956547  1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e46887f0-5d0c-4f99-82c1-d03aa2608bf7\">\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>sepal length (cm)</th>\n",
              "      <th>sepal width (cm)</th>\n",
              "      <th>petal length (cm)</th>\n",
              "      <th>petal width (cm)</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>sepal length (cm)</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.117570</td>\n",
              "      <td>0.871754</td>\n",
              "      <td>0.817941</td>\n",
              "      <td>0.782561</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sepal width (cm)</th>\n",
              "      <td>-0.117570</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.428440</td>\n",
              "      <td>-0.366126</td>\n",
              "      <td>-0.426658</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>petal length (cm)</th>\n",
              "      <td>0.871754</td>\n",
              "      <td>-0.428440</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.962865</td>\n",
              "      <td>0.949035</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>petal width (cm)</th>\n",
              "      <td>0.817941</td>\n",
              "      <td>-0.366126</td>\n",
              "      <td>0.962865</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.956547</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target</th>\n",
              "      <td>0.782561</td>\n",
              "      <td>-0.426658</td>\n",
              "      <td>0.949035</td>\n",
              "      <td>0.956547</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e46887f0-5d0c-4f99-82c1-d03aa2608bf7')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "df[['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)','petal width (cm)']].corr()"
      ],
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        "colab": {
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      "execution_count": null,
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          "data": {
            "text/plain": [
              "                   sepal length (cm)  sepal width (cm)  petal length (cm)  \\\n",
              "sepal length (cm)           1.000000         -0.117570           0.871754   \n",
              "sepal width (cm)           -0.117570          1.000000          -0.428440   \n",
              "petal length (cm)           0.871754         -0.428440           1.000000   \n",
              "petal width (cm)            0.817941         -0.366126           0.962865   \n",
              "\n",
              "                   petal width (cm)  \n",
              "sepal length (cm)          0.817941  \n",
              "sepal width (cm)          -0.366126  \n",
              "petal length (cm)          0.962865  \n",
              "petal width (cm)           1.000000  "
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              "      <th></th>\n",
              "      <th>sepal length (cm)</th>\n",
              "      <th>sepal width (cm)</th>\n",
              "      <th>petal length (cm)</th>\n",
              "      <th>petal width (cm)</th>\n",
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              "      <th>sepal length (cm)</th>\n",
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              "      <td>-0.117570</td>\n",
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              "      <td>1.000000</td>\n",
              "      <td>-0.428440</td>\n",
              "      <td>-0.366126</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>petal length (cm)</th>\n",
              "      <td>0.871754</td>\n",
              "      <td>-0.428440</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.962865</td>\n",
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              "      <th>petal width (cm)</th>\n",
              "      <td>0.817941</td>\n",
              "      <td>-0.366126</td>\n",
              "      <td>0.962865</td>\n",
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              "\n",
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              "\n",
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              "          const element = document.querySelector('#df-2a38b0f6-ec83-4c31-ba48-6742bcd02448');\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",
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              "  "
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rjc0Qu8AcHkR",
        "outputId": "4be309ad-ecea-4af6-832b-b0ae96904133"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)',\n",
              "       'petal width (cm)', 'target', 'target_names'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 155
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Conclusão"
      ],
      "metadata": {
        "id": "oCQRQs7BsEe8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Em resumo, a estatística é uma parte fundamental da ciência de dados e é usada para descrever, analisar e interpretar conjuntos de dados. Compreender os conceitos fundamentais da estatística, como medidas de tendência central, medidas de dispersão, distribuições de probabilidade, testes de hipóteses, regressão linear, ANOVA e estatística descritiva versus inferencial, é essencial para qualquer cientista de dados. Dominar esses conceitos permite aos cientistas de dados tomar decisões informadas com base em dados e obter insights valiosos que podem ser usados para melhorar a eficiência, desempenho e resultados em uma ampla variedade de áreas.\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "KVd47ot1sAmM"
      }
    }
  ]
}