distribuicao_binomial.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPEpc6PbqPNrPBK24iBYS8P",
      "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/jeniferGoncalvesDaSilvaDev/Estatistica-python/blob/main/distribuicao_binomial.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-eTGmoTppT6M",
        "outputId": "240b2ebd-2a2e-4f6d-d27e-2cd00c62cf8e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[3.34897977e-01 4.01877572e-01 2.00938786e-01 5.35836763e-02\n",
            " 8.03755144e-03 6.43004115e-04 2.14334705e-05]\n",
            "A probabilidade de obter o número 2 em 0 vezes nas 6 tentativas é de 33.49 %\n",
            "A probabilidade de obter o número 2 em 1 vezes nas 6 tentativas é de 40.19 %\n",
            "A probabilidade de obter o número 2 em 2 vezes nas 6 tentativas é de 20.09 %\n",
            "A probabilidade de obter o número 2 em 3 vezes nas 6 tentativas é de 5.36 %\n",
            "A probabilidade de obter o número 2 em 4 vezes nas 6 tentativas é de 0.80 %\n",
            "A probabilidade de obter o número 2 em 5 vezes nas 6 tentativas é de 0.06 %\n",
            "A probabilidade de obter o número 2 em 6 vezes nas 6 tentativas é de 0.00 %\n"
          ]
        }
      ],
      "source": [
        "from numpy import arange as r\n",
        "from scipy.stats import binom\n",
        "import matplotlib.pyplot as plt\n",
        "#distribuicao de probabilidade binomial\n",
        "#lançamento de dados\n",
        "#numero de tentativas\n",
        "tentativas =6\n",
        "#ptobabilidade de um numero qualquer cair em um dado\n",
        "p=1/6\n",
        "#quantidade de sucesso\n",
        "indice_k = r(0,tentativas+1)\n",
        "probabilidades = binom.pmf(indice_k,tentativas,p)\n",
        "print(probabilidades)\n",
        "#a funcap zip, agrupa duas listas\n",
        "for k, prob in zip(indice_k, probabilidades):\n",
        "    print(f'A probabilidade de obter o número 2 em {k} vezes nas {tentativas} tentativas é de {prob*100:.2f} %')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.bar(indice_k, probabilidades)\n",
        "plt.xlabel('tentativas')\n",
        "plt.ylabel('probabilidades')"
      ],
      "metadata": {
        "id": "NS5nP_S9u1Z4",
        "outputId": "0146554d-d200-48a2-d63c-0fe27278a569",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 262
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'probabilidades')"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}