mean-std-with-python.es.ipynb

{
  "cells": [
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "y36nuoCplKRM"
      },
      "source": [
        "### Media y Desviación Estándar \n",
        "\n",
        "$$\\text{mean} = \\mu = \\frac{1}{n}\\sum_{i = 1}^n x_i$$\n",
        "\n",
        "$$\\text{standard deviation} = \\sigma = \\sqrt{\\frac{1}{n}\\sum_{i = 1}^n (x_i - \\mu)^2}$$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_W3B7Lg0lKDS",
        "outputId": "7211b8a4-bce0-4ec2-caa7-0667237ce146",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "7.0"
            ]
          },
          "execution_count": 36,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#media de una lista\n",
        "x = [3, 5, 7, 9, 11]\n",
        "np.mean(x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F0H2cUZxlJ-i",
        "outputId": "f824b2f6-0889-48a2-f579-6c6577f11fd9",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "2.8284271247461903"
            ]
          },
          "execution_count": 37,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#desviación estándar de una lista \n",
        "np.std(x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1QLD9HNXlJ4O",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [],
      "source": [
        "#distribución Binominal con n = 20, p = 0.5  \n",
        "b = stats.binom(20, 0.5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N5Wt76YylJ1P",
        "outputId": "583d959e-69a8-4b38-afe3-844e4ec15735",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "10.0"
            ]
          },
          "execution_count": 39,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#media\n",
        "b.mean()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "PqPK-Ow1lJyD",
        "outputId": "31eaed44-08dc-4d58-da2d-844895d3ec5f",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<BarContainer object of 21 artists>"
            ]
          },
          "execution_count": 40,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light",
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "#gráfica\n",
        "plt.bar(range(21), b.pmf(range(21)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "a8KU1n9Mlg6J",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [],
      "source": [
        "#otro ejemplo\n",
        "#binomial n = 50, p = 0.3\n",
        "ex2 = stats.binom(50, 0.3)\n",
        "ex3 = stats.binom(20, 0.3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jgo80blnmWfR",
        "outputId": "6a68e841-da08-4326-a4dc-9ab7a58abd40",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "15.0"
            ]
          },
          "execution_count": 46,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#media\n",
        "ex2.mean()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U-ByFeFyuY8E",
        "outputId": "2f9fc86c-f183-47c4-edad-3bdd2f876020",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "6.0"
            ]
          },
          "execution_count": 47,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "ex3.mean()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bfy3I2xeubzP",
        "outputId": "811354f1-cedb-455e-a7ae-3131df8f9a2f",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "2.0493901531919194"
            ]
          },
          "execution_count": 48,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "ex3.std()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9tA96xDxmWZH",
        "outputId": "fae5fdae-400c-40ec-cc5b-0b9e4a452ec7",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "3.24037034920393"
            ]
          },
          "execution_count": 43,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#desviación \n",
        "ex2.std()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "xWSDRiBpmZlP",
        "outputId": "98c424bb-e3fc-4192-9598-13e41ef6af7e",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<BarContainer object of 21 artists>"
            ]
          },
          "execution_count": 49,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light",
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "#gráfica\n",
        "plt.bar(range(51), ex2.pmf(range(51)))\n",
        "plt.bar(range(21), ex3.pmf(range(21)))"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "calc_distribution_review.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}