assignment4.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPKlAPrA1wg62zRxJI3P0RC"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "00jbQNotqrYl"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.random.seed(1)"
      ],
      "metadata": {
        "id": "IJe28Ifsqw7K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "a=np.random.uniform(-1,1,10000)\n",
        "x=[-1,0,1]\n",
        "y=[0.5,0.5,0.5]"
      ],
      "metadata": {
        "id": "344O83Lzqy9l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,6))\n",
        "plt.hist(a, density=True)\n",
        "plt.plot(x,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 540
        },
        "id": "_AohhH1Zq3va",
        "outputId": "ce3b9421-bc47-45b9-c304-49dffa898d1e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7d4c66b72500>]"
            ]
          },
          "metadata": {},
          "execution_count": 79
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#2번"
      ],
      "metadata": {
        "id": "1t7RvCZosyvQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "random.seed(1)\n",
        "from scipy.stats import norm"
      ],
      "metadata": {
        "id": "JmNGxrKmvD3x"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def roll():\n",
        "  return random.randint(1,6)\n",
        "def simulation():\n",
        "  count = []\n",
        "  for i in range(100):\n",
        "    dice = roll()\n",
        "    count.append(dice)\n",
        "  return np.mean(count)\n",
        "def repeat():\n",
        "  xbars = []\n",
        "  for i in range(10000):\n",
        "    xbar = simulation()\n",
        "    xbars.append( xbar)\n",
        "  mean = np.mean(xbars)\n",
        "  var = np.var(xbars)\n",
        "  sd = np.std(xbars)\n",
        "  print('평균:',mean)\n",
        "  print('분산:',var)\n",
        "  pdf_plot = np.linspace(2.5,4.5,1000)\n",
        "  plt.figure(figsize=(10,6))\n",
        "  plt.hist(xbars, density=True)\n",
        "  plt.plot(pdf_plot, norm.pdf(pdf_plot, loc=mean, scale=sd))"
      ],
      "metadata": {
        "id": "pDBhvFmXxlur"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "repeat()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 558
        },
        "id": "QP9BQmG0y8jx",
        "outputId": "47fe27bd-0e7a-415f-baf0-3e54bdcd8783"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "평균: 3.4991199999999996\n",
            "분산: 0.0288773456\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BNy8IPBExGtA"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}