{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import numpy\n",
    "import scipy.stats as stats\n",
    "from datascience import *\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plots\n",
    "plots.style.use('fivethirtyeight')\n",
    "import numpy as np\n",
    "import scipy.stats as stats\n",
    "\n",
    "\n",
    "def biasdemo1():\n",
    "    \n",
    "    np.random.seed(15)\n",
    "    Population = np.random.normal(15000,500,100000)\n",
    "    PopSigma = np.round(np.std(Population),0)\n",
    "\n",
    "    Biased = make_array()\n",
    "    Unbiased=make_array()\n",
    "\n",
    "    reps = 20\n",
    "\n",
    "    for i in np.arange(reps):\n",
    "        Sample = np.random.choice(Population, 3)\n",
    "        Biased = np.append(Biased, np.std(Sample))\n",
    "        Unbiased = np.append(Unbiased, stats.tstd(Sample))\n",
    "\n",
    "    biasedmean = np.round(np.mean(Biased),0)\n",
    "    unbiasedmean = np.round(np.mean(Unbiased),0)\n",
    "    \n",
    "    plots.figure(figsize=(12,6))\n",
    "    plots.suptitle(\"Biased = Bad, Unbiased = Good\")\n",
    "\n",
    "    plots.subplot(1,2,1)\n",
    "    plots.hist(Biased)\n",
    "    plots.scatter(PopSigma, -3, marker=\"^\", zorder=5, s=80, color='green')\n",
    "    plots.scatter(np.mean(Biased),-3, marker = \"s\", zorder=5, s=80, color='red')\n",
    "    plots.title(\"Biased Estimator of Sigma\")\n",
    "    plots.text(710, 0.1*reps, f\"Estimator = {biasedmean}\", color='red')\n",
    "    plots.text(710, 0.085*reps,f\"True Value = {PopSigma}\", color='green');\n",
    "\n",
    "    plots.subplot(1,2,2)\n",
    "    plots.hist(Unbiased)\n",
    "    plots.scatter(PopSigma, -3, marker=\"^\", zorder=5, s=80, color='green')\n",
    "    plots.scatter(np.mean(Unbiased),-3, marker = \"s\", zorder=5, s=80, color='red')\n",
    "    plots.title(\"Unbiased Estimator of Sigma\")\n",
    "    plots.text(710,  0.1*reps, f\"Estimator = {unbiasedmean}\", color='red')\n",
    "    plots.text(710,  0.085*reps,f\"True Value = {PopSigma}\", color='green');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "biasdemo1()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}