sample parameter and bootstrapping.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "students = np.array([1,0,1,1,1,1,0,0,1,0,1,1,0,1,1,1,1,1,1,1,1])# rep coffee drin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7619047619047619"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "students.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.random.choice(students,21)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7142857142857143"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample =_\n",
    "sample.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "proportion = [np.random.choice(students,21).mean() for _ in range(1000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  6.,  12.,  27.,  63., 112., 387., 161., 140.,  70.,  22.]),\n",
       " array([0.42857143, 0.48571429, 0.54285714, 0.6       , 0.65714286,\n",
       "        0.71428571, 0.77142857, 0.82857143, 0.88571429, 0.94285714,\n",
       "        1.        ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(proportion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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   },
   "file_extension": ".py",
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