Medium_06_1

Medium_06_1.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([102, 435, 348, 270, 106,  71, 188,  20, 102, 121, 466, 214, 330,\n",
       "       458,  87, 372,  99, 359, 151, 130, 149, 308, 257, 343, 491, 413,\n",
       "       293, 385, 191, 443, 276, 160, 459, 313,  21, 252, 235, 344,  48,\n",
       "       474,  58, 169, 475, 187, 463, 270, 189, 445, 174, 445,  50, 363,\n",
       "        54, 243, 319, 130, 484, 306, 134,  20, 328, 166, 273, 387,  88,\n",
       "       315,  13, 241, 264, 345,  52, 385, 339,  91, 366, 443, 454, 427,\n",
       "       263, 430,  34, 205,  80, 419,  49, 359, 387,   1, 389,  53, 105,\n",
       "       259, 309, 476, 190, 401, 217,  43, 161, 201])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# construct a population pickups for our lab\n",
    "np.random.seed(42)\n",
    "pickups = np.random.randint(0,500 , size=100)\n",
    "pickups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "252.7"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# population mean\n",
    "pickups.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "144.25342283634035"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# population standard deviation\n",
    "pickups.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([166, 201, 458, 190, 445,  87, 385, 427, 387, 166, 474,  49, 430,\n",
       "       205,  54, 343, 413, 389,  20,  58, 191,  87, 463,  88, 389,  52,\n",
       "       102,   1, 102,  20])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# draw a sample from population\n",
    "sample = np.random.choice(pickups, size=30)\n",
    "sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "228.06666666666666"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# our first sample mean\n",
    "sample_mean = sample.mean()\n",
    "sample_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "166.96890756052164"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# standard deiveation for this sample\n",
    "sample_std = np.std(sample, ddof=1)\n",
    "sample_std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30.48421235763086"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# estimated standard error for the sapmle mann\n",
    "sample_std/(30 ** 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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