Frequentist Inference Case Study - Part A (3).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d2c9e463",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import norm\n",
    "from scipy.stats import t\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from numpy.random import seed\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0f711a95",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "62a89e80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.84800948,  1.30590636,  0.92420797,  0.6404118 , -1.05473698])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seed(47)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7ca161c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.19355593334131074"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "45edb50e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9606195639478641"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "std= np.sqrt(np.sum(\n",
    "    (sample-mean)**2)\n",
    "/5)\n",
    "\n",
    "std  #Q4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c73e3ed0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0740053227518152"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "  #Q5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e6b1063b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9606195639478641"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " #Q6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "10509b1a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6ed0e82e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0740053227518152"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8714f0b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "seed(47)\n",
    "pop_heights=norm.rvs(172,5, 50000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "de47b791",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_= plt.hist(pop_heights, bins=30)\n",
    "_ = plt.xlabel('height (cm)')\n",
    "_ = plt.ylabel('number of people')\n",
    "_ = plt.title('Distribution of heights in entire town population')\n",
    "_ = plt.axvline(172, color='r')\n",
    "_ = plt.axvline(172+5, color='r', linestyle='--')\n",
    "_ = plt.axvline(172-5, color='r', linestyle='--')\n",
    "_ = plt.axvline(172+10, color='r', linestyle='-.')\n",
    "_ = plt.axvline(172-10, color='r', linestyle='-.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0a2c893",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
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