Q9_validation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "risp = np.array([32, 70, 49, 75, 45, 95, 60, 74, 85, 50, 90, 75, 85, 85, 0, \\\n",
    "        100, 53, 100, 78, 80, 50, 96, 80, 100, 50, 2, 39, 20, 80, 85, \\\n",
    "        78, 90, 67, 90, 80, 74, 83, 75, 80, 100, 90, 80, 70, 50, 85, 85, \\\n",
    "        75, 20, 80, 72, 55, 24, 100, 10, 90, 75, 70, 39, 95, 90, 50, 86, \\\n",
    "        100, 70, 1, 100, 100, 100, 7, 90, 100, 80, 30])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kJEcCFwI3LqhzI3Bxt/xC4LNVVT3alCT1NPYZf1XtTfIq4NPAYcDGqrojyZuAuaq6EbgGeG+S7cADDH44SJKWUa85/qraBGxaUPaGecv/B/xVnzbG0Hu66BDkMU+/1o4XPOYlE2deJKktPrJBkhozNcF/oMdHTIMkJyb5XJI7k9yR5LKu/Ngkm5Ns696PWe6+TlqSw5L8d5KPd+sndY8B2d49FuTI5e7jJCU5OsmHktyd5K4kT5v2cU7yD93/661Jrk/y+9M2zkk2JtmdZOu8sqHjmoF3dsd+e5IzJtWPqQj+RT4+YhrsBV5TVacCZwKXdsd5OXBTVZ0M3NStT5vLgLvmrb8VeEf3OJAfMHg8yDT5V+BTVfUnwGkMjn1qxznJSuDvgdmqejKDC0YuZPrG+Vrg3AVlo8b1PODk7rUOePekOjEVwc/iHh9xyKuqXVV1a7f8IwZhsJLffDTGdcALlqeHSyPJCcCfA1d36wGezeAxIDBlx5zkMcAzGVwVR1X9vKp+yJSPM4OLTf6gu+fnkcAupmycq+pmBlc4zjdqXC8A3lMDXwaOTnL8JPoxLcG/mMdHTJXuSadPAW4BVlTVrm7Td4EVy9StpfIvwGuBX3XrjwV+2D0GBKZvvE8C9gD/0U1vXZ3kKKZ4nKtqJ/DPwP8yCPwHgS1M9zjvM2pclyzXpiX4m5LkUcCHgVdX1UPzt3U3yE3NpVpJzgd2V9WW5e7LQXQ4cAbw7qp6CvATFkzrTOE4H8PgDPck4AnAUTx8SmTqHaxxnZbgX8zjI6ZCkiMYhP77quojXfH9+34F7N53L1f/lsDTgecnuZfBFN6zGcx/H91NCcD0jfcOYEdV3dKtf4jBD4JpHufnAP9TVXuq6hfARxiM/TSP8z6jxnXJcm1agn8xj4845HVz29cAd1XV2+dtmv9ojIuBjx3svi2VqnpdVZ1QVasZjOtnq+pFwOcYPAYEpu+Yvwvcl+SUruhs4E6meJwZTPGcmeSR3f/zfcc8teM8z6hxvRF4SXd1z5nAg/OmhPqpqql4AWuBbwLfAl6/3P1ZomN8BoNfA28HbuteaxnMed8EbAP+Czh2ufu6RMd/FvDxbvkPga8A24EPAo9Y7v5N+FhPB+a6sf4ocMy0jzODh4LeDWwF3gs8YtrGGbiewd8wfsHgN7tLRo0rEAZXK34L+DqDK54m0g/v3JWkxkzLVI8kaZEMfklqjMEvSY0x+CWpMQa/JDXG4Jekxhj8ktQYg1+SGvP/usbf+GSfubEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_bins = 10\n",
    "n, bins, patches = plt.hist(risp, num_bins, facecolor='blue', alpha=0.5)\n",
    "plt.vlines(np.mean(risp), 0, 20, colors='r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(risp < 52)"
   ]
  }
 ],
 "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}