robinhuud15/exercise issue6.ipynb

## exercise issue6.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8c89001c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Exercise1\n",
    "#1. Explain the difference between median and middle value?\n",
    "#2. Are mean and mode values always the same for unsorted and sorted dataset? Why? \n",
    "#3. If range is caculate with last datapoint – first datapoint, should the dataset is sorted first or not? Why?\n",
    "#\n",
    "#1. \n",
    "#a. Median: It's the central value in a sorted set of data. It means half the data points are greater than the median and the other half are smaller. So, it represents the \"middle ground\" of the data.\n",
    "#b. Middle value: This term simply refers to the value that is physically in the middle of a data set, regardless of order. If you have an even number of data points, the middle value would be the average of the two central values.\n",
    "#2. \n",
    "#a. Mean: This is the average of all data points, calculated by adding them up and dividing by the total number. Sorting the data doesn't affect the sum of the values, so the mean remains the same.\n",
    "#b. Mode: This is the most frequent value in the data set. Sorting only changes the order, not the number of times each value appears. Therefore, the mode can change if sorting reveals a different value as the most frequent.\n",
    "#3.\n",
    "#It does not matter whether the data is sorted or not when calculating the range.\n",
    "#Range: This is the difference between the largest and smallest values in the data set. Sorting only changes the order, not the values themselves. Therefore, the range remains the same regardless of sorting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "811a6501",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[32, 47, 62, 33, 26, 23, 28, 34, 48, 43, 50, 63, 50, 51, 72, 11, 62, 77, 11, 63, 12, 47, 73, 60, 51, 47, 28, 24, 75, 66, 28, 29, 57, 80, 57, 72, 77, 51, 22, 24, 79, 12, 64, 39, 58, 16, 68, 30, 23, 62, 31, 25, 68, 42, 38, 66, 46, 64, 37, 14, 62, 26, 51, 66, 22, 47, 49, 31, 78, 60, 56, 38, 53, 77, 63, 17, 59, 23, 31, 44, 70, 75, 70, 47, 67, 17, 10, 65, 25, 53, 77, 60, 59, 52, 22, 45, 59, 15, 12, 63]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Exercise2\n",
    "import random as rnd\n",
    "x = [\n",
    "    rnd.randint(10, 80)\n",
    "    for i in range(0, 100)\n",
    "]\n",
    "print(x)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.hist(x, bins=25, color='green', edgecolor='black')\n",
    "plt.xlabel('Value')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Histogram of Random Data by ayodya')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6c31f4c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Exercise3\n",
    "import numpy as np\n",
    "import statistics as stat\n",
    "N = 1000; a = 20; b = 60; x = []\n",
    "for i in range(N):\n",
    "    xi = rnd.randint(a, b)\n",
    "    x.append(xi)\n",
    "plt.hist(x, bins=10)\n",
    "plt.xticks(np.arange(20, 64, 4))\n",
    "plt.xlim([20, 60])\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d989cf10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Exercise4\n",
    "N = 6000; a=1; b=6; dice = []\n",
    "for i in range(N):\n",
    "    xi = rnd.randint(a, b)\n",
    "    dice.append(xi)\n",
    "plt.hist(dice, bins=6)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "979a54d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'mu = 100')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Exercise5\n",
    "mu = 100\n",
    "si = 10\n",
    "N = 100000\n",
    "values = np.random.normal(mu, si, N)\n",
    "\n",
    "plt.subplot(3,3,1)\n",
    "plt.hist(values, 100)\n",
    "plt.axvline(values.mean(), color='r')\n",
    "plt.xlim([60,340])\n",
    "plt.title(\"mu = 100\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ea1daced",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'mu = 200')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mu = 200\n",
    "si = 10\n",
    "N = 100000\n",
    "values = np.random.normal(mu, si, N)\n",
    "\n",
    "plt.subplot(3,3,4)\n",
    "plt.hist(values, 100)\n",
    "plt.axvline(values.mean(), color='r')\n",
    "plt.xlim([60,340])\n",
    "plt.title(\"mu = 200\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9fe299bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'mu = 300')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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srNRqli1bJr1795bc3Fw5efKkTJw4UYYNGyZ1dXVt3I3Iyy+/LDt27JCioiI5ffq0TJ8+XZ577jm5c+eOU/aUnZ0te/bskeLiYikuLpa1a9eKu7u7FBUVOVwvnT5MD2ocpvr6ejGbzZKcnKyN1dTUiMlkku3bt4uISEVFhbi7u0t6erpWc/XqVXFxcZGcnJw2O/ZHuXHjhgCQ/Px8EekYPfn4+MgXX3zhcL10+tO85ly4cAFlZWWIiIjQxvR6PSZMmIAjR44AAE6cOIF79+7Z1VgsFgQFBWk17anhe12+vr4AnLun+/fvIz09HXfv3sWYMWMcrheGqRkND9k29zWQsrIyeHh4wMfH55E17UVEEB8fj3HjxiEoKAiAc/ZUWFgIb29v6PV6LFu2DFlZWXjhhRccrheHfjbPUbT0ayBPWtPaYmJiUFBQgMOHDzdZ50w9DRo0CKdPn0ZFRQUyMjKwYMEC5Ofna+sdpRfOTM0wm80A0OzXQMxmM2pra1FeXv7ImvYQGxuL7Oxs5OXlISAgQBt3xp48PDwwYMAAjBw5EklJSRg2bBg++ugjh+uFYWpGYGAgzGaz3ddAamtrkZ+fr30NJDg4GO7u7nY1paWlKCoqapeviogIYmJikJmZiQMHDiAwMNBuvTP21JiIwGazOV4vSm9nOKGqqio5deqUnDp1SgDIpk2b5NSpU3Lp0iUR+ffWq8lkkszMTCksLJR58+Y99NZrQECA/PTTT3Ly5EmZNGlSu90af+ONN8RkMsnBgweltLRUW/755x+txpl6SkhIkEOHDsmFCxekoKBA1q5dKy4uLrJ//36H66XThykvL++hv0BjwYIFIvLvreTExEQxm82i1+slNDRUCgsL7bZRXV0tMTEx4uvrK56enhIZGSmXL19uh27kkb8QZMeOHVqNM/W0aNEi6du3r3h4eEiPHj1k8uTJWpBEHKsXfgWDSBFeMxEpwjARKcIwESnCMBEpwjARKcIwESnCMBEpwjARKcIwESnCMBEpwjARKfL/zFuRjiAK8U4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mu = 300\n",
    "si = 10\n",
    "N = 100000\n",
    "values = np.random.normal(mu, si, N)\n",
    "\n",
    "plt.subplot(3,3,7)\n",
    "plt.hist(values, 100)\n",
    "plt.axvline(values.mean(), color='r')\n",
    "plt.xlim([60,340])\n",
    "plt.title(\"mu = 300\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4b65896c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'si = 5')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mu = 100\n",
    "si = 5\n",
    "N = 100000\n",
    "values = np.random.normal(mu, si, N)\n",
    "\n",
    "plt.subplot(3,3,3)\n",
    "plt.hist(values, 100)\n",
    "plt.axvline(values.mean(), color='r')\n",
    "plt.xlim([60,340])\n",
    "plt.title(\"si = 5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e5f19fff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'si = 10')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mu = 100\n",
    "si = 10\n",
    "N = 100000\n",
    "values = np.random.normal(mu, si, N)\n",
    "\n",
    "plt.subplot(3,3,6)\n",
    "plt.hist(values, 100)\n",
    "plt.axvline(values.mean(), color='r')\n",
    "plt.xlim([60,340])\n",
    "plt.title(\"si = 10\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "89f4b873",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mu = 100\n",
    "si = 20\n",
    "N = 100000\n",
    "values = np.random.normal(mu, si, N)\n",
    "\n",
    "plt.subplot(3,3,9)\n",
    "plt.hist(values, 100)\n",
    "plt.axvline(values.mean(), color='r')\n",
    "plt.xlim([60,340])\n",
    "plt.title(\"si = 20\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ca3a8e2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}