Issues6zam.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0a348b1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#EXERCISE_1\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",
    "\n",
    "# 1.The median is a measure of central tendency that represents the middle value of a dataset when it is ordered from smallest to largest. If the dataset has an odd number of observations, the median is the middle value. If the dataset has an even number of observations, the median is the average of the two middle values.\n",
    "#   The middle value is simply the value that occupies the middle position in a dataset. It does not necessarily have to be the median. For example, in the dataset [1, 3, 5, 7, 9], the median is 5, while the middle value is also 5 because it occupies the middle position. However, in the dataset [1, 2, 3, 4, 5, 6], the median is (3 + 4) / 2 = 3.5, but the middle value is 4.\n",
    "\n",
    "# 2.The mean (average) and mode (most frequent value) may or may not be the same for unsorted and sorted datasets.\n",
    "#   For the mean, sorting the dataset does not change the sum of the values, so the mean remains the same whether the dataset is sorted or not.\n",
    "#   For the mode, sorting the dataset may change the frequency distribution of the values, potentially affecting which value appears most frequently and thus changing the mode. However, if there is a single mode (one value that appears most frequently), it will remain the same regardless of sorting.\n",
    "\n",
    "# 3.It doesn't matter whether the dataset is sorted or not when calculating the range. The range is simply the difference between the maximum and minimum values in the dataset.\n",
    "#   Sorting the dataset does not change the maximum and minimum values, so whether the dataset is sorted or not, the range will be the same.\n",
    "#   Therefore, sorting the dataset is unnecessary and does not affect the calculation of the range."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c8643171",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[28, 72, 12, 74, 68, 64, 57, 75, 69, 57, 22, 27, 44, 76, 28, 31, 14, 12, 19, 65, 27, 47, 26, 59, 12, 49, 42, 62, 64, 77, 55, 33, 14, 79, 22, 78, 12, 71, 40, 11, 40, 63, 23, 35, 15, 56, 24, 19, 25, 79, 11, 46, 25, 75, 26, 76, 52, 38, 33, 46, 20, 46, 80, 50, 50, 48, 76, 12, 15, 72, 55, 47, 27, 14, 36, 40, 69, 74, 30, 38, 58, 25, 17, 50, 41, 32, 21, 20, 73, 52, 56, 31, 53, 73, 72, 38, 18, 75, 73, 31]\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#EXERCISE_2\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='red', edgecolor='black')\n",
    "plt.xlabel('Value')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Histogram of Random Data by zafran marvis')\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "# In this example, I've chosen bins=25 to divide the data into 25 equally spaced bins.\n",
    "# Because to make the histogram look didn't worst and the detailed information is easy to read.\n",
    "# You can adjust the number of bins based on your preference and the distribution of your data.\n",
    "# If you have more data points or if you want more detailed information about the distribution, you may consider increasing the number of bins.\n",
    "# Conversely, if you have fewer data points or if you want a broader overview of the distribution, you may consider decreasing the number of bins.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7d6b332f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#EXERCISE_3\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()\n",
    "\n",
    "# n = b - a + 1 = 60 - 20 + 1 = 41\n",
    "# Average Value = N * 1/n * the width of the bins = 1000 * 1/41 * 4 = 97,5609"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ab9ddaa4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#EXERCISE_4\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()\n",
    "\n",
    "# if we choose bin = 6, The value is 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "af23c84b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#EXERCISE_5\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\")\n",
    "\n",
    "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\")\n",
    "\n",
    "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\")\n",
    "\n",
    "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\")\n",
    "\n",
    "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\")\n",
    "\n",
    "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": "cb6baef0",
   "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
}