Issues6.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a9e390fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Exercise 1\n",
    "#1.\n",
    "#Median:  median is the central value in a sorted dataset. It represents the value that has half the data points below it and half the data points above it. If there are an even number of data points, the median is the average of the two middle values.\n",
    "#Middle Value: The middle value, simply put, is the value located in the middle of a dataset, regardless of sorting. If there are an odd number of data points, the middle value is the median. However, if there are an even number of data points, there isn't a single unique \"middle value.\"\n",
    "#2.\n",
    "#Mean (Average): The mean is the sum of all values in a dataset divided by the number of values. It is calculated the same way regardless of whether the data is sorted or unsorted.\n",
    "#Mode: The mode is the most frequently occurring value in a dataset. Sorting the data can sometimes change the mode, although it often remains the same.\n",
    "#3.\n",
    "#No, sorting the dataset is not necessary when calculating the range as the difference between the last and first datapoints. The range is a measure of dispersion and is simply the numerical difference between the maximum (last datapoint) and minimum (first datapoint) values in the dataset.\n",
    "#Sorting the dataset would only be necessary if you were interested in other aspects of the data, such as finding percentiles, quartiles, or analyzing patterns. For calculating the range specifically, the order of the data points does not affect the result, and you can directly subtract the first datapoint from the last one without sorting the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4173a6af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[23, 51, 42, 54, 16, 22, 47, 13, 31, 19, 63, 52, 63, 40, 41, 36, 40, 40, 57, 20, 64, 42, 48, 28, 54, 51, 40, 71, 51, 33, 51, 57, 32, 21, 43, 49, 75, 72, 56, 40, 80, 62, 29, 19, 31, 75, 72, 66, 12, 65, 59, 27, 43, 17, 55, 33, 80, 33, 18, 29, 46, 28, 41, 30, 11, 18, 68, 55, 48, 76, 33, 30, 18, 67, 46, 23, 39, 16, 12, 25, 21, 36, 29, 43, 12, 41, 39, 43, 22, 80, 12, 42, 70, 62, 30, 29, 41, 78, 23, 15]\n"
     ]
    },
    {
     "data": {
      "image/png": "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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='green', edgecolor='black')\n",
    "plt.xlabel('Value')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Random Data Histogram ')\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "#Dalam contoh ini, saya memilih bins=25 untuk membagi data menjadi 25 nampan dengan jarak yang sama.\n",
    "# Karena agar tampilan histogram tidak jelek dan informasi detailnya mudah dibaca.\n",
    "# Anda dapat menyesuaikan jumlah bin berdasarkan preferensi dan distribusi data Anda.\n",
    "# Jika Anda memiliki lebih banyak titik data atau jika Anda menginginkan informasi lebih rinci tentang distribusinya, Anda dapat mempertimbangkan untuk menambah jumlah bin.\n",
    "# Sebaliknya, jika Anda memiliki titik data yang lebih sedikit atau ingin gambaran distribusi yang lebih luas, Anda dapat mempertimbangkan untuk mengurangi jumlah bin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "91ae4483",
   "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": 29,
   "id": "3d4fc41f",
   "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; x = []\n",
    "for i in range(N):\n",
    "    xi = rnd.randint(a, b)\n",
    "    x.append(xi)\n",
    "plt.hist(x, bins=6)\n",
    "plt.xticks(range(1, 7,1))\n",
    "plt.xlim([1, 6])\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "cc9cbf25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#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()"
   ]
  }
 ],
 "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
}