CriticalOlymp/tugas_stat.ipynb

## tugas_stat.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7d57ecf",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Exercise 1\n",
    "\n",
    "1.\tExplain the difference between median and middle value?\n",
    "Bayangkan kamu memiliki sekelompok anak yang ingin bermain sepak bola. Agar permainan adil, kamu perlu membagi mereka menjadi dua tim dengan jumlah pemain yang sama. Di sinilah konsep median dan nilai tengah dapat membantu.\n",
    "Median adalah nilai yang membagi kelompok data menjadi dua bagian sama besar. Dalam hal ini, median adalah jumlah anak yang tepat di tengah kelompok. Separuh anak akan berada di tim A, dan separuh lainnya di tim B. Sedangkan nilai tengah, di sisi lain, mengacu pada nilai yang terletak di tengah kelompok data, tanpa mempertimbangkan pembagian kelompok. Jika terdapat jumlah anak ganjil, nilai tengah dan median adalah sama.\n",
    "Namun, jika terdapat jumlah anak genap, nilai tengah akan menjadi rata-rata dari dua anak di tengah. Ini tidak ideal untuk membagi tim, karena akan ada satu anak yang tidak memiliki pasangan.\n",
    "\n",
    "2.\tAre mean and mode values always the same for unsorted and sorted dataset? Why?\n",
    "Tidak, nilai mean dan modus tidak selalu sama untuk data yang tidak diurutkan dan diurutkan. Mean (rata-rata), berfungsi untuk membagi semua nilai dengan jumlah data. Mengurutkan data tidak mengubah jumlah nilai, jadi mean tetap sama. Sedangkan Modus (nilai terbanyak), Bergantung pada urutan data. Mengubah urutan bisa saja mengubah nilai yang paling sering muncul, sehingga modus bisa berubah.\n",
    "\n",
    "3.\tIf range is calculate with last datapoint – first datapoint, should the dataset is sorted first or not? Why?\n",
    "Tidak perlu, hal ini dikarenakan penghitungan range cukup dengan mengurangkan data maksimum dengan data minimum. Jadi dalam pengerjaanya kita hanya dituntut untuk meneliti nilai mana yang maksimum dan minimum yang kemudian akan dikurangkan.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7e273769",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Exercise 2\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "11eea2e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = [12,78,35,52,58,50,40,26,39,42,54,80,21,45,23,14,23,42,38,29,18,48,27,42,20,16,42,34,70,41,52,58,67,57,16,75,80,44,45,47,24,32,15,22,69,44,38,59,44,59,38,75,53,32,52,46,58,15,66,43,37,33,37,66,50,57,80,56,43,49,80,50,72,60,37,80,21,70,67,55,69,10,45,32,33,26,45,52,76,20,53,53,11,77,63,49,52,10,58,29]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5be04482",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10, 10, 11, 12, 14, 15, 15, 16, 16, 18, 20, 20, 21, 21, 22, 23, 23, 24, 26, 26, 27, 29, 29, 32, 32, 32, 33, 33, 34, 35, 37, 37, 37, 38, 38, 38, 39, 40, 41, 42, 42, 42, 42, 43, 43, 44, 44, 44, 45, 45, 45, 45, 46, 47, 48, 49, 49, 50, 50, 50, 52, 52, 52, 52, 52, 53, 53, 53, 54, 55, 56, 57, 57, 58, 58, 58, 58, 59, 59, 60, 63, 66, 66, 67, 67, 69, 69, 70, 70, 72, 75, 75, 76, 77, 78, 80, 80, 80, 80, 80]\n"
     ]
    }
   ],
   "source": [
    "y = sorted(x)\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8f35bdae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(x, bins=7,  range=[10, 80])\n",
    "plt.yticks(range(0, 25, 5))\n",
    "plt.xticks(range(10, 90, 10))\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "168dc09a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Exercise 3\n",
    "\n",
    "1.\tWhat is n in this case?\n",
    "N dalam kasus ini merupakan sebuah angka total yang mungkin keluar dalam percobaan peluang atau dapat disebut dengan “Semesta”\n",
    "2.\tExplain how the average value about 100 can be found from 1/n?\n",
    "Average Value = N * 1/n * the width of the bins = 1000 * 1/41 * 4 = 97,5609"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c45f9a0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import random as rnd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7683dae6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1449ed02",
   "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
}