charlesmana/exercise_issue6.ipynb

## exercise_issue6.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5c8065ef",
   "metadata": {},
   "source": [
    "# exercise 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d004016d",
   "metadata": {},
   "source": [
    "    1.median adalah ukuran tendensi sentral yang mewakili nilai tengah suatu kumpulan data jika diurutkan dari yang kecil hingga yang terbesar. jika dataset memiliki jumlah observasi ganjil, median adalah nilai tengah. jika jumlah obsevasi dalam kumpulan data genap mediannya adalah rata-rata dari dua nilai tengah. nilai tengah hanyalah nilai yagn menempati posisi tengah dalam suatu kumpulan data, tidak harus menjadi median. misalnya pada dataset [2,5,3,7,4,1,9] mediannya adalah 7, sedangkan nilai tengahnya juga 7 karena menempati posisi tengah. namun pada set [2,3,4,5,6,7] mediannya adalah (4+5)/2 = 3,5 tetapi nilai tengahnya adalah 5.\n",
    "    2.rata-rata dan mode mungkin sama atau mungkin juga tidak sama untuk kumpulan data yang tidak diurutkan dan diurutkan. untuk mean atau rata-rata, pengurutan dataset tidak mengubah jumlah nalainya, sehingga mean tetap sama baik dataset dirurtkan atau tidak. untuk mode sendiri pengurutan kumpulan data dapat mengubah distribusi frekuensi nilai, yang berpotensi memengaruhi nilai mana yang paling sering muncul dan dengan demikian mengubah mode. namun, jika terdapat satu mode (satu nilai yang sering muncul), maka mode tersebut akan tetap sama apapun pengurutannya.\n",
    "    3.rentang hanyalah selisih antara nilai maksimum dan minimum dalam kumpulan data. pengurutan dataset tidak mengubah nilai maksimal dan minimum, jadi diurutkan atau tidak datasetnya, rangenya akan sama. oleh karena itu, pengurutan kumpulan data tidak diperlukan dan tidak mempengaruhi perhitungan rentang."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59155713",
   "metadata": {},
   "source": [
    "# exercise 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ec1996c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[34, 80, 42, 58, 34, 32, 24, 44, 77, 47, 18, 23, 28, 74, 66, 55, 79, 57, 43, 28, 21, 43, 24, 28, 27, 73, 66, 17, 33, 49, 66, 13, 66, 61, 17, 74, 21, 52, 57, 17, 17, 40, 45, 60, 19, 66, 80, 76, 54, 27, 46, 19, 15, 15, 22, 21, 24, 23, 11, 55, 60, 67, 63, 31, 67, 23, 49, 34, 43, 39, 78, 11, 25, 16, 75, 25, 72, 48, 78, 17, 74, 46, 20, 50, 58, 42, 16, 26, 44, 33, 51, 53, 11, 49, 40, 65, 51, 11, 35, 66]\n"
     ]
    }
   ],
   "source": [
    "import random as rnd\n",
    "x = [\n",
    "    rnd.randint(10, 80)\n",
    "    for i in range(0,100)\n",
    "]\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cbd5dc76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.hist(x, bins=30, color='blue', edgecolor='black')\n",
    "plt.xlabel('value')\n",
    "plt.ylabel('frequency')\n",
    "plt.title('histogram of random data by Charnadi Lesmana')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc67120f",
   "metadata": {},
   "source": [
    "saya memilih bins=30 untuk membagi data menjadi 30 bins dengan jarak yang sama agar tampilan histogram tidak jelek dan informasi detailnya mudah dibaca. anda dapat menyesuaikan jumlah bin berdasarkan preferensi dan distribusi data anda. jika anda memiliki lebih banyak titik data atau jika ingin informasi lebih detail tentang distribusinya, dapat mempertimbangkan untuk menambah jumlah bin. sebaliknya, jika anda memiliki titik data yang lebih sedikit atau ingin gambaran distribusi yang lebih luas, sehingga dapat mempertimbangkan untuk mengurangi jumlah bin."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51465a88",
   "metadata": {},
   "source": [
    "# exercise 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5450b48c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "N= 1000\n",
      "xmin = 20\n",
      "xmax = 60\n",
      "mean = 40.165\n"
     ]
    }
   ],
   "source": [
    "import statistics as stat\n",
    "import random as rnd\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",
    "    \n",
    "print('N=', len(x))\n",
    "print('xmin =', min(x))\n",
    "print('xmax =', max(x))\n",
    "print('mean =', stat.mean (x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "992b031e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PcSYGAABYiTMxAIAvBZvPJkmcUeoKZ2IAAICVKDEAAMBKlBgAAGAlSgwAALASJQYAAFiJEgMAAKxEiQEAAFaixAAAACtRYgAAgJUoMQAAwEqUGAAAYCVKDAAAsBIlBgAAWIkSAwAArESJAQAAVqLEAAAAK1FiAACAlSgxAADASo6XmHPnzunxxx9Xbm6uevfurZtuuklPPvmkwuFw5BhjjAKBgPx+v3r37q38/HwdPnzY6VEAAICLOV5iVq9erRdffFEVFRV67733tGbNGv3kJz9ReXl55Jg1a9Zo7dq1qqioUG1trXw+nyZPnqzTp087PQ4AAHApx0vMf//3f+uuu+7SnXfeqa9+9av6zne+o4KCAu3bt0/S+bMwZWVlWrFihWbOnKmhQ4dq06ZNamlpUWVlpdPjAAAAl3K8xIwbN06/+93vdOTIEUnS22+/rddff11/+7d/K0mqq6tTfX29CgoKIvfxer0aP368ampqnB4HAAC4VJLTD7h8+XI1NTVp8ODBSkxMVHt7u5555hndd999kqT6+npJUlZWVtT9srKydOzYsS4fMxgMKhgMRq43NzdLkrwJRomJxukI1403wUT9ais35HBDBskdOdyQQSJHPHFDBskdOZLCzs7ueIn5+c9/rs2bN6uyslJDhgzRwYMHVVhYKL/fr3nz5kWO83g8UfczxnRa61BaWqpVq1Z1Wn98RFipqe3OBoiBp0aFr3yQBdyQww0ZJHfkcEMGiRzxxA0ZJLtztLSENdvBx3O8xDz66KN67LHHdO+990qShg0bpmPHjqm0tFTz5s2Tz+eTdP6MTHZ2duR+DQ0Nnc7OdCguLlZRUVHkenNzs3JycvT0Wwk6l5zodITrxptg9NSosFbuS1Aw3HWBs4Ebcrghg+SOHG7IIJEjnrghg+SOHEkhZ1/F4niJaWlpUUJC9JCJiYmRt1jn5ubK5/OpqqpKI0aMkCS1tbWpurpaq1ev7vIxvV6vvF5vp/Vg2KNz7XY+kRcKhj0KkiMuuCGD5I4cbsggkSOeuCGDZHeOdofLl+MlZvr06XrmmWc0cOBADRkyRG+99ZbWrl2r73//+5LOfxupsLBQJSUlysvLU15enkpKSpSamqrZs508yQQAANzM8RJTXl6ulStXauHChWpoaJDf79eCBQv0T//0T5Fjli1bptbWVi1cuFCNjY0aPXq0du3apbS0NKfHAQAALuV4iUlLS1NZWZnKysoueYzH41EgEFAgEHD6twcAAF8SfHYSAACwEiUGAABYiRIDAACsRIkBAABWosQAAAArUWIAAICVKDEAAMBKlBgAAGAlSgwAALASJQYAAFiJEgMAAKxEiQEAAFaixAAAACtRYgAAgJUoMQAAwEqUGAAAYCVKDAAAsBIlBgAAWIkSAwAArESJAQAAVqLEAAAAK1FiAACAlSgxAADASpQYAABgJUoMAACwEiUGAABYiRIDAACsRIkBAABWosQAAAArUWIAAICVKDEAAMBKlBgAAGAlSgwAALASJQYAAFiJEgMAAKxEiQEAAFaixAAAACtRYgAAgJUoMQAAwEqUGAAAYCVKDAAAsBIlBgAAWKlHSszJkyf1wAMPKDMzU6mpqbrtttu0f//+yO3GGAUCAfn9fvXu3Vv5+fk6fPhwT4wCAABcyvES09jYqLFjxyo5OVmvvPKK/vSnP+m5557TDTfcEDlmzZo1Wrt2rSoqKlRbWyufz6fJkyfr9OnTTo8DAABcKsnpB1y9erVycnK0YcOGyNpXv/rVyH8bY1RWVqYVK1Zo5syZkqRNmzYpKytLlZWVWrBggdMjAQAAF3K8xOzYsUNTpkzRPffco+rqan3lK1/RwoUL9cMf/lCSVFdXp/r6ehUUFETu4/V6NX78eNXU1HRZYoLBoILBYOR6c3Pz+fslGCUmGqcjXDfeBBP1q63ckMMNGSR35HBDBokc8cQNGSR35EgKOzu7xxjj6COmpKRIkoqKinTPPffozTffVGFhoX76059q7ty5qqmp0dixY3Xy5En5/f7I/R566CEdO3ZMO3fu7PSYgUBAq1at6rReWVmp1NRUJ8cHAAA9pKWlRbNnz1ZTU5P69u37hR/P8TMx4XBYo0aNUklJiSRpxIgROnz4sF544QXNnTs3cpzH44m6nzGm01qH4uJiFRUVRa43NzcrJydHT7+VoHPJiU5HuG68CUZPjQpr5b4EBcNdZ7eBG3K4IYPkjhxuyCCRI564IYPkjhxJIWdfiut4icnOztZf//VfR63dcsst+q//+i9Jks/nkyTV19crOzs7ckxDQ4OysrK6fEyv1yuv19tpPRj26Fy7nU/khYJhj4LkiAtuyCC5I4cbMkjkiCduyCDZnaPd4fLl+LuTxo4dq/fffz9q7ciRIxo0aJAkKTc3Vz6fT1VVVZHb29raVF1drTFjxjg9DgAAcCnHz8Q8/PDDGjNmjEpKSjRr1iy9+eabWr9+vdavXy/p/LeRCgsLVVJSory8POXl5amkpESpqamaPXu20+MAAACXcrzE3H777dq2bZuKi4v15JNPKjc3V2VlZbr//vsjxyxbtkytra1auHChGhsbNXr0aO3atUtpaWlOjwMAAFzK8RIjSdOmTdO0adMuebvH41EgEFAgEOiJ3x4AAHwJ8NlJAADASpQYAABgJUoMAACwEiUGAABYiRIDAACsRIkBAABWosQAAAArUWIAAICVKDEAAMBKlBgAAGAlSgwAALASJQYAAFiJEgMAAKxEiQEAAFaixAAAACtRYgAAgJUoMQAAwEqUGAAAYCVKDAAAsBIlBgAAWIkSAwAArESJAQAAVqLEAAAAK1FiAACAlSgxAADASpQYAABgJUoMAACwEiUGAABYiRIDAACsRIkBAABWosQAAAArUWIAAICVKDEAAMBKlBgAAGAlSgwAALASJQYAAFiJEgMAAKxEiQEAAFaixAAAACtRYgAAgJUoMQAAwEo9XmJKS0vl8XhUWFgYWTPGKBAIyO/3q3fv3srPz9fhw4d7ehQAAOAiPVpiamtrtX79et16661R62vWrNHatWtVUVGh2tpa+Xw+TZ48WadPn+7JcQAAgIv0WIk5c+aM7r//fv3sZz9Tv379IuvGGJWVlWnFihWaOXOmhg4dqk2bNqmlpUWVlZU9NQ4AAHCZpJ564EWLFunOO+/UpEmT9PTTT0fW6+rqVF9fr4KCgsia1+vV+PHjVVNTowULFnR6rGAwqGAwGLne3Nx8/n4JRomJpqci9Dhvgon61VZuyOGGDJI7crghg0SOeOKGDJI7ciSFnZ29R0rMli1bdODAAdXW1na6rb6+XpKUlZUVtZ6VlaVjx451+XilpaVatWpVp/XHR4SVmtruwMSx9dSocKxHcIQbcrghg+SOHG7IIJEjnrghg2R3jpaWsGY7+HiOl5gTJ07oxz/+sXbt2qWUlJRLHufxeKKuG2M6rXUoLi5WUVFR5Hpzc7NycnL09FsJOpec6MzgMeBNMHpqVFgr9yUoGO46uw3ckMMNGSR35HBDBokc8cQNGSR35EgKOfsqFsdLzP79+9XQ0KCRI0dG1trb27V3715VVFTo/fffl3T+jEx2dnbkmIaGhk5nZzp4vV55vd5O68GwR+fa7XwiLxQMexQkR1xwQwbJHTnckEEiRzxxQwbJ7hztDpcvx1/YO3HiRL3zzjs6ePBg5DJq1Cjdf//9OnjwoG666Sb5fD5VVVVF7tPW1qbq6mqNGTPG6XEAAIBLOX4mJi0tTUOHDo1a69OnjzIzMyPrhYWFKikpUV5envLy8lRSUqLU1FTNnu3kd8oAAICb9di7ky5n2bJlam1t1cKFC9XY2KjRo0dr165dSktLi8U4AADAQtelxOzZsyfqusfjUSAQUCAQuB6/PQAAcCE+OwkAAFiJEgMAAKxEiQEAAFaixAAAACtRYgAAgJUoMQAAwEqUGAAAYCVKDAAAsBIlBgAAWIkSAwAArESJAQAAVqLEAAAAK1FiAACAlSgxAADASpQYAABgJUoMAACwEiUGAABYiRIDAACsRIkBAABWosQAAAArUWIAAICVKDEAAMBKlBgAAGAlSgwAALASJQYAAFiJEgMAAKxEiQEAAFaixAAAACtRYgAAgJUoMQAAwEqUGAAAYCVKDAAAsBIlBgAAWIkSAwAArESJAQAAVqLEAAAAK1FiAACAlSgxAADASpQYAABgJUoMAACwEiUGAABYyfESU1paqttvv11paWnq37+/7r77br3//vtRxxhjFAgE5Pf71bt3b+Xn5+vw4cNOjwIAAFzM8RJTXV2tRYsW6Y033lBVVZXOnTungoICnT17NnLMmjVrtHbtWlVUVKi2tlY+n0+TJ0/W6dOnnR4HAAC4VJLTD/jb3/426vqGDRvUv39/7d+/X9/61rdkjFFZWZlWrFihmTNnSpI2bdqkrKwsVVZWasGCBU6PBAAAXMjxEnOxpqYmSVJGRoYkqa6uTvX19SooKIgc4/V6NX78eNXU1HRZYoLBoILBYOR6c3Pz+fslGCUmmp4cv0d5E0zUr7ZyQw43ZJDckcMNGSRyxBM3ZJDckSMp7OzsHmNMj/1pGGN01113qbGxUa+99pokqaamRmPHjtXJkyfl9/sjxz700EM6duyYdu7c2elxAoGAVq1a1Wm9srJSqampPTU+AABwUEtLi2bPnq2mpib17dv3Cz9ej56JWbx4sQ4dOqTXX3+9020ejyfqujGm01qH4uJiFRUVRa43NzcrJydHT7+VoHPJic4OfR15E4yeGhXWyn0JCoa7zm4DN+RwQwbJHTnckEEiRzxxQwbJHTmSQs6+FLfHSsySJUu0Y8cO7d27VwMGDIis+3w+SVJ9fb2ys7Mj6w0NDcrKyurysbxer7xeb6f1YNijc+12PpEXCoY9CpIjLrghg+SOHG7IIJEjnrghg2R3jnaHy5fj704yxmjx4sXaunWrfv/73ys3Nzfq9tzcXPl8PlVVVUXW2traVF1drTFjxjg9DgAAcCnHz8QsWrRIlZWV+sUvfqG0tDTV19dLktLT09W7d295PB4VFhaqpKREeXl5ysvLU0lJiVJTUzV79mynxwEAAC7leIl54YUXJEn5+flR6xs2bND3vvc9SdKyZcvU2tqqhQsXqrGxUaNHj9auXbuUlpbm9DgAAMClHC8xV/NmJ4/Ho0AgoEAg4PRvDwAAviT47CQAAGAlSgwAALASJQYAAFiJEgMAAKxEiQEAAFaixAAAACtRYgAAgJUoMQAAwEqUGAAAYCVKDAAAsBIlBgAAWIkSAwAArESJAQAAVqLEAAAAK1FiAACAlSgxAADASpQYAABgJUoMAACwEiUGAABYiRIDAACsRIkBAABWosQAAAArUWIAAICVKDEAAMBKlBgAAGAlSgwAALASJQYAAFiJEgMAAKxEiQEAAFaixAAAACtRYgAAgJUoMQAAwEqUGAAAYCVKDAAAsBIlBgAAWIkSAwAArESJAQAAVqLEAAAAK1FiAACAlSgxAADASpQYAABgpZiWmOeff165ublKSUnRyJEj9dprr8VyHAAAYJGYlZif//znKiws1IoVK/TWW2/pm9/8pqZOnarjx4/HaiQAAGCRmJWYtWvXav78+frBD36gW265RWVlZcrJydELL7wQq5EAAIBFkmLxm7a1tWn//v167LHHotYLCgpUU1PT6fhgMKhgMBi53tTUJElKCp3t2UF7WFLYqKUlrKRQgtrDnliPc83ckMMNGSR35HBDBokc8cQNGSR35Oj4um2MceYBTQycPHnSSDJ/+MMfotafeeYZc/PNN3c6/oknnjCSuHDhwoULFy4uuHz44YeO9ImYnInp4PFEN0ljTKc1SSouLlZRUVHk+v/93/9p0KBBOn78uNLT03t8zp7S3NysnJwcnThxQn379o31ONfMDTnckEFyRw43ZJDIEU/ckEFyR46mpiYNHDhQGRkZjjxeTErMjTfeqMTERNXX10etNzQ0KCsrq9PxXq9XXq+303p6erq1T+SF+vbtS4444YYMkjtyuCGDRI544oYMkjtyJCQ485LcmLywt1evXho5cqSqqqqi1quqqjRmzJhYjAQAACwTs28nFRUVac6cORo1apTuuOMOrV+/XsePH9ePfvSjWI0EAAAsErMS893vfld//vOf9eSTT+rUqVMaOnSofvOb32jQoEFXvK/X69UTTzzR5beYbEKO+OGGDJI7crghg0SOeOKGDJI7cjidwWOMU+9zAgAAuH747CQAAGAlSgwAALASJQYAAFiJEgMAAKwUtyWmtLRUt99+u9LS0tS/f3/dfffdev/996OOMcYoEAjI7/erd+/eys/P1+HDh2M0cdeuJseFFixYII/Ho7Kysus35FW4mhxnzpzR4sWLNWDAAPXu3Vu33HJLXH2g5wsvvKBbb7018oOi7rjjDr3yyiuSpFAopOXLl2vYsGHq06eP/H6/5s6dq08//TTGU3d2uRwd3nvvPc2YMUPp6elKS0vTN77xjbj+hPjS0lJ5PB4VFhZG1mzY3xfrKseF4nV/X6irDPG+tyUpEAjI4/FEXXw+nyS79vflcnSwYX+fPHlSDzzwgDIzM5WamqrbbrtN+/fvj9zu1P6O2xJTXV2tRYsW6Y033lBVVZXOnTungoICnT37+Yc+rlmzRmvXrlVFRYVqa2vl8/k0efJknT59OoaTR7uaHB22b9+uP/7xj/L7/TGY9PKuJsfDDz+s3/72t9q8ebPee+89Pfzww1qyZIl+8YtfxHDyzw0YMEDPPvus9u3bp3379unb3/627rrrLh0+fFgtLS06cOCAVq5cqQMHDmjr1q06cuSIZsyYEeuxO7lcDkn68MMPNW7cOA0ePFh79uzR22+/rZUrVyolJSXGk3ettrZW69ev16233hq1bsP+vtClcnSI5/3d4VIZ4n1vdxgyZIhOnToVubzzzjuSZNX+li6dQ7Jjfzc2Nmrs2LFKTk7WK6+8oj/96U967rnndMMNN0SOcWx/O/IJTNdBQ0ODkWSqq6uNMcaEw2Hj8/nMs88+Gznms88+M+np6ebFF1+M1ZhXdHGODp988on5yle+Yt59910zaNAgs27dutgMeJW6yjFkyBDz5JNPRh33N3/zN+bxxx+/3uNdtX79+pl//dd/7fK2N99800gyx44du85Tdd+FOb773e+aBx54IMYTXZ3Tp0+bvLw8U1VVZcaPH29+/OMfG2Ps29+XytHBhv19uQw27O0nnnjCDB8+/KqPj9f9faUcNuzv5cuXm3Hjxl3ydif3d9yeiblYU1OTJEU+NKqurk719fUqKCiIHOP1ejV+/HjV1NTEZMarcXEOSQqHw5ozZ44effRRDRkyJFajdUtXOcaNG6cdO3bo5MmTMsZo9+7dOnLkiKZMmRKrMS+pvb1dW7Zs0dmzZ3XHHXd0eUxTU5M8Hk/Uvx7izcU5wuGwfv3rX+vmm2/WlClT1L9/f40ePVrbt2+P9ahdWrRoke68805NmjQpat22/X2pHJI9+/tyGWzZ20ePHpXf71dubq7uvfdeffTRR5c8Np7396Vy2LK/d+zYoVGjRumee+5R//79NWLECP3sZz+L3O7o/r7GonVdhcNhM3369Khm94c//MFIMidPnow69oc//KEpKCi43iNela5yGGNMSUmJmTx5sgmHw8YYE7f/UutwqRzBYNDMnTvXSDJJSUmmV69e5t/+7d9iNGXXDh06ZPr06WMSExNNenq6+fWvf93lca2trWbkyJHm/vvvv84TXp1L5Th16pSRZFJTU83atWvNW2+9ZUpLS43H4zF79uyJ8dTR/uM//sMMHTrUtLa2GmNM1L/+bdrfl8thjB37+0oZbNjbv/nNb8x//ud/mkOHDkXOJmVlZZn/+Z//6XRsPO/vy+WwZX97vV7j9XpNcXGxOXDggHnxxRdNSkqK2bRpkzHG2f1tRYlZuHChGTRokDlx4kRkreMP4dNPP4069gc/+IGZMmXK9R7xqnSVY9++fSYrKyvqyYzH/8ldqKscxhjzk5/8xNx8881mx44d5u233zbl5eXmL/7iL0xVVVWMJu0sGAyao0ePmtraWvPYY4+ZG2+80Rw+fDjqmLa2NnPXXXeZESNGmKamphhNenmXynHy5Ekjydx3331Rx0+fPt3ce++9MZq2s+PHj5v+/fubgwcPRta6KjHxvr+vlMOG/X2lDMbYsbcvdubMGZOVlWWee+65qHUb9veFLsxhy/5OTk42d9xxR9TakiVLzDe+8Q1jjLP7O+5LzOLFi82AAQPMRx99FLX+4YcfGknmwIEDUeszZswwc+fOvZ4jXpVL5Vi3bp3xeDwmMTExcpFkEhISzKBBg2Iz7GVcKkdLS4tJTk42v/rVr6LW58+fH1dfdC42ceJE89BDD0Wut7W1mbvvvtvceuutXf4LLl515AgGgyYpKck89dRTUbcvW7bMjBkzJkbTdbZt2zYjqdPf+4698MEHH1ixv6+U45//+Z/jfn9fKcOZM2es3NvGGDNp0iTzox/9KHLd1v3dkcOW/T1w4EAzf/78qLXnn3/e+P1+Y4yzX79j9gGQV2KM0ZIlS7Rt2zbt2bNHubm5Ubfn5ubK5/OpqqpKI0aMkCS1tbWpurpaq1evjsXIXbpSjjlz5nT6HvSUKVM0Z84cPfjgg9dz1Mu6Uo5QKKRQKKSEhOiXWSUmJiocDl/PUbvFGKNgMCjpfIZZs2bp6NGj2r17tzIzM2M83dXryNGrVy/dfvvtnd7+fuTIkav6cNXrZeLEiVHvuJCkBx98UIMHD9by5ct10003WbG/r5QjOzu70+tG4m1/XylDe3u7lXs7GAzqvffe0ze/+U1J9u7vC3PYsr/Hjh172Rkd/fp9zVWrh/393/+9SU9PN3v27DGnTp2KXFpaWiLHPPvssyY9Pd1s3brVvPPOO+a+++4z2dnZprm5OYaTR7uaHBeLt9PNxlxdjvHjx5shQ4aY3bt3m48++shs2LDBpKSkmOeffz6Gk3+uuLjY7N2719TV1ZlDhw6Zf/zHfzQJCQlm165dJhQKmRkzZpgBAwaYgwcPRmUMBoOxHj3K5XIYY8zWrVtNcnKyWb9+vTl69KgpLy83iYmJ5rXXXovx5Jd38bcwbNjfXenq3UkXisf9fbGLM8T73jbGmKVLl5o9e/aYjz76yLzxxhtm2rRpJi0tzXz88cdW7e/L5TDGjv395ptvmqSkJPPMM8+Yo0ePmn//9383qampZvPmzZFjnNrfcVtiJHV52bBhQ+SYcDhsnnjiCePz+YzX6zXf+ta3zDvvvBO7obtwNTkuFo//k7uaHKdOnTLf+973jN/vNykpKeZrX/uaee655yIvaIy173//+2bQoEGmV69e5i//8i/NxIkTI1/46+rqLplx9+7dsR38IpfL0eGll14yf/VXf2VSUlLM8OHDzfbt22M07dW7+AunDfu7K24sMfG+t405/9bj7Oxsk5ycbPx+v5k5c2bk9W427e/L5ehgw/7+5S9/aYYOHWq8Xq8ZPHiwWb9+fdTtTu1vjzHGdO/cDQAAQOxZ83NiAAAALkSJAQAAVqLEAAAAK1FiAACAlSgxAADASpQYAABgJUoMAACwEiUGAABYiRIDAACsRIkBAABWosQAAAArUWIAAICV/h+AHGHTgkn4pgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\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": 16,
   "id": "41b5f9c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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": "markdown",
   "id": "d5024e83",
   "metadata": {},
   "source": [
    "n = b - a + 1 = 60 - 20 + 1 = 41/n\n",
    "avarage value = N*1/n* lebar bins = 1000*1/41*4 = 97,5609"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae991556",
   "metadata": {},
   "source": [
    "# exercise 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "271403bc",
   "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; 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": "markdown",
   "id": "6f3ec8e4",
   "metadata": {},
   "source": [
    "jika memilih bin = 6, maka nilainya = 1000"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebc6b5ec",
   "metadata": {},
   "source": [
    "# exercise 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e4c6bef4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "mu = 100\n",
    "si = 5\n",
    "N = 100000\n",
    "values1 =  np.random.normal(mu, si, N)\n",
    "\n",
    "plt.hist(values1, 100)\n",
    "plt.axvline(values1.mean(), color='r')\n",
    "plt.xlim([20,350])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1ae1796e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "mu = 200\n",
    "si = 10\n",
    "N = 100000\n",
    "values2 = np.random.normal(mu, si, N)\n",
    "\n",
    "plt.hist(values2, 100)\n",
    "plt.axvline(values2.mean(), color='r')\n",
    "plt.xlim([20,350])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cd0a8a4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 26,
     "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": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "mu = 300\n",
    "si = 20\n",
    "N = 100000\n",
    "values1 = np.random.normal(mu, si, N)\n",
    "\n",
    "plt.hist(values1, 100)\n",
    "plt.axvline(values1.mean(), color='r')\n",
    "plt.xlim([20,350])\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "758470e6",
   "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
}