exercise_6.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3f6d6c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# LATIHAN 1\n",
    "\n",
    "1.\tPerbedaan Nilai Median dan Nilai Tengah\n",
    "•\tNilai Median:\n",
    "Definisi: Nilai median adalah nilai yang membagi kumpulan data menjadi dua bagian yang sama besar, dengan setengah data lebih kecil dan setengahnya lebih besar dari nilai median.\n",
    "Cara Menghitung:\n",
    "•\tUrutkan data dari terkecil ke terbesar.\n",
    "•\tJika jumlah data ganjil, nilai median adalah nilai tengah.\n",
    "•\tJika jumlah data genap, nilai median adalah rata-rata dari dua nilai tengah.\n",
    "Contoh:\n",
    "Misalkan kita memiliki data: 2, 4, 6, 8, 10.\n",
    "Nilai median: 6 (nilai tengah)\n",
    "•\tNilai Tengah:\n",
    "Definisi: Nilai tengah adalah nilai yang diperoleh dengan menjumlahkan semua nilai dalam kumpulan data dan membaginya dengan jumlah data.\n",
    "Cara Menghitung:\n",
    "•\tJumlahkan semua nilai dalam kumpulan data.\n",
    "•\tBagi total nilai dengan jumlah data.\n",
    "Contoh:\n",
    "Misalkan kita memiliki data: 2, 4, 6, 8, 10.\n",
    "Nilai tengah: (2 + 4 + 6 + 8 + 10) / 5 = 6\n",
    "Perbedaan:\n",
    "Nilai median:\n",
    "Berfokus pada pembagian data menjadi dua bagian yang sama besar. Tidak terpengaruh oleh nilai outlier (nilai yang sangat besar atau kecil). Serta lebih mudah dihitung untuk kumpulan data yang besar.\n",
    "Nilai tengah:\n",
    "Berfokus pada nilai rata-rata dari semua nilai dalam dataset. Terpengaruh oleh nilai outlier. Dan lebih mudah diinterpretasikan dalam beberapa situasi.\n",
    "Kesimpulan:\n",
    "Nilai median dan nilai tengah adalah dua cara untuk mengukur nilai tengah dalam suatu kumpulan data. Nilai median lebih stabil dan tidak terpengaruh oleh nilai outlier, sedangkan nilai tengah lebih mudah diinterpretasikan dalam beberapa situasi. Pilihan metode yang tepat tergantung pada kebutuhan analisis dan jenis data yang digunakan.\n",
    "\n",
    "2.\tNilai Rata-rata, Median, dan Modus pada Himpunan Data\n",
    "Nilai rata-rata (mean), median, dan modus adalah tiga cara untuk mengukur nilai tengah dalam suatu himpunan data. Nilai rata-rata dihitung dengan menjumlahkan semua nilai dan membaginya dengan jumlah data. Median adalah nilai yang membagi data menjadi dua bagian yang sama besar, dengan setengah data lebih kecil dan setengahnya lebih besar. Modus adalah nilai yang paling sering muncul dalam data.\n",
    "Pada himpunan data yang tidak diurutkan:\t\n",
    "•\tNilai rata-rata: Tidak terpengaruh oleh urutan data.\n",
    "•\tMedian: Sulit untuk dihitung secara manual, membutuhkan pengurutan data terlebih dahulu.\n",
    "•\tModus: Sulit untuk dihitung secara manual, membutuhkan penghitungan frekuensi setiap nilai terlebih dahulu.\n",
    "Pada himpunan data yang diurutkan:\n",
    "•\tNilai rata-rata: Tidak terpengaruh oleh urutan data.\n",
    "•\tMedian: Mudah dihitung, nilai tengah dalam data yang diurutkan.\n",
    "•\tModus: Mudah dihitung, nilai yang paling sering muncul dalam data yang diurutkan.\n",
    "Kesimpulan:\n",
    "Nilai rata-rata tidak terpengaruh oleh urutan data, baik pada himpunan data yang diurutkan maupun tidak diurutkan. Median dan modus mudah dihitung pada himpunan data yang diurutkan, tetapi sulit dihitung secara manual pada himpunan data yang tidak diurutkan.\n",
    "Contoh:\n",
    "Misalkan kita memiliki data: 2, 4, 6, 8, 10.\n",
    "Himpunan data yang tidak diurutkan:\n",
    "•\tNilai rata-rata: (2 + 4 + 6 + 8 + 10) / 5 = 6\n",
    "•\tMedian: Sulit dihitung secara manual, membutuhkan pengurutan data terlebih dahulu.\n",
    "•\tModus: Sulit dihitung secara manual, membutuhkan penghitungan frekuensi setiap nilai terlebih dahulu.\n",
    "Himpunan data yang diurutkan:\n",
    "•\tNilai rata-rata: (2 + 4 + 6 + 8 + 10) / 5 = 6\n",
    "•\tMedian: 6 (nilai tengah)\n",
    "•\tModus: 6 (nilai yang paling sering muncul)\n",
    "\n",
    "3.\tUntuk menghitung rentang (range) dengan rumus titik data terakhir dikurangi titik data pertama, dataset tidak perlu diurutkan terlebih dahulu.\n",
    "Alasan:\n",
    "Rentang hanya memperhitungkan nilai minimum dan maksimum dalam dataset. Urutan data tidak memengaruhi nilai minimum dan maksimum.\n",
    "Contoh:\n",
    "Misalkan dataset: 5, 3, 8, 1, 2.\n",
    "Rentang tanpa pengurutan: 8 - 1 = 7\n",
    "Rentang dengan pengurutan: 8 - 1 = 7\n",
    "Kesimpulan:\n",
    "Pengurutan data tidak diperlukan untuk menghitung rentang. Menghitung rentang dengan rumus titik data terakhir dikurangi titik data pertama akan menghasilkan nilai yang sama"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8691fc16",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[29, 24, 80, 80, 22, 19, 12, 73, 20, 80, 79, 62, 42, 32, 61, 19, 70, 12, 71, 76, 10, 60, 76, 52, 28, 57, 60, 34, 54, 10, 15, 56, 24, 42, 21, 71, 72, 74, 63, 27, 11, 37, 20, 79, 74, 66, 42, 44, 28, 48, 41, 36, 42, 37, 10, 63, 77, 46, 60, 61, 64, 80, 29, 68, 49, 36, 50, 33, 75, 14, 17, 10, 33, 62, 76, 15, 10, 49, 80, 75, 34, 58, 47, 19, 59, 75, 23, 60, 29, 62, 59, 51, 30, 77, 57, 38, 73, 74, 56, 55]\n"
     ]
    }
   ],
   "source": [
    "# LATIHAN 2\n",
    "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": 3,
   "id": "c6b7021b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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='purple', edgecolor='black')\n",
    "plt.xlabel('value')\n",
    "plt.ylabel('frequency')\n",
    "plt.title('histogram of random data by Yulia Valonia')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "478a766a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LATIHAN 3\n",
    "\n",
    "import numpy as np\n",
    "import statistics as stat\n",
    "\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",
    "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": "1ea5237e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LATIHAN 4\n",
    "\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",
    "    \n",
    "plt.hist(dice, bins=6)\n",
    "plt.grid()\n",
    "plt.show()\n",
    "\n",
    "# VALUE = 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2f05e9bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SSdKQIUN8xtPS0jRx4kRJ5/YITp06palTp+rYsWPq169fpXsEwcHBGjt2rE6dOqWhQ4dq2bJlFfYIHnjgAecqppEjR2rx4sV1mSMAAGiCahVi2CMAAACBgu9OAgAArkSIAQAArkSIAQAArkSI8RM+HA8AgIZFiKkEgQQAgMBHiAEAAK5EiAEAAK5EiAEAuAZv9+PHCDEAAMCVCDEAAMCVCDEAAMCVCDEAAMCVCDEAAMCVCDF+xFnzAHDx6KWoKUIMAABwJULMedgDAADAHQgxAADAlQgxAADAlQgxAADAlQgxAADAlQgxAADAlQgxAADAlQgxAADAlQgxAADAlQgxfsaH5QFA/aLPohwh5kfYMAAAcA9CDAAAcCVCDAAAcCVCDAAAcCVCzP/D+TAAALgLIaYeEIgAAKh/hBgAQMBgJxC1QYgBAACuRIgBAACuRIgBAACuRIgBAACuRIgBAACuRIipJ5xhDwBA/SLEAABchx1FSIQYSWwMAAC4ESEGAAC4EiGmHnGEBwBqjp6J2iLEAAAAV2r2IYbkDwCAOzX7EFPfCEkAANSPZh1iCBgA4F70cDTrENNQ2NAAAPC/gA8xzz33nOLj49WqVSv17t1b7733nl+WS7AAgMrVV9+tDj0ZdRHQIWb16tWaPn26Zs+erR07duimm27S7bffrv3791/UchtjY2EDBeAG9dV3gfoQZGbW2CtRlX79+um6667TkiVLnLEePXpo1KhRSk1N9aktKSlRSUmJc7+goEBdunTRgQMHFBUV5YwnzP2fOq1Lq9PF+v+e+6Ukqc/U/1ZxaKs6LWf3o7fW6XloHIWFhYqLi9Px48fl8Xgae3WAelcfffdCqurLNe279NWmpVZ91wJUSUmJtWzZ0tLT033GH3jgARs0aFCF+rlz55okbtzq5XbgwIGG+tUHGg19l1sg3WrSd4MVoL799ludPXtWMTExPuMxMTHKz8+vUD9r1izNmDHDuV9WVqajR4+qXbt2CgoK8ss6lafD2u5lBCrmc2FmpqKiIsXGxvpleUAgo+/WP+ZzYbXpuwEbYsqdvyGYWaUbR1hYmMLCwnzGLrnkknpZp6ioqCbxy1eupvPZtm2bXnzxRb377rvau3evWrdurZ49e+qRRx7RLbfcUqH+yy+/1MyZM7Vx40aVlpaqf//+euKJJ3TddddVqF21apUWLFigzz77TNHR0Ro7dqwee+wxRURE1Nt8aoq3kdDc0HcrmjhxojZt2qS9e/f6ZXk1mc9XX32lf//3f9eOHTv08ccfq6CgQGlpaZo4cWKl9Rs2bNCcOXP08ccfq3Xr1hoxYoQWLlyojh07+mWdq9NYfTdgT+xt3769WrZsWSH9Hz58uMJeAhrGK6+8oo8++ki//vWv9cYbb2jp0qUKCwvT0KFD9d///d8+tUeOHNFNN92kzz//XC+++KL+9re/qbi4WEOGDFFeXp5P7YoVK3T33Xerb9++eueddzR37lwtW7ZMY8aMacjpAc0efbdqc+bMUUZGRoO+5hdffKEVK1YoNDRU//Iv/1JtbU5Ojm6//XbFxMTojTfe0DPPPKMNGzZo6NChPuctNTl+eSO1nlx//fX2m9/8xmesR48e9vDDDzfK+hQUFJgkKygoaJTX97fazuebb76pMFZaWmqJiYl2+eWX+4z/9re/tZCQENu7d6/P67Vv397Gjh3r8/xOnTpZUlKSz/NXrFhhkmzdunX1Nh8AFdF361dt5nP27Fnn71u3bjVJlpaWVmlt37597eqrr7YzZ844Y5s3bzZJ9txzz130elelsX8+AXskRpJmzJihpUuX6sUXX9Snn36qBx98UPv379eUKVMaZX3CwsI0d+7cCodPqzNv3jwFBQVp165duvPOO+XxeBQdHa0ZM2aotLRUeXl5uu222xQZGanLLrtMCxcu9Hn+smXLFBQUVOEQ5qZNmxQUFKRNmzY12HwqOyTZsmVL9e7dWwcOHPAZz8jI0C233KKuXbs6Y1FRURozZozefPNNlZaWSpK2bNmiQ4cO6Ve/+pXP8++8805FRETUas+nLj8fAL6aQt+trSNHjig5OVlxcXEKCwtThw4ddOONN2rDhg1OzcSJE3XZZZdd9GvVZj4tWtTsv+iDBw9q69atGj9+vIKDfzhLZMCAAbryyivr9QhSY/fdgD4n5q677tJ3332n+fPn69ChQ0pISNC6det8/mNsSGFhYZo3b16dnjt27Fjdc889uu+++5SVlaWFCxfqzJkz2rBhg6ZOnaqZM2dq5cqV+v3vf69u3br57a0UM9PZs2crfaxly5b6wx/+IEkqLS31+eWvqdLSUr333nu65pprnLFTp07pn//8p0aPHl2hPjExUadOndKXX36pK6+8Urt373bGfywkJERXXXWV83hNXMzPB8A5Tanv1tT48eO1fft2/elPf9KVV16p48ePa/v27fruu+9qvayysjKVlZVV+Xh53/XXic+Squyj5WObN2/222udr7H7bkCHGEmaOnWqpk6d2tircdGSk5Ods/iHDRum9evXa/HixUpPT3f+sx8yZIjeeustrVixwm8hJicnRzfffHONavfs2VPrPY158+bpiy++0Ouvv+6MHTt2TGam6OjoCvXlY+XNofzPqmr9dRIdgJprKn23pjZv3qx7771XkydPdsZ+/vOf12lZ8+fP16OPPnrBuq5du/qtv12oj9YljLlFwIeYpmLEiBE+93v06KGPP/5Yt99+uzMWHBysbt26ad++fX573d69e2vr1q01qq3tZcRLly7Vn/70Jz300EOVbvDV7Wmc/1hVtf7cWwGAylx//fVatmyZ2rVrp2HDhql3794KCQmp07KSk5Mr9PvK1MfbL82xjxJiGsj5CTk0NFStW7dWq1atKowXFhb67XUjIiL005/+tEa1tXk7KS0tTffdd5+Sk5P15JNP+jzWtm1bBQUFVZr+jx49KumHf4927dpJOrcncf7VD0ePHq10zwIA/Gn16tV67LHHtHTpUs2ZM0cREREaPXq0Fi5cKK/XW6tleb3eGl3S7M9g8eM+er6m3kcD+sReyAk5518i9+2339bo+Tk5OQoJCanRraaHNtPS0nTvvfdqwoQJev755ytsjOHh4erWrZtyc3MrPDc3N1fh4eH6yU9+Iknq2bOnM/5jpaWl+uyzz5SQkFCjdQKAumrfvr0WLVqkvXv3at++fUpNTVV6enqVn8dSnfnz59eo315++eV+W//yPllVz23KfZQjMQGu/ByVXbt2qXv37s742rVra/R8f7+dtGzZMt1777265557tHTp0ir3JkaPHq1FixbpwIEDiouLkyQVFRUpPT1dI0eOdI769OvXT506ddKyZct01113Oc9/7bXXdOLECT4rBkCD6tKli1JSUvT3v/+9TifENsbbSZdeeqmuv/56LV++XDNnzlTLli0lnbv6My8vT9OnT/fbawWcRrmwO4Dk5OTYiBEjrFOnTibJMjIyfB4vKyuzuXPnWqdOnaxVq1Y2ePBg2717t09NcXGxpaSkWLt27ax169b2s5/9zPnOh/LvFjly5IjPcyZMmGBt2rSpsD6DBw+2a665xrlfWlpq3bt3ty5dutjKlSvtnXfeseTkZIuPjzdJlp2d7fP8xx9/3Pr06WMRERHWoUMH+/nPf26fffaZX+a0ZMkSa9GihV133XW2efNm+/DDD31uxcXFzvMPHz5snTp1sp49e1pGRoatW7fOBg0aZJGRkfbpp5/6vNbLL79skiw5Odmys7PtL3/5i11yySU2fPhwe+6556xnz54WGRlpkZGRdsMNN/h8dszF/nwANLz67ru1cfz4cbv22mvtySeftDfffNM2bdpkTz75pLVq1crGjRvn1E2YMMG6du1a6TLqs+8+//zz9uqrr9oTTzxhkuz++++3V1991V599VWf52ZnZ1twcLCNHj3asrKybMWKFRYXF2cJCQk+vbkm3NR3m32IWbdunc2ePdvWrFlT6ca0YMECi4yMtDVr1lhubq7ddddd1qlTJyssLHRqpkyZYpdeeqllZWXZ9u3b7eabb7ZevXpZaWnpRYcYM7PPP//ckpKSLCoqyjp06GDTpk2zt99+u9IQc+utt1paWprt3r3bdu7caXfccYd16dLFTpw4cdFzatu2bbVf1rVnzx6fdfniiy9s1KhRFhUVZa1bt7ahQ4fatm3bKv05rFy50hITEy00NNS8Xq898MADVlRUZGvXrrW3337b8vLyLC8vzx555BELCQlxNpiL/fkAaHj13Xdro7i42KZMmWKJiYkWFRVl4eHh1r17d5s7d66dPHnSqasuxNRn362u555v/fr1dsMNN1irVq0sOjrafvnLX1b6IaUX4qa+2+xDzI+dvzGVlZWZ1+u1BQsWOGPFxcXm8Xjs+eefN7NzKT4kJMRWrVrl1Bw8eNBatGhhmZmZDbbuVTl8+LBJspycHDNrGnNq27atLV26tEnMBWju6LvumFOg9l1O7K3Gnj17lJ+fr6SkJGcsLCxMgwcP1gcffCDp3JcinjlzxqcmNjZWCQkJTk1jKigokPTD1UBuntPZs2e1atUqnTx5Uv3793f1XABUrils1/TdhpsLIaYa5V+CVt3X0ufn5ys0NFRt27atsqaxmJlmzJihgQMHOmenu3FOubm5ioiIUFhYmKZMmaKMjAxdffXVrpwLgOq5fbum7zbsXLg6qQZq+rX0ta2pbykpKdq1a5fef//9Co+5aU7du3fXzp07dfz4ca1Zs0YTJkxQTk6O87ib5gKgZty6XdN3q1Yfc+FITDXKP+Souq+l93q9On36tI4dO1ZlTWOYNm2a1q5dq+zsbHXu3NkZd+OcQkND1a1bN/Xp00epqanq1auXnnnmGVfOBUD13Lxd03cbfi6EmGrEx8fL6/UqKyvLGTt9+rRycnI0YMAASXI+nvrHNYcOHdLu3budmoZkZkpJSVF6ero2btyo+Ph4n8fdOKfzmZlKSkqaxFwA+HLjdk3fbcS5+PU0YRcqKiqyHTt22I4dO0ySPfXUU7Zjxw7bt2+fmZ27lMzj8Vh6errl5uba3XffXemlZJ07d7YNGzbY9u3b7ZZbbmm0S3h/85vfmMfjsU2bNtmhQ4ec2/fff+/UuGlOs2bNsnfffdf27Nlju3btskceecRatGhh69evd91cAJxD3w3sObmp7zbZc2LKysr09ddfKzIystr34N577z2fT1cs/6bpu+++W88//7ymTJmi48ePO3/26dNH6enpMjPnO44effRRlZWV6c4779SpU6c0ePBgrVy5UidPnqzfSVZiyZIlks59I/aPPffcc/rFL34hSa6a04EDB/SLX/xC+fn5ioqKUkJCgtasWaN+/fqpsLCw3udiZioqKlJsbKxatODAJVAd+u4Qn3H6bv333SAzM7/MOsB89dVXzsfdAxfrwIEDPu9xA6iIvgt/qknfbbJHYiIjIyWd+0eIiopq5LUJQCdPSuXflfT111KbNo27PgGqsLBQcXFxzu8TgKr5ve/Sp5ql2vTdJhtiyg9lRkVFEWIq8/++IEySFBVFc7iAxr5sE3ADv/dd+lSzVpO+y5v8AADAlQgxAADAlQgxAADAlQgxAADAlQgxAADAlQgxAICA12NOZmOvAgIQIQYAALgSIQYAALgSIaaZ4tAsAMDtCDEAAMCVCDHgqAyAgERvwoUQYiBJuuzhtxt7FQAAqBVCDAAAcCVCDAAAcCVCDAAAcCVCDADAFTh3D+cjxDRDNAIAQFNAiAEABBx2tlAThBgAgGsQbvBjhBgAAOBKhBg42MMBALgJIQYAALgSIQYAALgSIQYAALgSIQYAALgSIQYAALgSIaaZ4QokAEBTQYgBAACuRIiBD47UAGhs9CHUFCEGAAC4EiEGAAC4EiEGAAC4EiEGAAC4EiEGAOAqnPiLcoQYAADgSoQYAADgSoSYZoRDsACApoQQAwAAXIkQAwAAXIkQAwAAXIkQAwAIGJy7h9qoVYhJTU1V3759FRkZqY4dO2rUqFHKy8vzqTEzzZs3T7GxsQoPD9eQIUP0ySef+NSUlJRo2rRpat++vdq0aaORI0fqq6++8qk5duyYxo8fL4/HI4/Ho/Hjx+v48eN1myUAuBR9F6harUJMTk6O7r//fm3ZskVZWVkqLS1VUlKSTp486dQsXLhQTz31lBYvXqytW7fK6/Vq+PDhKioqcmqmT5+ujIwMrVq1Su+//75OnDihESNG6OzZs07NuHHjtHPnTmVmZiozM1M7d+7U+PHj/TBlAHAP+i5QDbsIhw8fNkmWk5NjZmZlZWXm9XptwYIFTk1xcbF5PB57/vnnzczs+PHjFhISYqtWrXJqDh48aC1atLDMzEwzM/vf//1fk2Rbtmxxaj788EOTZJ999lmN1q2goMAkWUFBwcVMsUnp+vu3nNtVD75mJplJdtWDr/k8hh/we4RA09T7Ln0Ktfk9uqhzYgoKCiRJ0dHRkqQ9e/YoPz9fSUlJTk1YWJgGDx6sDz74QJK0bds2nTlzxqcmNjZWCQkJTs2HH34oj8ejfv36OTU33HCDPB6PU3O+kpISFRYW+twAoKmh7wI/qHOIMTPNmDFDAwcOVEJCgiQpPz9fkhQTE+NTGxMT4zyWn5+v0NBQtW3bttqajh07VnjNjh07OjXnS01Ndd7H9Xg8iouLq+vUACAg0Xd/wAnAkC4ixKSkpGjXrl165ZVXKjwWFBTkc9/MKoyd7/yayuqrW86sWbNUUFDg3A4cOFCTaQCAa9B3AV91CjHTpk3T2rVrlZ2drc6dOzvjXq9Xkiqk9sOHDzt7CV6vV6dPn9axY8eqrfnmm28qvO6RI0cq7G2UCwsLU1RUlM8NP6jNXgt7OEDgoe8CFdUqxJiZUlJSlJ6ero0bNyo+Pt7n8fj4eHm9XmVlZTljp0+fVk5OjgYMGCBJ6t27t0JCQnxqDh06pN27dzs1/fv3V0FBgT766COn5h//+IcKCgqcGgBoDppT32UHCrUVXJvi+++/XytXrtQbb7yhyMhIJ/l7PB6Fh4crKChI06dP1+OPP64rrrhCV1xxhR5//HG1bt1a48aNc2onTZqkhx56SO3atVN0dLRmzpypnj17atiwYZKkHj166LbbbtPkyZP15z//WZKUnJysESNGqHv37v6cPwAENPouULVahZglS5ZIkoYMGeIznpaWpokTJ0qSfve73+nUqVOaOnWqjh07pn79+mn9+vWKjIx06p9++mkFBwdr7NixOnXqlIYOHaply5apZcuWTs2KFSv0wAMPOGfTjxw5UosXL67LHAHAtei7QNWCzMwaeyXqQ2FhoTwejwoKCnifVhUP04afLtanT/+rJKnHg6/pVGgrn8f3LrijwdYtkPF7BNTcxW4v9ClItfs94ruTAACAKxFiAACAKxFiAACAKxFiAACAKxFiUCk+rwEAEOgIMQAAV2JnC4QYAADgSoQYAECj46gK6oIQAwAAXIkQAwAAXIkQ0wxwmBYA0BQRYgAAgCsRYgAAgCsRYgAAgCsRYgAArsU5f80bIQYAALgSIQZVYg8HABDICDEAAMCVCDEAAMCVCDEAAMCVCDFNHOe1AACaKkIMAKBRsbOFuiLEAAAAVyLEAAAAVyLEAAAAVyLEAAAAVyLENGGcLAegOaDXNV+EGAAA4EqEGFSLPRwAQKAixAAAGg07SrgYhBgAAOBKhBgAAOBKhBgAAOBKhBgAgOtxbk3zRIgBAACuRIjBBbGHA6A+0FtwsQgxTRTNAQDQ1BFiAACAKxFiUCMc2QEABBpCDACgSWBnq/khxDRBbMgAAh19Cv5AiAEAAK5EiAEAAK5EiAEAAK5EiEGN8R42ACCQEGKaGIIGAKC5IMQAABpUfe5ssSPXvBBiAACAKxFimpCG2ANhLwdAoKNPNR+EGABAg2mogEGQaR4CPsQ899xzio+PV6tWrdS7d2+99957jb1KAakhN1iaA9C00XfhFgEdYlavXq3p06dr9uzZ2rFjh2666Sbdfvvt2r9/f2OvWrNHkAGapvrsuw3dN+hTTV9Ah5innnpKkyZN0r333qsePXpo0aJFiouL05IlSxp71QLGZQ+/3WgbKg0CaHrqq+/SL1Afght7Bapy+vRpbdu2TQ8//LDPeFJSkj744IMK9SUlJSopKXHuFxQUSJIKCwvrd0UbUcLc/6nzc8+eLlb5v8zZku9VZmV1Wk6XB1+VJO1+9NY6r0sgK//9MbNGXhOg/vm7715Mj5L806fKe5TUdPtUU1ObvhuwIebbb7/V2bNnFRMT4zMeExOj/Pz8CvWpqal69NFHK4zHxcXV2zq6naf8L8/98uKXteiiFxHQioqK5PF4LlwIuFgg9l36VPNVk74bsCGmXFBQkM99M6swJkmzZs3SjBkznPtlZWU6evSo2rVrV2l9XRQWFiouLk4HDhxQVFSUX5bZmJjPhZmZioqKFBsb65flAW5A360/zOfCatN3AzbEtG/fXi1btqyQ/g8fPlxhL0GSwsLCFBYW5jN2ySWX1Mu6RUVFNYlfvnI1nc+BAwf0wAMP6OOPP9Y333yj4OBg/eQnP9GkSZM0ZcoUBQf7/jp9+eWXmjlzpjZu3KjS0lL1799fTzzxhK677roKy161apUWLFigzz77TNHR0Ro7dqwee+wxRURE1Nt8aoojMGgu6LtVmzhxojZt2qS9e/f6ZXk1mU96erpeffVVbd26VQcPHlRMTIxuvPFGzZs3T1dccUWF+g0bNmjOnDn6+OOP1bp1a40YMUILFy5Ux44d/bLO1WmsvhuwJ/aGhoaqd+/eysrK8hnPysrSgAEDGmmtmreTJ08qKipKc+bM0dq1a7Vq1SoNHDhQ06ZN05QpU3xqjxw5optuukmff/65XnzxRf3tb39TcXGxhgwZory8PJ/aFStW6O6771bfvn31zjvvaO7cuVq2bJnGjBnTkNMDmj36btXmzJmjjIyMBn3NJ554Qt9//71mz56tzMxMPfbYY9qxY4euu+46ffLJJz61OTk5uv322xUTE6M33nhDzzzzjDZs2KChQ4f6nLfU5FgAW7VqlYWEhNhf//pX+9///V+bPn26tWnTxvbu3dso61NQUGCSrKCgoFFe39/8NZ+xY8dacHCwFRcXO2O//e1vLSQkxOdnVVBQYO3bt7exY8c6Y6WlpdapUydLSkryWeaKFStMkq1bt67G69HUfj5AY6Dv1q/azOebb76pMHbw4EELCQmxSZMm+Yz37dvXrr76ajtz5owztnnzZpNkzz333MWveBUa++cT0CHGzOy//uu/rGvXrhYaGmrXXXed5eTkNNq6FBcX29y5c33+s76QuXPnmiT7+OOP7V//9V8tKirK2rZtaw8++KCdOXPGPvvsM7v11lstIiLCunbtak888YTP89PS0kyS7dmzx2c8OzvbJFl2dnaDzqcy999/v4WGhvpsPN26dbNbb721Qm1ycrKFh4c7te+//75JsldeecWn7vTp0xYREWGTJ0+u8Xr4az5Ac+f2vltbhw8ftsmTJ1vnzp0tNDTU2rdvbwMGDLCsrCynZsKECda1a9eLfi1/zCc+Pt5nx++rr74ySZaamlqh9sorr7Thw4fX+bUupLH7bsCeE1Nu6tSpmjp1amOvhqRz7//OmzevTs8dO3as7rnnHt13333KysrSwoULdebMGW3YsEFTp07VzJkztXLlSv3+979Xt27d/PZWipnp7NmzlT7WsmVL/eEPf5AklZaWVjin5ULLLCoq0vr167Vs2TI99NBDzvNPnTqlf/7znxo9enSF5yYmJurUqVP68ssvdeWVV2r37t3O+I+FhIToqquuch6viYv5+QD4QVPpuzU1fvx4bd++XX/605905ZVX6vjx49q+fbu+++67Wi+rrKxMZWVVXwpe3nfreuLzl19+qX379mnUqFHOWFV9tHxs8+bNdXqtmmjsvhvwIaapSE5Ods7iHzZsmNavX6/FixcrPT3d+c9+yJAheuutt7RixQq/hZicnBzdfPPNNards2ePLrvssgvWPfHEE5o1a5akc1cxPPLII3rsscecx48dOyYzU3R0dIXnlo+VN4fyP6uq9ddJdABQlc2bN+vee+/V5MmTnbGf//zndVrW/PnzK73s/Hxdu3atdX8rLS3VpEmTFBERoQcffNAZv1AfrUsYcwtCTAMZMWKEz/0ePXro448/1u233+6MBQcHq1u3btq3b5/fXrd3797aunVrjWprehnxxIkTNWzYMB09elQbN27Uk08+qYKCAj377LM+ddXtaZz/WFW1/rpMEwCqcv3112vZsmVq166dhg0bpt69eyskJKROy0pOTq7Q7ytz/lVdF2JmmjRpkt577z2tWbOm0s/iaY59lBDTQM5PyKGhoWrdurVatWpVYdyfnzIcERGhn/70pzWqrenbSV6vV16vV9K5T/Js27atHn74Yf3617/Wtddeq7Zt2yooKKjS9H/06FFJP/x7tGvXTtK5PYnzL+E8evRopXsWAOBPq1ev1mOPPaalS5dqzpw5ioiI0OjRo7Vw4UKn19WU1+ut0SXNtQkWZqZ7771Xy5cv10svvVThKNGP++j5mnofDdhLrHFOecg5/xK5b7/9tkbPz8nJUUhISI1udX3r5vrrr5ckff7555Kk8PBwdevWTbm5uRVqc3NzFR4erp/85CeSpJ49ezrjP1ZaWqrPPvtMCQkJdVonAKip9u3ba9GiRdq7d6/27dun1NRUpaena+LEibVe1vz582vUby+//PIaLa88wKSlpWnp0qW65557KtSU98mqem5T7qPNPsS8++67+tnPfqbY2FgFBQXp9ddf93nczDRv3jzFxsYqPDxcQ4YMqXB9fklJiaZNm6b27durTZs2GjlypL766iu/rF/5OSq7du3yGV+7dm2l9ampqerbt68iIyPVsWNHPfnkk86HJZXfPvroI02ePFnt2rVTaGiorrvuOq1atcrn7aTazCk7O1uS1K1bN2ds9OjR2rhxow4cOOCMFRUVKT09XSNHjnSO+vTr10+dOnXSsmXLfJb52muv6cSJEwoNDVViYqLzQUr9+/fXO++849Q19s8HQO0Fct/t0qWLUlJSNHz4cG3fvr1Gz/lx33322Wc1aNCgC/bd9u3bX3BOP/vZzzRu3DilpaXpz3/+s371q19V+vqXXnqprr/+ei1fvtznQo4tW7YoLy+v1udYLlmyxD19t1GuiQog69ats9mzZ9uaNWtMkmVkZPg8vmDBAouMjLQ1a9ZYbm6u3XXXXdapUycrLCx0aqZMmWKXXnqpZWVl2fbt2+3mm2+2Xr16WWlpqXOJ9ZEjR3yWO2HCBGvTpk2F9Rk8eLBdc801zv3S0lLr3r27denSxVauXGnvvPOOJScnW3x8fKWXWN96662WlpZmu3fvtp07d9odd9xhXbp0sRMnTlz0nGJiYiw5OdlWrFhhmzZtstdff92mTJliLVu2tDvvvNNnPQ4fPmydOnWynj17WkZGhq1bt84GDRpkkZGR9umnn/rUvvzyyybJkpOTLTs72/7yl7/YJZdcYsOHD7e1a9fa22+/bXl5eZaXl2ePPPKIhYSE2O7du/3y8wHQ8Oq779bG8ePH7dprr7Unn3zS3nzzTdu0aZM9+eST1qpVKxs3bpxTV90l1vXVdy+99FKTZL/61a/sww8/9Llt377dZx2ys7MtODjYRo8ebVlZWbZixQqLi4uzhISEWl/+7Ka+2+xDzI+dvzGVlZWZ1+u1BQsWOGPFxcXm8Xjs+eefN7NzG0BISIitWrXKqTl48KC1aNHCMjMzLzrEmJl9/vnnlpSUZFFRUdahQwebNm2avf322zX6nJjDhw+bJOdzHi5mTkFBQXbttddaTEyMBQcHW0REhF1//fX2n//5nz6fEVPuiy++sFGjRllUVJS1bt3ahg4datu2bat0PVeuXGmJiYkWGhpqXq/XHnjgASsqKqq0tm3btrZ06VK//HwANK766Lu1UVxcbFOmTLHExESLioqy8PBw6969u82dO9dOnjzp1NXmc2L81Xc7d+5skiq9VbYu69evtxtuuMFatWpl0dHR9stf/rLSD8yri0Dtu4SYHzl/Y/rnP/9pkiok3pEjR9ovf/lLMzP7+9//bpLs6NGjPjWJiYn2xz/+sd7X+UL+7//+zyRZbm6umbl7TqWlpfbKK69YaGioffLJJ66eC4Bz6LuBPadA77vN/pyY6pR/CVp1X0ufn5+v0NBQtW3btsqaxmJmmjFjhgYOHOic2OXGOeXm5ioiIkJhYWGaMmWKMjIydPXVV7tyLgCq5/btmr7bsHPhEusaqOnX0te2pr6lpKRo165dev/99ys85qY5de/eXTt37tTx48e1Zs0aTZgwQTk5Oc7jbpoLgJpx63ZN361afcyFIzHVKP98gOq+lt7r9er06dM6duxYlTWNYdq0aVq7dq2ys7PVuXNnZ9yNcwoNDVW3bt3Up08fpaamqlevXnrmmWdcORcA1XPzdk3fbfi5EGKqER8fL6/X6/O19KdPn1ZOTo7ztfTln+z445pDhw5p9+7djfLV9WamlJQUpaena+PGjYqPj/d53I1zOp+ZqaSkpEnMBYAvN27X9N1GnItfz7BxoaKiItuxY4ft2LHDJNlTTz1lO3bssH379pnZuUvJPB6PpaenW25urt19992VXkrWuXNn27Bhg23fvt1uueWWRruE9ze/+Y15PB7btGmTHTp0yLl9//33To2b5jRr1ix79913bc+ePbZr1y575JFHrEWLFrZ+/XrXzQXAOfTdwJ6Tm/pusw8x2dnZlV6+NmHCBDM7d2nc3Llzzev1WlhYmA0aNMg547zcqVOnLCUlxaKjoy08PNxGjBhh+/fvb4TZWJWX46WlpTk1bprTr3/9a+vatauFhoZahw4dbOjQoc6GZOauuQA4h74b2HNyU98NMjPz77GdwFBWVqavv/5akZGRjX6iF9zLzFRUVKTY2Fi1aMG7r0B16Lvwh9r03SZ7ddLXX39d6bd8AnVx4MABnxP1AFRE34U/1aTvNtkQExkZKencP0JUVFQjrw3q3cmTUvl3P339tdSmjV8WW1hYqLi4OOf3CUDV/N5362m7RmCrTd9tsiGm/FBm+RdYoYlr2fKHv0dF+b3ZcWgcuDC/99163q4R2GrSd3mTHwAAuBIhBgAAuBIhBgAAuBIhBgDgCpc9/HZjrwICDCEGABBwzg8sPeZkNtKaIJARYgAAAYngggshxKDJofEBQPNAiAEAAK5EiAEAuAon+KIcIQYA4BoEGPwYIQYAALgSIQZNAifzAs0LR2QgEWIAAAGGgIKaIsQAAABXIsSgSWJPDgCaPkIMACBgsAOC2iDEAAAAVyLEAAACAkdhUFuEGLgejQ9ontj2QYgBAACuRIgBAACuRIgBADQ63hpCXRBiAACAKxFi0GSxZwc0fWznzRshBgAAuBIhBgAAuBIhBgAAuBIhBgDQqDivBXVFiIGr0fwAoPkixAAAXI2dmeaLEAMAAFyJEAMAAFyJEAMAAFyJEIMmjffKgcDGNoqLQYgBAACuRIgBALgeR3SaJ0IMAABwJUIMAKBRcPQEF4sQAwAAXIkQA9diLw4AmjdCDAAAcCVCDAAAcCVCDAAAcCVCDACgwXFOG/yhViEmNTVVffv2VWRkpDp27KhRo0YpLy/Pp8bMNG/ePMXGxio8PFxDhgzRJ5984lNTUlKiadOmqX379mrTpo1Gjhypr776yqfm2LFjGj9+vDwejzwej8aPH6/jx4/XbZYAgCaPYNT81CrE5OTk6P7779eWLVuUlZWl0tJSJSUl6eTJk07NwoUL9dRTT2nx4sXaunWrvF6vhg8frqKiIqdm+vTpysjI0KpVq/T+++/rxIkTGjFihM6ePevUjBs3Tjt37lRmZqYyMzO1c+dOjR8/3g9TBgAATUGtQkxmZqYmTpyoa665Rr169VJaWpr279+vbdu2STp3FGbRokWaPXu2xowZo4SEBL300kv6/vvvtXLlSklSQUGB/vrXv+o//uM/NGzYMF177bVavny5cnNztWHDBknSp59+qszMTC1dulT9+/dX//799cILL+itt96qcOQHAJoyjoADVbuoc2IKCgokSdHR0ZKkPXv2KD8/X0lJSU5NWFiYBg8erA8++ECStG3bNp05c8anJjY2VgkJCU7Nhx9+KI/Ho379+jk1N9xwgzwej1NzvpKSEhUWFvrcAIlDzHA3joADVQuu6xPNTDNmzNDAgQOVkJAgScrPz5ckxcTE+NTGxMRo3759Tk1oaKjatm1boab8+fn5+erYsWOF1+zYsaNTc77U1FQ9+uijdZ0OAASkzMxMn/tpaWnq2LGjtm3bpkGDBlU4Ai5JL730kmJiYrRy5Urdd999zhHwl19+WcOGDZMkLV++XHFxcdqwYYNuvfVW5wj4li1bnB3IF154Qf3791deXp66d+/esBMHaqDOR2JSUlK0a9cuvfLKKxUeCwoK8rlvZhXGznd+TWX11S1n1qxZKigocG4HDhyoyTTgUhxdQXPVFI6As/3CX+oUYqZNm6a1a9cqOztbnTt3dsa9Xq8kVThacvjwYefojNfr1enTp3Xs2LFqa7755psKr3vkyJEKR3nKhYWFKSoqyucGAE1JbY+A//jodn0dAS8/f8bj8SguLu7iJugHBKTmpVYhxsyUkpKi9PR0bdy4UfHx8T6Px8fHy+v1Kisryxk7ffq0cnJyNGDAAElS7969FRIS4lNz6NAh7d6926np37+/CgoK9NFHHzk1//jHP1RQUODUAEBzwxFwwFetzom5//77tXLlSr3xxhuKjIx00rnH41F4eLiCgoI0ffp0Pf7447riiit0xRVX6PHHH1fr1q01btw4p3bSpEl66KGH1K5dO0VHR2vmzJnq2bOn815tjx49dNttt2ny5Mn685//LElKTk7WiBEjeF8WQLNUfgT83XffrfIIeKdOnZzxqo6A//hozOHDh50dw7oeAQ8LC7v4yQF1VKsjMUuWLFFBQYGGDBmiTp06ObfVq1c7Nb/73e80ffp0TZ06VX369NHBgwe1fv16RUZGOjVPP/20Ro0apbFjx+rGG29U69at9eabb6ply5ZOzYoVK9SzZ08lJSUpKSlJiYmJevnll/0wZQBwD46AA1Wr1ZEYM7tgTVBQkObNm6d58+ZVWdOqVSs9++yzevbZZ6usiY6O1vLly2uzegDQ5HAEHKhanS+xBgDUvyVLlkiShgwZ4jOelpamiRMnSjp3BPzUqVOaOnWqjh07pn79+lV6BDw4OFhjx47VqVOnNHToUC1btqzCEfAHHnjAuYpp5MiRWrx4cf1OELgIhBgACGBN7Qg4Vw/Bn/gWazQLNE6g+WB7bz4IMQAAwJUIMXAd9rIAABIhBgDQQNgBgb8RYgAAgCsRYgAATQ5HfZoHQgwAAHAlQgwAAHAlQgwAoN7x9g7qAyEGAAC4EiEGzQZ7gkDzwjbf9BFiAACAKxFiAAD1iiMiqC+EGAAA4EqEGAAA4EqEGABAk8VbWU0bIQYAALgSIQauwl4V4C5ss6hPhBgAAOBKhBgAAOBKhBg0KxzaBpoftvumixADAABciRADAABciRADAABciRAD1+B9bQB1Rf9omggxaHZoZkDDYFtDfSPEAACaBUJV00OIAQAArkSIAQAArkSIAQD4HW/doCEQYgAAzQbhqmkhxAAAAFcixMAV/L33xN4YUH8S5v5PY68CmglCDAAAcCVCDACgWeFIbNNBiAEAAK5EiAEANDscjWkaCDEIeDQbAEBlCDEAgGaJHST3I8QAAABXIsQgoNXnnhJ7YQDoA+5GiAEANGsEGfcixKBZo3kBgHsRYhCwCBgAGgr9xp0CPsQ899xzio+PV6tWrdS7d2+99957jb1KANCkNde+S5Bxn4AOMatXr9b06dM1e/Zs7dixQzfddJNuv/127d+/v7FXDU0IjQv4QXPvu/QDdwnoEPPUU09p0qRJuvfee9WjRw8tWrRIcXFxWrJkSWOvGupZQzcSGhdwDn33XD8ovyGwBTf2ClTl9OnT2rZtmx5++GGf8aSkJH3wwQcV6ktKSlRSUuLcLygokCQVFhbW74rCrxLm/k+dnnf2dLHKf9JnS75XmZXVehldHnxVux+91Wes/PfHzOq0XoCb+KvvlpV875f18cd2fbG6PPiq8/fz+wPqR236bsCGmG+//VZnz55VTEyMz3hMTIzy8/Mr1KempurRRx+tMB4XF1dv64jA4in/y3O/rPsyFlU+XlRUJI/HU/mDQBPhr757cMlEv62TP7Zrf6mqP6B+1KTvBmyIKRcUFORz38wqjEnSrFmzNGPGDOd+WVmZjh49qnbt2lVaXxeFhYWKi4vTgQMHFBUV5ZdlNibmc2FmpqKiIsXGxvpleYAb0HfrD/O5sNr03YANMe3bt1fLli0rpP/Dhw9X2EuQpLCwMIWFhfmMXXLJJfWyblFRUU3il68c86keR2DQXNB3Gw7zqV5N+27AntgbGhqq3r17Kysry2c8KytLAwYMaKS1AoCmi74LtwnYIzGSNGPGDI0fP159+vRR//799Ze//EX79+/XlClTGnvVAKBJou/CTQI6xNx111367rvvNH/+fB06dEgJCQlat26dunbt2ijrExYWprlz51Y4fOpWzAfA+ei79Yv5+FeQce0oAABwoYA9JwYAAKA6hBgAAOBKhBgAAOBKhBgAAOBKhBgAAOBKzT7EvPvuu/rZz36m2NhYBQUF6fXXX/d53Mw0b948xcbGKjw8XEOGDNEnn3ziU1NSUqJp06apffv2atOmjUaOHKmvvvqqAWfxg9TUVPXt21eRkZHq2LGjRo0apby8PJ8aN81pyZIlSkxMdD4Nsn///nrnnXecx900FwDn0HcDe06u6rvWzK1bt85mz55ta9asMUmWkZHh8/iCBQssMjLS1qxZY7m5uXbXXXdZp06drLCw0KmZMmWKXXrppZaVlWXbt2+3m2++2Xr16mWlpaUNPBuzW2+91dLS0mz37t22c+dOu+OOO6xLly524sQJV85p7dq19vbbb1teXp7l5eXZI488YiEhIbZ7927XzQXAOfTdwJ6Tm/pusw8xP3b+xlRWVmZer9cWLFjgjBUXF5vH47Hnn3/ezMyOHz9uISEhtmrVKqfm4MGD1qJFC8vMzGywda/K4cOHTZLl5OSYWdOYU9u2bW3p0qVNYi5Ac0ffdcecArXvNvu3k6qzZ88e5efnKykpyRkLCwvT4MGD9cEHH0iStm3bpjNnzvjUxMbGKiEhwalpTAUFBZKk6OhoSe6e09mzZ7Vq1SqdPHlS/fv3d/VcAFSuKWzX9N2Gmwshphrl3+R6/re3xsTEOI/l5+crNDRUbdu2rbKmsZiZZsyYoYEDByohIUGSO+eUm5uriIgIhYWFacqUKcrIyNDVV1/tyrkAqJ7bt2v6bsPOJaC/OylQBAUF+dw3swpj56tJTX1LSUnRrl279P7771d4zE1z6t69u3bu3Knjx49rzZo1mjBhgnJycpzH3TQXADXj1u2avlu1+pgLR2Kq4fV6JalCcjx8+LCTQr1er06fPq1jx45VWdMYpk2bprVr1yo7O1udO3d2xt04p9DQUHXr1k19+vRRamqqevXqpWeeecaVcwFQPTdv1/Tdhp8LIaYa8fHx8nq9ysrKcsZOnz6tnJwcDRgwQJLUu3dvhYSE+NQcOnRIu3fvdmoakpkpJSVF6enp2rhxo+Lj430ed+OczmdmKikpaRJzAeDLjds1fbcR5+LX04RdqKioyHbs2GE7duwwSfbUU0/Zjh07bN++fWZ27lIyj8dj6enplpuba3fffXell5J17tzZNmzYYNu3b7dbbrml0S71+81vfmMej8c2bdpkhw4dcm7ff/+9U+OmOc2aNcveffdd27Nnj+3atcseeeQRa9Giha1fv951cwFwDn03sOfkpr7b7ENMdna2SapwmzBhgpmduzRu7ty55vV6LSwszAYNGmS5ubk+yzh16pSlpKRYdHS0hYeH24gRI2z//v2NMBurdC6SLC0tzalx05x+/etfW9euXS00NNQ6dOhgQ4cOdTYkM3fNBcA59N3AnpOb+m6QmZl/j+0AAADUP86JAQAArkSIAQAArkSIAQAArkSIAQAArkSIAQAArkSIAQAArkSIAQAArkSIAQAArkSIAQAArkSIAQAArkSIAQAArvT/A6V0d/n8jI1+AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LATIHAN 5\n",
    "\n",
    "mu = 100\n",
    "si = 10\n",
    "N = 100000\n",
    "values = np.random.normal(mu, si, N)\n",
    "\n",
    "plt.subplot(3,3,1)\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": "6b99a49a",
   "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}