NerdyVandheka/Issues 7 Nerdy.ipynb

## Issues 7 Nerdy.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4f3da98a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import statistics as stat\n",
    "\n",
    "dataset = [0, 0, 2, 2, 4, 3, 5, 0, 0, 3]\n",
    "\n",
    "mean = stat.mean(dataset)\n",
    "median = stat.median(dataset)\n",
    "mode = stat.mode(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bd5313f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x = [10, 20, 40, 40, 40, 60, 70, 20, 40, 40, 40, 40, 40, 60, 70, 20, 30, 40, 50, 70, 20]\n",
      "\n",
      "N = 21\n"
     ]
    }
   ],
   "source": [
    "#Generate dataset\n",
    "x = [\n",
    "    10, 20, 40, 40, 40, 60, 70,\n",
    "    20, 40, 40, 40, 40, 40, 60,\n",
    "    70, 20, 30, 40, 50, 70, 20,\n",
    "]\n",
    "print('x =', x)\n",
    "print()\n",
    "print('N =', len(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b0c25e6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10, 20, 20, 20, 20, 30, 40, 40, 40, 40, 40, 40, 40, 40, 40, 50, 60, 60, 70, 70, 70]\n"
     ]
    }
   ],
   "source": [
    "#Sorted/Unsorted dataset, range\n",
    "y = sorted(x)\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a335295e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "range = 60\n"
     ]
    }
   ],
   "source": [
    "rng = y[-1] - y[0]\n",
    "print('range =', rng)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e126b399",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "range = 10\n"
     ]
    }
   ],
   "source": [
    "rng = x[-1] - x[0]\n",
    "print('range =', rng)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0177759e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10, 20, 40, 40, 40, 60, 70, 20, 40, 40, 40, 40, 40, 60, 70, 20, 30, 40, 50, 70, 20]\n"
     ]
    }
   ],
   "source": [
    "#Unsorted dataset, middle value, median\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e52433b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mid value = 65.0\n"
     ]
    }
   ],
   "source": [
    "mid = (x[13] + x[14]) / 2\n",
    "print ('mid value =', mid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0f6f5ad8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40\n"
     ]
    }
   ],
   "source": [
    "import statistics as stat\n",
    "print(stat.median(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7be5fa5e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10, 20, 20, 20, 20, 30, 40, 40, 40, 40, 40, 40, 40, 40, 40, 50, 60, 60, 70, 70, 70]\n"
     ]
    }
   ],
   "source": [
    "#Sorted dataset, middle value, median\n",
    "y = sorted(x)\n",
    "print(y) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fa2e42e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "median = 40.0\n"
     ]
    }
   ],
   "source": [
    "med = (y[13] + y[14]) / 2\n",
    "print ('median =', med)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b67c36d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40\n"
     ]
    }
   ],
   "source": [
    "import statistics as stat\n",
    "print(stat.median(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c40b08d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean = 40.95238095238095\n"
     ]
    }
   ],
   "source": [
    "#Mean 0f X\n",
    "sx = sum(x)\n",
    "N = len(x)\n",
    "mean = sx / N\n",
    "print('mean =', mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f8f5a779",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40.95238095238095\n"
     ]
    }
   ],
   "source": [
    "import statistics as stat\n",
    "print(stat.mean(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d356d660",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean = 40.95238095238095\n"
     ]
    }
   ],
   "source": [
    "#Mean 0f y\n",
    "sy = sum(y)\n",
    "N = len(y)\n",
    "mean = sy / N\n",
    "print('mean =', mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "be2e2729",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40.95238095238095\n"
     ]
    }
   ],
   "source": [
    "import statistics as stat\n",
    "print(stat.mean(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "31c2af8a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10, 20, 20, 20, 20, 30, 40, 40, 40, 40, 40, 40, 40, 40, 40, 50, 60, 60, 70, 70, 70]\n"
     ]
    }
   ],
   "source": [
    "#sorted dataset and mode\n",
    "y = sorted(x)\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "396a6195",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mode = 40\n"
     ]
    }
   ],
   "source": [
    "import statistics as stat\n",
    "print('mode =', stat.mode(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9edbacdc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40\n"
     ]
    }
   ],
   "source": [
    "import statistics as stat\n",
    "print(stat.mode(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "90276133",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (3699984715.py, line 3)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  Cell \u001b[1;32mIn[23], line 3\u001b[1;36m\u001b[0m\n\u001b[1;33m    1. Dalam statistik, median mewakili nilai tengah dalam dataset yang telah diurutkan.\u001b[0m\n\u001b[1;37m       ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "#Exercise 1\n",
    "\n",
    "1. Dalam statistik, median mewakili nilai tengah dalam dataset yang telah diurutkan. \n",
    "Ini membagi data menjadi dua bagian yang sama, dengan setengah nilai lebih kecil dari median dan setengah lainnya lebih besar.\n",
    "Sedangkan, middle value mungkin hanya mengacu pada nilai aritmatika pusat dalam dataset yang tidak di urutkan. \n",
    "Namun, ini tidak selalu sama dengan median, terutama ketika dataset memiliki jumlah elemen genap. \n",
    "Kita dapat menemukan nilai tengah dengan mengindeks data yang diurutkan pada posisi tengah. \n",
    "Namun, ini hanya berfungsi jika ada jumlah elemen ganjil.\n",
    "2. Tidak, nilai mean dan mode values tidak selalu sama untuk dataset yang tidak diurutkan dan diurutkan. \n",
    "Hal ini disebabkan karena mean mewakili rata-rata dari semua nilai dalam dataset.\n",
    "Ini dihitung dengan menjumlahkan semua nilai dan membaginya dengan jumlah total nilai. \n",
    "Pada mean, Mengurutkan data tidak memengaruhi jumlah nilai, sehingga mean tetap sama apa pun urutan datanya. \n",
    "Sedangkan mode values sendiri mewakili nilai paling sering dalam data set, pada mode values mengurutkan data dapat mengubah\n",
    "frekuensi setiap nilai. Misalnya, jika nilai paling sering awalnya muncul beberapa kali di posisi berbeda, pengurutan mungkin\n",
    "mengelompokkannya bersama, membuatnya tampak lebih sering.\n",
    "3. Jika kita menghitung range hanya sebagai selisih antara titik data terakhir dan titik data pertama dalam dataset, \n",
    "maka pengurutan tidak lah diperlukan karena urutan data tidak memengaruhi elemen pertama atau terakhir. \n",
    "Apapun pengurutannya, elemen pertama akan tetap menjadi yang pertama dan elemen terakhir akan tetap menjadi yang terakhir.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "73182711",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Histogram of x\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.hist(x, bins=7 , range=[10,80])\n",
    "plt.yticks(range(0, 11,1))\n",
    "plt.grid()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8e5fad7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Histogram of y\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.hist(y, bins=7 , range=[10,80])\n",
    "plt.yticks(range(0, 11,1))\n",
    "plt.grid()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ee2108c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#2nd dataset, bins = 7\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = [10, 11, 12, 13, 14, 30, 50, 70, 72, 72]\n",
    "plt.hist(x, bins=7 , range=[10,80])\n",
    "plt.yticks(range(0, 7,1))\n",
    "plt.xticks(range(10, 90,10))\n",
    "plt.grid()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "41aa6359",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#2nd dataset, bins = 4\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = [10, 11, 12, 13, 14, 30, 50, 70, 72, 72]\n",
    "plt.hist(x, bins=4 , range=[10,80])\n",
    "plt.yticks(range(0, 7,1))\n",
    "plt.xticks([10, 27, 45, 62])\n",
    "plt.grid()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "da99073b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhYAAAGdCAYAAABO2DpVAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAAAWn0lEQVR4nO3dbYxcZd348d9su0xZ7IJ0Ld3KUqtCBGoxYQlUolKgxQoEUAmxFdEo0dAgtTGoEOLWYqsmf4KG2FA0oC/WEkNAEh7S9UULBqttsUklimCqVHnK8rDTdnUYu/N/caebu+5u2VN+07l3+XySTXrOnLnO9eaa+e6Z6Z5SvV6vBwBAgpZmTwAAmDyEBQCQRlgAAGmEBQCQRlgAAGmEBQCQRlgAAGmEBQCQZuqRPuHQ0FA8//zzMX369CiVSkf69ADAYajX67Fnz56YPXt2tLSMfV3iiIfF888/H11dXUf6tABAgt27d8eJJ5445uNHPCymT58eEf8zsfb29rRxa7VabNy4MRYvXhytra1p4wLjZx3C5FWpVKKrq2v4fXwsRzwsDnz80d7enh4WbW1t0d7e7gUNmsQ6hMnvzb7G4MubAEAaYQEApBEWAEAaYQEApBEWAEAaYQEApBEWAEAaYQEApBEWAEAaYQEApCkcFv/85z/js5/9bMyYMSPa2triQx/6UGzfvr0RcwMAJphC9wp57bXX4txzz42FCxfGI488EjNnzoy//vWvcdxxxzVoegDARFIoLL7//e9HV1dX3H333cP73vOe92TPCQCYoAqFxYMPPhgXXXRRXHnllbF58+Z497vfHdddd11ce+21Yz6nWq1GtVod3q5UKhHxP3dBrNVqhzntkQ6MlTkmUIx1CJPXeNd1qV6v18c76LRp0yIiYuXKlXHllVfG73//+1ixYkXceeed8bnPfW7U5/T09MSqVatG7O/t7Y22trbxnhoAaKLBwcFYunRpDAwMRHt7+5jHFQqLo446Krq7u+OJJ54Y3vfVr341tm7dGr/97W9Hfc5oVyy6urqiv7//kBMrqlarRV9fX9yyrSWqQ4e+VzzQGOWWeqzuHopFixZFa2trs6cDJKpUKtHR0fGmYVHoo5DOzs447bTTDtp36qmnxn333Tfmc8rlcpTL5RH7W1tbG/LCUx0qRXW/sIBmatT6BppnvGu60H83Pffcc+Ppp58+aN9f/vKXmDNnTpFhAIBJqlBYfO1rX4stW7bEmjVr4tlnn43e3t5Yv359LF++vFHzAwAmkEJhcdZZZ8X9998fv/jFL2LevHmxevXquP3222PZsmWNmh8AMIEU+o5FRMQll1wSl1xySSPmAgBMcO4VAgCkERYAQBphAQCkERYAQBphAQCkERYAQBphAQCkERYAQBphAQCkERYAQBphAQCkERYAQBphAQCkERYAQBphAQCkERYAQBphAQCkERYAQBphAQCkERYAQBphAQCkERYAQBphAQCkERYAQBphAQCkERYAQBphAQCkERYAQBphAQCkERYAQBphAQCkERYAQBphAQCkERYAQBphAQCkERYAQBphAQCkERYAQBphAQCkERYAQBphAQCkERYAQBphAQCkERYAQBphAQCkERYAQBphAQCkERYAQJpCYdHT0xOlUumgn1mzZjVqbgDABDO16BNOP/30+PWvfz28PWXKlNQJAQATV+GwmDp1qqsUAMCoCofFM888E7Nnz45yuRxnn312rFmzJt773veOeXy1Wo1qtTq8XalUIiKiVqtFrVY7jCmP7sBY5ZZ62phAMQfWX+baBv5vGO+6LtXr9XG/Ez/yyCMxODgYp5xySrz00ktx6623xp///Od46qmnYsaMGaM+p6enJ1atWjVif29vb7S1tY331ABAEw0ODsbSpUtjYGAg2tvbxzyuUFj8t3379sX73ve+uPHGG2PlypWjHjPaFYuurq7o7+8/5MSKqtVq0dfXF7dsa4nqUCltXGD8yi31WN09FIsWLYrW1tZmTwdIVKlUoqOj403DovBHIf/bMcccEx/84AfjmWeeGfOYcrkc5XJ5xP7W1taGvPBUh0pR3S8soJkatb6B5hnvmn5Lf8eiWq3Gn/70p+js7HwrwwAAk0ShsPj6178emzdvjl27dsXvfve7+PSnPx2VSiWuueaaRs0PAJhACn0U8o9//CM+85nPRH9/f7zrXe+Kc845J7Zs2RJz5sxp1PwAgAmkUFhs2LChUfMAACYB9woBANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANK8pbBYu3ZtlEqlWLFiRdJ0AICJ7LDDYuvWrbF+/fqYP39+5nwAgAnssMJi7969sWzZsrjrrrvine98Z/acAIAJ6rDCYvny5XHxxRfHhRdemD0fAGACm1r0CRs2bIgnn3wytm7dOq7jq9VqVKvV4e1KpRIREbVaLWq1WtHTj+nAWOWWetqYQDEH1l/m2gb+bxjvui4UFrt3744bbrghNm7cGNOmTRvXc9auXRurVq0asX/jxo3R1tZW5PTjsrp7KH1MoJi+vr5mTwFINjg4OK7jSvV6fdy/4j/wwANxxRVXxJQpU4b37d+/P0qlUrS0tES1Wj3osYjRr1h0dXVFf39/tLe3j/fUb6pWq0VfX1/csq0lqkOltHGB8Su31GN195B1CE30x56LGjJupVKJjo6OGBgYOOT7d6ErFhdccEHs3LnzoH1f+MIX4gMf+EB84xvfGBEVERHlcjnK5fKI/a2trdHa2lrk9ONSHSpFdb8XNGgm6xCapxHvrUXGLRQW06dPj3nz5h2075hjjokZM2aM2A8AvP34y5sAQJrC/yvkv23atClhGgDAZOCKBQCQRlgAAGmEBQCQRlgAAGmEBQCQRlgAAGmEBQCQRlgAAGmEBQCQRlgAAGmEBQCQRlgAAGmEBQCQRlgAAGmEBQCQRlgAAGmEBQCQRlgAAGmEBQCQRlgAAGmEBQCQRlgAAGmEBQCQRlgAAGmEBQCQRlgAAGmEBQCQRlgAAGmEBQCQRlgAAGmEBQCQRlgAAGmEBQCQRlgAAGmEBQCQRlgAAGmEBQCQRlgAAGmEBQCQRlgAAGmEBQCQRlgAAGmEBQCQRlgAAGmEBQCQRlgAAGmEBQCQRlgAAGkKhcW6deti/vz50d7eHu3t7bFgwYJ45JFHGjU3AGCCKRQWJ554Ynzve9+Lbdu2xbZt2+L888+Pyy67LJ566qlGzQ8AmECmFjn40ksvPWj7u9/9bqxbty62bNkSp59+eurEAICJp1BY/G/79++PX/7yl7Fv375YsGDBmMdVq9WoVqvD25VKJSIiarVa1Gq1wz39CAfGKrfU08YEijmw/qxDaJ7M99bDGbdUr9cLvQLs3LkzFixYEP/+97/jHe94R/T29sYnPvGJMY/v6emJVatWjdjf29sbbW1tRU4NADTJ4OBgLF26NAYGBqK9vX3M4wqHxRtvvBHPPfdcvP7663HffffFT37yk9i8eXOcdtppox4/2hWLrq6u6O/vP+TEiqrVatHX1xe3bGuJ6lApbVxg/Mot9VjdPWQdQhP9seeihoxbqVSio6PjTcOi8EchRx11VLz//e+PiIju7u7YunVr/PCHP4w777xz1OPL5XKUy+UR+1tbW6O1tbXo6d9UdagU1f1e0KCZrENonka8txYZ9y3/HYt6vX7QFQkA4O2r0BWLm266KZYsWRJdXV2xZ8+e2LBhQ2zatCkeffTRRs0PAJhACoXFSy+9FFdffXW88MILceyxx8b8+fPj0UcfjUWLFjVqfgDABFIoLH760582ah4AwCTgXiEAQBphAQCkERYAQBphAQCkERYAQBphAQCkERYAQBphAQCkERYAQBphAQCkERYAQBphAQCkERYAQBphAQCkERYAQBphAQCkERYAQBphAQCkERYAQBphAQCkERYAQBphAQCkERYAQBphAQCkERYAQBphAQCkERYAQBphAQCkERYAQBphAQCkERYAQBphAQCkERYAQBphAQCkERYAQBphAQCkERYAQBphAQCkERYAQBphAQCkERYAQBphAQCkERYAQBphAQCkERYAQBphAQCkERYAQBphAQCkKRQWa9eujbPOOiumT58eM2fOjMsvvzyefvrpRs0NAJhgCoXF5s2bY/ny5bFly5bo6+uL//znP7F48eLYt29fo+YHAEwgU4sc/Oijjx60fffdd8fMmTNj+/bt8dGPfjR1YgDAxFMoLP7bwMBAREQcf/zxYx5TrVajWq0Ob1cqlYiIqNVqUavV3srpD3JgrHJLPW1MoJgD6886hObJfG89nHFL9Xr9sF4B6vV6XHbZZfHaa6/F448/PuZxPT09sWrVqhH7e3t7o62t7XBODQAcYYODg7F06dIYGBiI9vb2MY877LBYvnx5PPTQQ/Gb3/wmTjzxxDGPG+2KRVdXV/T39x9yYkXVarXo6+uLW7a1RHWolDYuMH7llnqs7h6yDqGJ/thzUUPGrVQq0dHR8aZhcVgfhVx//fXx4IMPxmOPPXbIqIiIKJfLUS6XR+xvbW2N1tbWwzn9IVWHSlHd7wUNmsk6hOZpxHtrkXELhUW9Xo/rr78+7r///ti0aVPMnTv3sCYHAExOhcJi+fLl0dvbG7/61a9i+vTp8eKLL0ZExLHHHhtHH310QyYIAEwchf6Oxbp162JgYCDOO++86OzsHP659957GzU/AGACKfxRCADAWNwrBABIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIUzgsHnvssbj00ktj9uzZUSqV4oEHHmjAtACAiahwWOzbty/OOOOMuOOOOxoxHwBgApta9AlLliyJJUuWNGIuAMAE5zsWAECawlcsiqpWq1GtVoe3K5VKRETUarWo1Wpp5zkwVrmlnjYmUMyB9WcdQvNkvrcezrgND4u1a9fGqlWrRuzfuHFjtLW1pZ9vdfdQ+phAMdYhNM/DDz/ckHEHBwfHdVypXq8f9q8WpVIp7r///rj88svHPGa0KxZdXV3R398f7e3th3vqEWq1WvT19cUt21qiOlRKGxcYv3JLPVZ3D1mH0ER/7LmoIeNWKpXo6OiIgYGBQ75/N/yKRblcjnK5PGJ/a2trtLa2pp+vOlSK6n4vaNBM1iE0TyPeW4uMWzgs9u7dG88+++zw9q5du2LHjh1x/PHHx0knnVR0OABgEikcFtu2bYuFCxcOb69cuTIiIq655pq455570iYGAEw8hcPivPPOi7fwtQwAYBLzdywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIc1hh8eMf/zjmzp0b06ZNizPPPDMef/zx7HkBABNQ4bC49957Y8WKFXHzzTfHH/7wh/jIRz4SS5Ysieeee64R8wMAJpDCYXHbbbfFF7/4xfjSl74Up556atx+++3R1dUV69ata8T8AIAJZGqRg994443Yvn17fPOb3zxo/+LFi+OJJ54Y9TnVajWq1erw9sDAQEREvPrqq1Gr1YrOd0y1Wi0GBwdjaq0l9g+V0sYFxm/qUD0GB4esQ2iiV155pSHj7tmzJyIi6vX6IY8rFBb9/f2xf//+OOGEEw7af8IJJ8SLL7446nPWrl0bq1atGrF/7ty5RU4NTBBLmz0BeJvr+H+NHX/Pnj1x7LHHjvl4obA4oFQ6+DeRer0+Yt8B3/rWt2LlypXD20NDQ/Hqq6/GjBkzxnzO4ahUKtHV1RW7d++O9vb2tHGB8bMOYfKq1+uxZ8+emD179iGPKxQWHR0dMWXKlBFXJ15++eURVzEOKJfLUS6XD9p33HHHFTltIe3t7V7QoMmsQ5icDnWl4oBCX9486qij4swzz4y+vr6D9vf19cWHP/zhYrMDACadwh+FrFy5Mq6++uro7u6OBQsWxPr16+O5556Lr3zlK42YHwAwgRQOi6uuuipeeeWV+M53vhMvvPBCzJs3Lx5++OGYM2dOI+Y3buVyOb797W+P+NgFOHKsQ6BUf7P/NwIAME7uFQIApBEWAEAaYQEApBEWAECaCRcWjz32WFx66aUxe/bsKJVK8cADDxz0eL1ej56enpg9e3YcffTRcd5558VTTz3VnMnCJLd27doolUqxYsWK4X2f//zno1QqHfRzzjnnNG+SwBE14cJi3759ccYZZ8Qdd9wx6uM/+MEP4rbbbos77rgjtm7dGrNmzYpFixYN3zwFyLF169ZYv359zJ8/f8RjH//4x+OFF14Y/nn44YebMEOgGQ7rXiHNtGTJkliyZMmoj9Xr9bj99tvj5ptvjk9+8pMREfGzn/0sTjjhhOjt7Y0vf/nLR3KqMGnt3bs3li1bFnfddVfceuutIx4vl8sxa9asJswMaLYJd8XiUHbt2hUvvvhiLF68eHhfuVyOj33sY2Pe1h0obvny5XHxxRfHhRdeOOrjmzZtipkzZ8Ypp5wS1157bbz88stHeIZAs0y4KxaHcuDmaKPd1v3vf/97M6YEk86GDRviySefjK1bt476+JIlS+LKK6+MOXPmxK5du+KWW26J888/P7Zv3+4vcsLbwKQKiwOK3NYdGL/du3fHDTfcEBs3boxp06aNesxVV101/O958+ZFd3d3zJkzJx566KHhjyiByWtSfRRy4DPdIrd1B8Zv+/bt8fLLL8eZZ54ZU6dOjalTp8bmzZvjRz/6UUydOjX2798/4jmdnZ0xZ86ceOaZZ5owY+BIm1RhMXfu3Jg1a9ZBt3V/4403YvPmzW7rDgkuuOCC2LlzZ+zYsWP4p7u7O5YtWxY7duyIKVOmjHjOK6+8Ert3747Ozs4mzBg40ibcRyF79+6NZ599dnh7165dsWPHjjj++OPjpJNOihUrVsSaNWvi5JNPjpNPPjnWrFkTbW1tsXTp0ibOGiaH6dOnx7x58w7ad8wxx8SMGTNi3rx5sXfv3ujp6YlPfepT0dnZGX/729/ipptuio6OjrjiiiuaNGvgSJpwYbFt27ZYuHDh8PbKlSsjIuKaa66Je+65J2688cb417/+Fdddd1289tprcfbZZ8fGjRtj+vTpzZoyvG1MmTIldu7cGT//+c/j9ddfj87Ozli4cGHce++91iC8TbhtOgCQZlJ9xwIAaC5hAQCkERYAQBphAQCkERYAQBphAQCkERYAQBphAQCkERYAQBphAQCkERYAQBphAQCk+f8fyl1/uTGxnwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#2nd dataset, bins = 2\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = [10, 11, 12, 13, 14, 30, 50, 70, 72, 72]\n",
    "plt.hist(x, bins=2 , range=[10,80])\n",
    "plt.yticks(range(0, 7,1))\n",
    "plt.xticks([10, 45])\n",
    "plt.grid()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "352442b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#2nd dataset, bins = 19\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = [10, 11, 12, 13, 14, 30, 50, 70, 72, 72]\n",
    "plt.hist(x, bins=19 , range=[10,80])\n",
    "plt.yticks(range(0, 7,1))\n",
    "plt.xticks(range(10, 80, 4))\n",
    "plt.grid()\n",
    "plt.show()\n",
    "\n",
    "#Bad choice for bins value!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b20bfc24",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{10, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 32, 34, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 53, 54, 56, 57, 59, 60, 61, 63, 64, 65, 66, 67, 71, 72, 73, 74, 75, 77, 78, 79}\n"
     ]
    }
   ],
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a6c7577a",
   "metadata": {},
   "outputs": [
    {
     "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 matplotlib.pyplot as plt\n",
    "\n",
    "plt.hist(x, bins=10 , range=[0,100])\n",
    "plt.yticks(range(0, 11,1))\n",
    "plt.xticks(range(0, 100,10))\n",
    "plt.grid()\n",
    "plt.show()\n",
    "\n",
    "#Nilai bins yang harus digunakan ialah 10 karena pada nilai bins ini grafik terlihat jelas dan \n",
    "#tidak ada kekosongan pada tengah tengah histogram.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "d3f79e07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "N = 1000\n",
      "xmin = 20\n",
      "xmax = 60\n",
      "mean = 39.864\n"
     ]
    }
   ],
   "source": [
    "#Discrete uniform distribution\n",
    "#Generate dataset\n",
    "\n",
    "import statistics as stat\n",
    "import random as rnd\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",
    "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": 46,
   "id": "9b300306",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Histogram\n",
    "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": 47,
   "id": "c9cf0d19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Repeat it (2)\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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "9bb9d407",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Repeat it (3)\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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "28b7a1b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Repeat it (4)\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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "1fb4dfac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Repeat it (5)\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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "d3f23888",
   "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 matplotlib.pyplot as plt\n",
    "import numpy as np\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",
    "plt.hist(x, bins=10)\n",
    "plt.xticks(range(20, 64,4))\n",
    "plt.xlim([20,60])\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c01fd504",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Exercise 3\n",
    "1. N mewakili jumlah titik data yang akan dihasilkan untuk dataset. \n",
    "Kode ini akan membuat dataset dengan 1000 titik data.\n",
    "2. Rata-rata nilai yang sekitar 100 bisa ditemukan dari rumus 1/n karena :\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\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "22268f8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#N=10\n",
    "N = 10; a = 1; b = 10; 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(range(1, 11,1))\n",
    "plt.xlim([1, 10])\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "d3d1b801",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#N =100\n",
    "N = 100; a = 1; b = 10; 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(range(1, 11,1))\n",
    "plt.xlim([1, 10])\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "972fd94f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#N =1000\n",
    "N = 1000; a = 1; b = 10; 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(range(1, 11,1))\n",
    "plt.xlim([1, 10])\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "ef00389b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#N = 10^4\n",
    "N = 10000; a = 1; b = 10; 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(range(1, 11,1))\n",
    "plt.xlim([1, 10])\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "59896355",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#N =10^5\n",
    "N = 100000; a = 1; b = 10; 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(range(1, 11,1))\n",
    "plt.xlim([1, 10])\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "8733b1db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#N =10^6\n",
    "N = 1000000; a = 1; b = 10; 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(range(1, 11,1))\n",
    "plt.xlim([1, 10])\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "27d220d5",
   "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": 60,
   "id": "f232ceb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Correlation\n",
    "# dataset 1 : x\n",
    "N = 1000; a = 1; b = 10; 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(range(1, 11,1))\n",
    "plt.xlim([1, 10])\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "8b366c99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Correlation\n",
    "# dataset 1 : y\n",
    "N = 1000; a = 1; b = 10; y = []\n",
    "for i in range(N):\n",
    "    yi = rnd.randint(a, b)\n",
    "    y.append(yi)\n",
    "plt.hist(y, bins=10, color='tab:red')\n",
    "plt.xticks(np.arange(1, 11,1))\n",
    "plt.xlim([1, 10])\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "fcc167c2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.         0.03996468]\n",
      " [0.03996468 1.        ]]\n"
     ]
    }
   ],
   "source": [
    "#dataset 1 : No Correlation\n",
    "import matplotlib.pyplot as plt\n",
    "plt.scatter(x, y)\n",
    "plt.show()\n",
    "\n",
    "import numpy as np\n",
    "cc = np.corrcoef(x, y)\n",
    "print(cc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "0642acbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#dataset 2 : x\n",
    "N = 1000; a = 1; b = 10; 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(range(1, 11,1))\n",
    "plt.xlim([1, 10])\n",
    "plt.grid()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "db16598e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# dataset 2 : y\n",
    "y = [11-i for i in x]\n",
    "plt.hist(y, bins=10, color='tab:red')\n",
    "plt.xticks(np.arange(1, 11,1))\n",
    "plt.xlim([1, 10])\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "22b5ddb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EBwCQlPgAAJISHwBAUuIDAEhKfAAASYkPACAp8QEAJCU+AICkxAcAkNRxlR4AgKHrpv9YH5v/59Dz6eMiVs9trtxAVAU7HwCUxYSFPcMjImLz/xw8Tm0THwDkrrfAECC1TXwAkKub/qNvYdHXdQw94gOAXB1+qmWg6xh6xAcAkJT4AACSEh8A5Gr6uHzXMfSIDwBy1df7eLjfR+0SHwDkrn3pscOit9cZ2sQHAGXRvrT5iFMr08cJD9xeHYAycmqFo7HzAQAkJT4AgKTEBwCQlPgAAJISHwBAUuIDAEhKfAAASYkPACAp8QEAJCU+AICkxAcAkJT4AACSEh8AQFLiAwBISnwAAEmJDwAgKfEBACQlPgCApMQHAJCU+AAAkhIfAEBS4gMASEp8AABJiQ8AICnxAQAkdVylBwCAavetZetjy65Dz6eeEvF/FzRXbqBBLvedj5aWlrj44oujrq4uRo8eHVdffXW89dZbeb8NACQxYWHP8IiI2LLr4HE+m9zjY+PGjTF79ux46aWXYsOGDbFv376YMWNG7N27N++3AoCy6i0wBMhnU8iyLCvnG3z44YcxevTo2LhxY1x66aW9ri+VStHQ0BCdnZ1RX19fztEA4FMdfqrl0zgFc1B/vr/Lfs1HZ2dnRESceuqpR329q6srurq6up+XSqVyjwQAvepLePRnHYeU9dcuWZbF/PnzY/r06TF58uSjrmlpaYmGhobuR1NTUzlHAgAqrKzxMWfOnHjttdfi8ccf/9Q1ixYtis7Ozu5HR0dHOUcCACqsbKddbr311li3bl1s2rQpxo8f/6nrisViFIvFco0BAJ/J1FP6dkpl6inln2WoyX3nI8uymDNnTjz11FPx/PPPx8SJE/N+CwAou75eROpi0/7LPT5mz54dq1evjjVr1kRdXV3s2LEjduzYER9//HHebwUAZdW+9Nhh0dvrHF3uP7UtFApHPf7YY4/FzTff3Ou/91NbAKqNO5z2rqI/tS3zbUMAIDmhkS9/WA4ASEp8AABJiQ8AICnxAQAkJT4AgKTEBwCQlPgAAJISHwBAUuIDAEhKfAAASYkPACAp8QEAJCU+AICkxAcAkJT4AACSEh8AQFLiAwBISnwAAEmJDwAgKfEBACQlPgCApMQHAJCU+AAAkhIfAEBS4gMASOq4Sg8AAKQx4871sW3/oedfGB7x7OLm5HPY+QCAGjBhYc/wiIjYtv/g8dTEBwAMcb0FRuoAER8AMITNuLNvYdHXdXkQHwAwhB1+qmWg6/IgPgCApMQHAJCU+ACAIewLw/NdlwfxAQBDWF/v45Hyfh/iAwCGuPalxw6L3l7Pm/gAgBrQvrT5iFMrXxiePjwi3F4dAGpGJW6lfjR2PgCApMQHAJCU+AAAkhIfAEBS4gMASEp8AABJiQ8AICnxAQAkJT4AgKSq7g6nWZZFRESpVKrwJABAX/3ze/uf3+PHUnXxsXv37oiIaGpqqvAkAEB/7d69OxoaGo65ppD1JVESOnDgQHzwwQdRV1cXhUKh0uNUpVKpFE1NTdHR0RH19fWVHqfm+Tyqj8+kuvg8qku5Po8sy2L37t3R2NgYw4Yd+6qOqtv5GDZsWIwfP77SYwwK9fX1/odcRXwe1cdnUl18HtWlHJ9Hbzse/+SCUwAgKfEBACQlPgahYrEYd999dxSLxUqPQvg8qpHPpLr4PKpLNXweVXfBKQAwtNn5AACSEh8AQFLiAwBISnwAAEmJj0GkpaUlLr744qirq4vRo0fH1VdfHW+99Valx+IfWlpaolAoxLx58yo9Ss16//3346abbopRo0bFyJEj48ILL4ytW7dWeqyatG/fvrjrrrti4sSJMWLEiDjjjDPivvvuiwMHDlR6tJqxadOmmDlzZjQ2NkahUIinn366x+tZlsU999wTjY2NMWLEiLj88svjjTfeSDKb+BhENm7cGLNnz46XXnopNmzYEPv27YsZM2bE3r17Kz1azWttbY2VK1fG+eefX+lRatauXbti2rRpcfzxx8czzzwTb775Zjz44INx8sknV3q0mrRs2bJ45JFHYsWKFfH3v/89HnjggfjZz34Wv/jFLyo9Ws3Yu3dvXHDBBbFixYqjvv7AAw/E8uXLY8WKFdHa2hpjx46NK6+8svtvrJWTn9oOYh9++GGMHj06Nm7cGJdeemmlx6lZe/bsiS9+8Yvxy1/+Mu6///648MIL46GHHqr0WDVn4cKF8Ze//CX+/Oc/V3oUIuJrX/tajBkzJn71q191H/vmN78ZI0eOjN/+9rcVnKw2FQqFWLt2bVx99dURcXDXo7GxMebNmxcLFiyIiIiurq4YM2ZMLFu2LL73ve+VdR47H4NYZ2dnRESceuqpFZ6kts2ePTuam5vjiiuuqPQoNW3dunUxderUuPbaa2P06NExZcqUePTRRys9Vs2aPn16PPfcc7Ft27aIiHj11Vdj8+bNcdVVV1V4MiIi3n333dixY0fMmDGj+1ixWIzLLrssXnjhhbK/f9X9YTn6JsuymD9/fkyfPj0mT55c6XFq1hNPPBGvvPJKtLa2VnqUmvfOO+/Eww8/HPPnz48f/ehH8fLLL8dtt90WxWIxvvOd71R6vJqzYMGC6OzsjEmTJsXw4cNj//79sXjx4rj++usrPRoRsWPHjoiIGDNmTI/jY8aMie3bt5f9/cXHIDVnzpx47bXXYvPmzZUepWZ1dHTE3Llz49lnn40TTzyx0uPUvAMHDsTUqVNjyZIlERExZcqUeOONN+Lhhx8WHxXw5JNPxurVq2PNmjVx7rnnRltbW8ybNy8aGxtj1qxZlR6PfygUCj2eZ1l2xLFyEB+D0K233hrr1q2LTZs2xfjx4ys9Ts3aunVr7Ny5My666KLuY/v3749NmzbFihUroqurK4YPH17BCWvLuHHj4pxzzulx7Oyzz47f/e53FZqott1xxx2xcOHCuO666yIi4rzzzovt27dHS0uL+KgCY8eOjYiDOyDjxo3rPr5z584jdkPKwTUfg0iWZTFnzpx46qmn4vnnn4+JEydWeqSa9pWvfCVef/31aGtr635MnTo1brzxxmhraxMeiU2bNu2In55v27YtTj/99ApNVNs++uijGDas51fM8OHD/dS2SkycODHGjh0bGzZs6D72ySefxMaNG+OSSy4p+/vb+RhEZs+eHWvWrInf//73UVdX133OrqGhIUaMGFHh6WpPXV3dEdfbnHTSSTFq1CjX4VTA7bffHpdcckksWbIkvv3tb8fLL78cK1eujJUrV1Z6tJo0c+bMWLx4cZx22mlx7rnnxl//+tdYvnx5fPe73630aDVjz5498fbbb3c/f/fdd6OtrS1OPfXUOO2002LevHmxZMmSOOuss+Kss86KJUuWxMiRI+OGG24o/3AZg0ZEHPXx2GOPVXo0/uGyyy7L5s6dW+kxatYf/vCHbPLkyVmxWMwmTZqUrVy5stIj1axSqZTNnTs3O+2007ITTzwxO+OMM7I777wz6+rqqvRoNeNPf/rTUb8zZs2alWVZlh04cCC7++67s7Fjx2bFYjG79NJLs9dffz3JbO7zAQAk5ZoPACAp8QEAJCU+AICkxAcAkJT4AACSEh8AQFLiAwBISnwAAEmJDwAgKfEBACQlPgCApMQHAJDU/weK90C8kiE+RQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1. -1.]\n",
      " [-1.  1.]]\n"
     ]
    }
   ],
   "source": [
    "#dataset 2 = Negative Correlation\n",
    "import matplotlib.pyplot as plt\n",
    "plt.scatter(x, y)\n",
    "plt.show()\n",
    "\n",
    "import numpy as np\n",
    "cc = np.corrcoef(x, y)\n",
    "print(cc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "3ae71f00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#dataset 3 : x\n",
    "N = 1000; a = 1; b = 10; 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(range(1, 11,1))\n",
    "plt.xlim([1, 10])\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "cc29a31e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# dataset 3 : y\n",
    "y = [i for i in x]\n",
    "plt.hist(y, bins=10, color='tab:red')\n",
    "plt.xticks(np.arange(1, 11,1))\n",
    "plt.xlim([1, 10])\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "ce833766",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 1.]\n",
      " [1. 1.]]\n"
     ]
    }
   ],
   "source": [
    "#dataset 2 = Positive Correlation\n",
    "import matplotlib.pyplot as plt\n",
    "plt.scatter(x, y)\n",
    "plt.show()\n",
    "\n",
    "import numpy as np\n",
    "cc = np.corrcoef(x, y)\n",
    "print(cc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "e28fa0f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.         0.96039071]\n",
      " [0.96039071 1.        ]]\n"
     ]
    }
   ],
   "source": [
    "# A little bit random (1)\n",
    "import random as rnd\n",
    "y = [rnd.randint(-1, 1) + i for i in x]\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.scatter(x,y)\n",
    "plt.show()\n",
    "\n",
    "import numpy as np\n",
    "cc = np.corrcoef(x, y)\n",
    "print(cc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "60958fe5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.         0.90220854]\n",
      " [0.90220854 1.        ]]\n"
     ]
    }
   ],
   "source": [
    "# A little bit random (2)\n",
    "import random as rnd\n",
    "y = [rnd.randint(-2, 2) + i for i in x]\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.scatter(x,y)\n",
    "plt.show()\n",
    "\n",
    "import numpy as np\n",
    "cc = np.corrcoef(x, y)\n",
    "print(cc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "b8c4363d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.         0.81534209]\n",
      " [0.81534209 1.        ]]\n"
     ]
    }
   ],
   "source": [
    "# A little bit random (3)\n",
    "import random as rnd\n",
    "y = [rnd.randint(-3, 3) + i for i in x]\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.scatter(x,y)\n",
    "plt.show()\n",
    "\n",
    "import numpy as np\n",
    "cc = np.corrcoef(x, y)\n",
    "print(cc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "1551603e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.         0.99482892]\n",
      " [0.99482892 1.        ]]\n"
     ]
    }
   ],
   "source": [
    "# A little bit random (note)\n",
    "import random as rnd\n",
    "N = 100; a = 1; b = 100; x = []\n",
    "for i in range(N):\n",
    "    xi = 0.1 * rnd.randint(a, b)\n",
    "    x.append(xi)\n",
    "\n",
    "y = [0.1* rnd.randint(-5,5) + i for i in x]\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.scatter(x,y)\n",
    "plt.show()\n",
    "\n",
    "import numpy as np\n",
    "cc = np.corrcoef(x, y)\n",
    "print(cc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "24d747c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Normal Distribution\n",
    "# N=100000, Mu=100, Sin=5, bins=10\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "mu = 100\n",
    "si = 5\n",
    "N = 100000\n",
    "values = np.random.normal(mu, si, N)\n",
    "\n",
    "plt.hist(values, 10)\n",
    "plt.axvline(values.mean(), color='r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "8ec5b6a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Normal Distribution\n",
    "# N=100000, Mu=100, Sin=5, bins=50\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "mu = 100\n",
    "si = 5\n",
    "N = 100000\n",
    "values = np.random.normal(mu, si, N)\n",
    "\n",
    "plt.hist(values, 50)\n",
    "plt.axvline(values.mean(), color='r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "2c8ae987",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Normal Distribution\n",
    "# N=100000, Mu=100, Sin=5, bins=100\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "mu = 100\n",
    "si = 5\n",
    "N = 100000\n",
    "values = np.random.normal(mu, si, N)\n",
    "\n",
    "plt.hist(values, 100)\n",
    "plt.axvline(values.mean(), color='r')\n",
    "plt.show()\n",
    "\n",
    "#bins yang direkomendasikan sekitar 50-100 dan \n",
    "#lebih dari 10 tapi kurang dari 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "52fcd5f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#set xlim option\n",
    "# N=100000, Mu=100, Sin=5, bins=100\n",
    "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([20,160])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "b565ee64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#set xlim option\n",
    "# N=100000, Mu=100, Sin=10, bins=100\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "mu = 100\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([20,160])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "f3b71279",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#set xlim option\n",
    "# N=100000, Mu=100, Sin=20, bins=100\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "mu = 100\n",
    "si = 20\n",
    "N = 100000\n",
    "values = np.random.normal(mu, si, N)\n",
    "\n",
    "plt.hist(values, 100)\n",
    "plt.axvline(values.mean(), color='r')\n",
    "plt.xlim([20,160])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "e4f008b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# N=100000, Mu=120, Sin=10, bins=100\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "mu = 120\n",
    "si = 10\n",
    "N = 100000\n",
    "v1 = np.random.normal(mu, si, N)\n",
    "\n",
    "plt.hist(v1, 100)\n",
    "plt.axvline(v1.mean(), color='r')\n",
    "plt.xlim([20,160])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "787c3aa2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# N=100000, Mu=60, Sin=10, bins=100\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "mu = 60\n",
    "si = 10\n",
    "N = 100000\n",
    "v2 = np.random.normal(mu, si, N)\n",
    "\n",
    "plt.hist(v2, 100)\n",
    "plt.axvline(v2.mean(), color='r')\n",
    "plt.xlim([20,160])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "1dd59217",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Merge 2 distribution 1\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "v3 = np.concatenate((v2, v1))\n",
    "plt.hist(v3, 100)\n",
    "plt.axvline(v1.mean(), color='r')\n",
    "plt.axvline(v2.mean(), color='r')\n",
    "plt.xlim([20,160])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "75d953e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lcmnixIm6dOmSXZOfn6+dO3eqrKxMe/fuVWNjo3JycnT16lW7Ji8vT9XV1SovL1d5ebmqq6vl8XgCvUsAACCEhAd6gy+//LJSUlL0xhtv2MsGDhxof21ZltasWaPly5dr6tSpkqQtW7YoOTlZ27dv1+zZs+Xz+bRp0yZt3bpVEyZMkCSVlpYqJSVFe/bs0aRJk1RTU6Py8nIdOHBAmZmZkqSNGzcqKytLJ06c0ODBgwO9awB6kIFLd3VY9tnKKUEYCYCeJuAzR2+//baGDx+uH//4x0pKStKwYcO0ceNGe31tba3q6uqUnZ1tL4uKitLYsWO1b98+SVJVVZVaW1v9atxut9LT0+2a/fv3y+l02sFIkkaOHCmn02nXAL3RwKW7/F4AgMAKeDj6y1/+onXr1iktLU3vvvuunn76aS1YsEC//e1vJUl1dXWSpOTkZL/PJScn2+vq6uoUGRmpfv36XbcmKSmpw/dPSkqya9prbm5WQ0OD3wsAAMAU8MNqbW1tGj58uAoLCyVJw4YN07Fjx7Ru3To99dRTdp3D4fD7nGVZHZa1176ms/rrbaeoqEgrVqy46X0BAAChJ+AzR/fee68eeOABv2VDhgzR6dOnJUkul0uSOszu1NfX27NJLpdLLS0t8nq91605f/58h+9/4cKFDrNS1yxbtkw+n89+nTlz5jb2EAAA9GYBD0ejR4/WiRMn/JadPHlS9913nyQpNTVVLpdLFRUV9vqWlhZVVlZq1KhRkqSMjAxFRET41Zw7d05Hjx61a7KysuTz+XTo0CG75uDBg/L5fHZNe1FRUYqLi/N7AQAAmAJ+WO3ZZ5/VqFGjVFhYqGnTpunQoUPasGGDNmzYIOnrQ2H5+fkqLCxUWlqa0tLSVFhYqL59+yovL0+S5HQ6NWPGDC1atEgJCQmKj4/X4sWLNXToUPvqtSFDhmjy5MmaOXOm1q9fL0maNWuWcnJyuFINAADctoCHoxEjRmjnzp1atmyZXnzxRaWmpmrNmjV64okn7JolS5aoqalJc+bMkdfrVWZmpnbv3q3Y2Fi7ZvXq1QoPD9e0adPU1NSk8ePHa/PmzQoLC7Nrtm3bpgULFthXteXm5qqkpCTQuwQAAEKIw7IsK9iDCJaGhgY5nU75fD4OsaHHuFsv349u+Uo1q/9BkjTk2X9RU2SfII+oo6Dd5+jyZelb3/r668ZGKSYmOOO429AX3Kbu/vvNs9UAAAAMhCMAAAAD4QgAAMBAOAIAADAQjgAAAAyEIwAAAAPhCAAAwEA4AgAAMBCOAAAADIQjAAAAA+EIAADAQDgCAAAwEI4AAAAMhCMAAAAD4QgAAMAQHuwBAMCdMnDpLr/3n62cEqSRALibMXMEAABgIBwBAAAYOKwGALhjzEOb0S1fqSaIYwG6QjgCANx1OD8MwcRhNQAAAAMzR8BdrP2/ngEA3Y+ZIwAAAAPhCAAAwEA4AgAAMHDOEQCg29zseXNDXihXU2Sfbh4NcHOYOQIAADAwcwQACAiurkRvwcwRAACAgXAEAABg4LAaAOCu19khOx4pgu7CzBEAAICBcAQAAGAgHAEAABg45wgA0CNxHhK6CzNHAAAABmaOAAC3jBs+ojdj5ggAAMBAOAIAADAQjgAAAAyEIwAAAAPhCAAAwMDVasBdgqt/7jzukwOgM4QjAMANEd4RSjisBgAAYCAcAQAAGAhHAAAABs45AgD0Gu3PjeIEe9wOZo4AAAAMhCMAAAAD4QgAAMBAOAIAADAQjgAAAAyEIwAAAAPhCAAAwEA4AgAAMHR7OCoqKpLD4VB+fr69zLIsFRQUyO12Kzo6WuPGjdOxY8f8Ptfc3Kz58+crMTFRMTExys3N1dmzZ/1qvF6vPB6PnE6nnE6nPB6PLl682N27BAAAerFuDUeHDx/Whg0b9L3vfc9v+apVq1RcXKySkhIdPnxYLpdLEydO1KVLl+ya/Px87dy5U2VlZdq7d68aGxuVk5Ojq1ev2jV5eXmqrq5WeXm5ysvLVV1dLY/H0527BAAAerlue3xIY2OjnnjiCW3cuFG//OUv7eWWZWnNmjVavny5pk6dKknasmWLkpOTtX37ds2ePVs+n0+bNm3S1q1bNWHCBElSaWmpUlJStGfPHk2aNEk1NTUqLy/XgQMHlJmZKUnauHGjsrKydOLECQ0ePLi7dg1AL9b+8RMSj6AAQk23zRzNnTtXU6ZMscPNNbW1taqrq1N2dra9LCoqSmPHjtW+ffskSVVVVWptbfWrcbvdSk9Pt2v2798vp9NpByNJGjlypJxOp13TXnNzsxoaGvxeAAAApm6ZOSorK9ORI0d0+PDhDuvq6uokScnJyX7Lk5OTderUKbsmMjJS/fr161Bz7fN1dXVKSkrqsP2kpCS7pr2ioiKtWLHi1ncIAEJIZ7NnPRUzgbgdAZ85OnPmjJ555hmVlpaqT58+XdY5HA6/95ZldVjWXvuazuqvt51ly5bJ5/PZrzNnzlz3+wEAgNAT8HBUVVWl+vp6ZWRkKDw8XOHh4aqsrNRvfvMbhYeH2zNG7Wd36uvr7XUul0stLS3yer3XrTl//nyH73/hwoUOs1LXREVFKS4uzu8FAABgCng4Gj9+vD755BNVV1fbr+HDh+uJJ55QdXW1Bg0aJJfLpYqKCvszLS0tqqys1KhRoyRJGRkZioiI8Ks5d+6cjh49atdkZWXJ5/Pp0KFDds3Bgwfl8/nsGgAAgFsV8HOOYmNjlZ6e7rcsJiZGCQkJ9vL8/HwVFhYqLS1NaWlpKiwsVN++fZWXlydJcjqdmjFjhhYtWqSEhATFx8dr8eLFGjp0qH2C95AhQzR58mTNnDlT69evlyTNmjVLOTk5XKkGAABuW7ddyn89S5YsUVNTk+bMmSOv16vMzEzt3r1bsbGxds3q1asVHh6uadOmqampSePHj9fmzZsVFhZm12zbtk0LFiywr2rLzc1VSUnJHd8fAEDPwUnauJE7Eo7ef/99v/cOh0MFBQUqKCjo8jN9+vTR2rVrtXbt2i5r4uPjVVpaGqBRAgAA8Gw1AAAAP0E5rAYAuDv0pnsaAYHCzBEAAICBcAQAAGAgHAEAABgIRwAAAAbCEQAAgIFwBAAAYCAcAQAAGLjPERAk3F8GAO5OzBwBAAAYCEcAAAAGwhEAAICBcAQAAGAgHAEAABi4Wg0AQghXSQI3RjgCAIS89qHxs5VTgjQS3A0IRwBwA/zhBEIL5xwBAAAYCEcAAAAGwhEAAICBcAQAAGAgHAEAABgIRwAAAAbCEQAAgIFwBAAAYOAmkADQS/GoEOD2MHMEAABgYOYIuAP4F3zv0tl/Tx4pAvQezBwBAAAYCEcAAAAGwhEAAICBcAQAAGDghGwAANrhpPvQxswRAACAgXAEAABgIBwBAAAYCEcAAAAGwhEAAICBcAQAAGDgUn4A6CV4hh8QGMwcAQAAGAhHAAAABg6rAQBwE7hrduggHAFAAPCHE+g9OKwGAABgYOYICDCuGAKAno2ZIwAAAAPhCAAAwEA4AgAAMHDOEQD0QJzbBnQfZo4AAAAMhCMAAAADh9UAoJtcO/QV3fKVaoI8FgA3j5kjAAAAA+EIAADAEPBwVFRUpBEjRig2NlZJSUn60Y9+pBMnTvjVWJalgoICud1uRUdHa9y4cTp27JhfTXNzs+bPn6/ExETFxMQoNzdXZ8+e9avxer3yeDxyOp1yOp3yeDy6ePFioHcJAIJq4NJdHV4Auk/AzzmqrKzU3LlzNWLECF25ckXLly9Xdna2jh8/rpiYGEnSqlWrVFxcrM2bN+u73/2ufvnLX2rixIk6ceKEYmNjJUn5+fl65513VFZWpoSEBC1atEg5OTmqqqpSWFiYJCkvL09nz55VeXm5JGnWrFnyeDx65513Ar1bABAQQ14oV1NkH/t9Zw+nJfwAwRXwcHQtqFzzxhtvKCkpSVVVVfr+978vy7K0Zs0aLV++XFOnTpUkbdmyRcnJydq+fbtmz54tn8+nTZs2aevWrZowYYIkqbS0VCkpKdqzZ48mTZqkmpoalZeX68CBA8rMzJQkbdy4UVlZWTpx4oQGDx4c6F0DAAAhoNvPOfL5fJKk+Ph4SVJtba3q6uqUnZ1t10RFRWns2LHat2+fJKmqqkqtra1+NW63W+np6XbN/v375XQ67WAkSSNHjpTT6bRr2mtublZDQ4PfCwAAwNStl/JblqWFCxdqzJgxSk9PlyTV1dVJkpKTk/1qk5OTderUKbsmMjJS/fr161Bz7fN1dXVKSkrq8D2TkpLsmvaKioq0YsWKb7ZTABBAHELr2dr/9+vsMCl6nm6dOZo3b54+/vhj/e53v+uwzuFw+L23LKvDsvba13RWf73tLFu2TD6fz36dOXPmZnYDAACEkG4LR/Pnz9fbb7+t9957T/3797eXu1wuSeowu1NfX2/PJrlcLrW0tMjr9V635vz58x2+74ULFzrMSl0TFRWluLg4vxcAAIAp4OHIsizNmzdPO3bs0B//+Eelpqb6rU9NTZXL5VJFRYW9rKWlRZWVlRo1apQkKSMjQxEREX41586d09GjR+2arKws+Xw+HTp0yK45ePCgfD6fXQMAAHCrAn7O0dy5c7V9+3b927/9m2JjY+0ZIqfTqejoaDkcDuXn56uwsFBpaWlKS0tTYWGh+vbtq7y8PLt2xowZWrRokRISEhQfH6/Fixdr6NCh9tVrQ4YM0eTJkzVz5kytX79e0teX8ufk5HClGgAAuG0BD0fr1q2TJI0bN85v+RtvvKGf/vSnkqQlS5aoqalJc+bMkdfrVWZmpnbv3m3f40iSVq9erfDwcE2bNk1NTU0aP368Nm/ebN/jSJK2bdumBQsW2Fe15ebmqqSkJNC7BAAAQkjAw5FlWTescTgcKigoUEFBQZc1ffr00dq1a7V27doua+Lj41VaWno7wwQAAOgUz1YDAAAwdOt9joBQwH1qAKB3YeYIAADAwMwRAAAB0tlMMnfN7nmYOQIAADAQjgAAAAyEIwAAAAPhCAAAwEA4AgAAMHC1GnALuKcRAPR+zBwBAAAYCEcAAAAGwhEAAICBcAQAAGDghGwAALoRjxTpeZg5AgAAMBCOAAAADIQjAAAAA+EIAADAQDgCAAAwEI4AAAAMhCMAAAAD9zkCusBDZgEgNBGOAAC4w9r/44ubQt5dOKwGAABgIBwBAAAYCEcAAAAGwhEAAICBcAQAAGAgHAEAABgIRwAAAAbucwQAQJB1dtNZ7n0UPIQj4H9xR2wAgMRhNQAAAD+EIwAAAAPhCAAAwEA4AgAAMBCOAAAADIQjAAAAA5fyAwBwF2p/exHue3TnEI4QkrinEQCgKxxWAwAAMBCOAAAADIQjAAAAA+ccodfj/CIAvQEPp71zmDkCAAAwEI4AAAAMHFYDAKCH4lBb92DmCAAAwMDMEXodTsAGEMq4s/Y3x8wRAACAgZkj9GjMEgEAAo2ZIwAAAAMzRwAA9GJc0XbrCEfoMTiEBgCBQWC6vh5/WO21115Tamqq+vTpo4yMDH3wwQfBHhIAAOjBevTM0Ztvvqn8/Hy99tprGj16tNavX69HHnlEx48f14ABA4I9PHxDzBQBwJ3DLQD+T48OR8XFxZoxY4b+8R//UZK0Zs0avfvuu1q3bp2KioqCPDpcD8EHAO5uoXzorceGo5aWFlVVVWnp0qV+y7Ozs7Vv375OP9Pc3Kzm5mb7vc/nkyQ1NDR030Ch9F+8G+wh4A642vKVrv2fdLX5S7VZbUEdz92E3nSOvvQ8A579fzesObpiUreP49rfbcuyumX7PTYc/fd//7euXr2q5ORkv+XJycmqq6vr9DNFRUVasWJFh+UpKSndMkYg1DivffHaU8Ecxl2J3nSOvvQ+zjV37nv99a9/ldPpvHHhLeqx4egah8Ph996yrA7Lrlm2bJkWLlxov7948aLuu+8+nT59ulua25M1NDQoJSVFZ86cUVxcXLCHc1ehN12jN12jN52jL12jN13z+XwaMGCA4uPju2X7PTYcJSYmKiwsrMMsUX19fYfZpGuioqIUFRXVYbnT6eQHrwtxcXH0pgv0pmv0pmv0pnP0pWv0pmv33NM9F9332Ev5IyMjlZGRoYqKCr/lFRUVGjVqVJBGBQAAeroeO3MkSQsXLpTH49Hw4cOVlZWlDRs26PTp03r66aeDPTQAANBD9ehw9JOf/ER//etf9eKLL+rcuXNKT0/Xf/zHf+i+++67qc9HRUXpF7/4RaeH2kIdvekavekavekavekcfekavelad/fGYXXXdXAAAAA9UI895wgAAKA7EI4AAAAMhCMAAAAD4QgAAMDQ68NRUVGRRowYodjYWCUlJelHP/qRTpw44VdjWZYKCgrkdrsVHR2tcePG6dixY0EacfAUFRXJ4XAoPz/fXhbKvfn888/15JNPKiEhQX379tXDDz+sqqoqe32o9ubKlSv6+c9/rtTUVEVHR2vQoEF68cUX1db2f8/FCpXe/OlPf9Kjjz4qt9sth8Oht956y2/9zfShublZ8+fPV2JiomJiYpSbm6uzZ8/ewb3oHtfrTWtrq55//nkNHTpUMTExcrvdeuqpp/TFF1/4baM39uZGPzOm2bNny+FwaM2aNX7Le2NfpJvrTU1NjXJzc+V0OhUbG6uRI0fq9OnT9vpA9abXh6PKykrNnTtXBw4cUEVFha5cuaLs7GxdvnzZrlm1apWKi4tVUlKiw4cPy+VyaeLEibp06VIQR35nHT58WBs2bND3vvc9v+Wh2huv16vRo0crIiJCv//973X8+HG9+uqr+va3v23XhGpvXn75Zb3++usqKSlRTU2NVq1apVdeeUVr1661a0KlN5cvX9ZDDz2kkpKSTtffTB/y8/O1c+dOlZWVae/evWpsbFROTo6uXr16p3ajW1yvN19++aWOHDmiF154QUeOHNGOHTt08uRJ5ebm+tX1xt7c6GfmmrfeeksHDx6U2+3usK439kW6cW/+67/+S2PGjNH999+v999/Xx999JFeeOEF9enTx64JWG+sEFNfX29JsiorKy3Lsqy2tjbL5XJZK1eutGu++uory+l0Wq+//nqwhnlHXbp0yUpLS7MqKiqssWPHWs8884xlWaHdm+eff94aM2ZMl+tDuTdTpkyxfvazn/ktmzp1qvXkk09alhW6vZFk7dy5035/M324ePGiFRERYZWVldk1n3/+uXXPPfdY5eXld2zs3a19bzpz6NAhS5J16tQpy7JCozdd9eXs2bPWd77zHevo0aPWfffdZ61evdpeFwp9sazOe/OTn/zE/j3TmUD2ptfPHLXn8/kkyX5YXW1trerq6pSdnW3XREVFaezYsdq3b19QxninzZ07V1OmTNGECRP8lodyb95++20NHz5cP/7xj5WUlKRhw4Zp48aN9vpQ7s2YMWP0hz/8QSdPnpQkffTRR9q7d69+8IMfSArt3phupg9VVVVqbW31q3G73UpPTw+pXklf/252OBz27Gyo9qatrU0ej0fPPfecHnzwwQ7rQ7kvu3bt0ne/+11NmjRJSUlJyszM9Dv0FsjehFQ4sixLCxcu1JgxY5Seni5J9oNr2z+sNjk5ucNDbXujsrIyHTlyREVFRR3WhXJv/vKXv2jdunVKS0vTu+++q6effloLFizQb3/7W0mh3Zvnn39ejz/+uO6//35FRERo2LBhys/P1+OPPy4ptHtjupk+1NXVKTIyUv369euyJhR89dVXWrp0qfLy8uwHrIZqb15++WWFh4drwYIFna4P1b7U19ersbFRK1eu1OTJk7V79279/d//vaZOnarKykpJge1Nj358yK2aN2+ePv74Y+3du7fDOofD4ffesqwOy3qbM2fO6JlnntHu3bv9jtm2F4q9aWtr0/Dhw1VYWChJGjZsmI4dO6Z169bpqaeesutCsTdvvvmmSktLtX37dj344IOqrq5Wfn6+3G63pk+fbteFYm86czt9CKVetba26rHHHlNbW5tee+21G9b35t5UVVXp17/+tY4cOXLL+9ib+yLJvuDjhz/8oZ599llJ0sMPP6x9+/bp9ddf19ixY7v87O30JmRmjubPn6+3335b7733nvr3728vd7lcktQhVdbX13f4F19vU1VVpfr6emVkZCg8PFzh4eGqrKzUb37zG4WHh9v7H4q9uffee/XAAw/4LRsyZIh9VUQo/9w899xzWrp0qR577DENHTpUHo9Hzz77rD37GMq9Md1MH1wul1paWuT1erus6c1aW1s1bdo01dbWqqKiwp41kkKzNx988IHq6+s1YMAA+3fyqVOntGjRIg0cOFBSaPZFkhITExUeHn7D38uB6k2vD0eWZWnevHnasWOH/vjHPyo1NdVvfWpqqlwulyoqKuxlLS0tqqys1KhRo+70cO+o8ePH65NPPlF1dbX9Gj58uJ544glVV1dr0KBBIdub0aNHd7jlw8mTJ+2HGofyz82XX36pe+7x/9URFhZm/8sulHtjupk+ZGRkKCIiwq/m3LlzOnr0aK/v1bVg9Omnn2rPnj1KSEjwWx+KvfF4PPr444/9fie73W4999xzevfddyWFZl8kKTIyUiNGjLju7+WA9uaWTt/ugf7pn/7Jcjqd1vvvv2+dO3fOfn355Zd2zcqVKy2n02nt2LHD+uSTT6zHH3/cuvfee62GhoYgjjw4zKvVLCt0e3Po0CErPDzceumll6xPP/3U2rZtm9W3b1+rtLTUrgnV3kyfPt36zne+Y/37v/+7VVtba+3YscNKTEy0lixZYteESm8uXbpkffjhh9aHH35oSbKKi4utDz/80L7i6mb68PTTT1v9+/e39uzZYx05csT6u7/7O+uhhx6yrly5EqzdCojr9aa1tdXKzc21+vfvb1VXV/v9bm5ubra30Rt7c6OfmfbaX61mWb2zL5Z1497s2LHDioiIsDZs2GB9+umn1tq1a62wsDDrgw8+sLcRqN70+nAkqdPXG2+8Yde0tbVZv/jFLyyXy2VFRUVZ3//+961PPvkkeIMOovbhKJR7884771jp6elWVFSUdf/991sbNmzwWx+qvWloaLCeeeYZa8CAAVafPn2sQYMGWcuXL/f7oxYqvXnvvfc6/f0yffp0y7Jurg9NTU3WvHnzrPj4eCs6OtrKycmxTp8+HYS9Cazr9aa2trbL383vvfeevY3e2Jsb/cy011k46o19sayb682mTZusv/mbv7H69OljPfTQQ9Zbb73lt41A9cZhWZZ1a3NNAAAAvVevP+cIAADgVhCOAAAADIQjAAAAA+EIAADAQDgCAAAwEI4AAAAMhCMAAAAD4QgAAMBAOAIAADAQjgAAAAyEIwAAAAPhCAAAwPD/AZHo/rNTrEgMAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Merge 3 Distributions\n",
    "si = 10\n",
    "N = 100000\n",
    "w1 = np.random.normal(80, si, N)\n",
    "w2 = np.random.normal(100, si, N)\n",
    "w3 = np.random.normal(120, si, N)\n",
    "w =  np.concatenate((w1, w1, w3))\n",
    "plt.hist(w, 100)\n",
    "plt.axvline(w1.mean(), color='r')\n",
    "plt.axvline(w2.mean(), color='r')\n",
    "plt.axvline(w3.mean(), color='r')\n",
    "plt.xlim([20,160])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "201886f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Exercise 5\n",
    "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": 98,
   "id": "b5a40297",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Exercise 5\n",
    "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": 99,
   "id": "ae12951f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Exercise 5\n",
    "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": 100,
   "id": "7bfec6aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#fff\n"
     ]
    }
   ],
   "source": [
    "# PYTHON LIST\n",
    "#EXERCISE_1\n",
    "x = [1, 'Hello', True, 1.093E-3, '#fff']\n",
    "print(x[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "5a01669d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n",
      "1\n",
      "4\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "#EXERCISE_2\n",
    "x = [9, 3, 4, 1, 4, 5, 6, 3, 7, 8]\n",
    "for i in x[2:6]:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "072638d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "  2\n",
      "    3\n",
      "  4\n",
      "    5\n",
      "      6\n",
      "  7\n",
      "8\n"
     ]
    }
   ],
   "source": [
    "#EXERCISE_3\n",
    "x = [1, [2, [3], 4,[5]], [[[6]]], [7], 8]\n",
    "for i in x:\n",
    "    if isinstance(i, list):\n",
    "        for j in i:\n",
    "            if isinstance(j, list):\n",
    "                for k in j:\n",
    "                    if isinstance(k, list):\n",
    "                        for y in k:\n",
    "                            print('     ', y)\n",
    "                    else:\n",
    "                        print('   ', k)\n",
    "            else:\n",
    "                print(' ', j)\n",
    "    else:\n",
    "        print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "4f4f1c7f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 3, 4, 5]\n",
      "['a', 2, 'b', 'c']\n",
      "[1, 3, 4, 5, 'a', 2, 'b', 'c']\n"
     ]
    }
   ],
   "source": [
    "#EXERCISE_4\n",
    "x = [1, 2, 3, 4, 5]\n",
    "y = ['a', 'b', 'c']\n",
    "z = x.pop(1)\n",
    "y.insert(1, z)\n",
    "z = x + y\n",
    "\n",
    "print(x)\n",
    "print(y)\n",
    "print(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "de7e857f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['a', 2000, 'i', 'o', 'u']\n",
      "['a', 'e', 'i', 'o', 'u']\n"
     ]
    }
   ],
   "source": [
    "#EXERCISE_5\n",
    "a = 'aeiou'\n",
    "b = tuple(a)\n",
    "c = list(b)\n",
    "d = c\n",
    "e = list(d)\n",
    "\n",
    "c[1] = 2000\n",
    "print(d)\n",
    "print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "50652cba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 2, 3, 4, 5]\n",
      "[2, 4, 3, 1, 0, 5]\n",
      "[5, 4, 3, 2, 1, 0]\n",
      "[5, 4, 3, 2, 1, 0]\n"
     ]
    }
   ],
   "source": [
    "#EXERCISE_6\n",
    "#To get [0, 1, 2, 3, 4, 5]\n",
    "x = sorted ([5, 0, 1, 3, 4, 2])\n",
    "print(x)\n",
    "\n",
    "#To get [2, 4, 3, 1, 0, 5]\n",
    "y = [5, 0, 1, 3, 4, 2][::-1]\n",
    "print(y)\n",
    "\n",
    "#To get [5, 4, 3, 2, 1, 0]\n",
    "z = sorted([5, 0, 1, 3, 4, 2], reverse=True)\n",
    "print(z)\n",
    "\n",
    "#To get [5, 4, 3, 2, 1, 0] in two lines\n",
    "zz = [5, 0, 1, 3, 4, 2]\n",
    "yz = list(reversed(sorted(zz)))\n",
    "print(yz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "75684435",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count of 1: 4\n",
      "\n",
      "1st index of 1: 1\n",
      "\n",
      "2nd index of 1: 4\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#EXERCISE_7\n",
    "x = [0, 1, 2, 3, 1, 4, 5, 6, 1, 9, 9, 1]\n",
    "\n",
    "print('count of 1:', x.count(1))\n",
    "print()\n",
    "\n",
    "c0 = x.index(1)\n",
    "print('1st index of 1:', c0)\n",
    "print()\n",
    "\n",
    "#wrongsyntax=c1 = x[c0 + 1:].index(1)\n",
    "#correction=\n",
    "c1 = x.index(1,c0 + 1)\n",
    "print('2nd index of 1:', c1)\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "44eb502a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 2, 4]\n",
      "[1, 3, 5]\n",
      "[1, 3, 5, 3, 5, 7, 5, 7, 9]\n"
     ]
    }
   ],
   "source": [
    "#EXERCISE_8\n",
    "x = [i for i in range (0,5,2)]\n",
    "y = [i for i in range (1,6,2)]\n",
    "z = [i+j for i in x for j in y]\n",
    "\n",
    "print(x)\n",
    "print(y)\n",
    "print(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "a2844180",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original\n",
      "[1, 5, 2, 4, 3] \n",
      "\n",
      "after modified descending bubble sort\n",
      "[5, 4, 3, 2, 1]\n"
     ]
    }
   ],
   "source": [
    "#EXERCISE_9\n",
    "x = [1, 5, 2, 4, 3]\n",
    "\n",
    "print('original'); print(x, '\\n')\n",
    "\n",
    "N = len(x)\n",
    "\n",
    "for i in range(N):\n",
    "    for j in range(i+1, N):\n",
    "        if x[i] < x[j]:\n",
    "            x[i], x[j] = x[j], x[i]\n",
    "\n",
    "print('after modified descending bubble sort'); print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "f55ed743",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['a', 1, 'b', 2, 'c', 3] \n",
      "\n",
      "['a', 'b', 'c'] \n",
      "\n",
      "[1, 2, 3]\n"
     ]
    }
   ],
   "source": [
    "#EXERCISE_10\n",
    "def sep_odd_even_index(z):\n",
    "    N = len(z)\n",
    "    r = range(N)\n",
    "    x = [z[i] for i in r if i % 2 == 0]\n",
    "    y = [z[i] for i in r if i % 2 == 1]\n",
    "    return [x, y]\n",
    "\n",
    "mix = ['a', 1, 'b', 2, 'c', 3]\n",
    "x, y = sep_odd_even_index(mix)\n",
    "print(mix, '\\n')\n",
    "print(x, '\\n')\n",
    "print(y)\n",
    "\n",
    "#The purpose of the sep_odd_even_index function is to separate the elements of a list into two sublists based on their indices (even and odd). Heres a breakdown of how it works:\n",
    "#      1. Input:\n",
    "#          The function takes a single argument z, which is the list to be separated.\n",
    "#      2. Steps:\n",
    "#          Calculate list length: The function first calculates the length of the input list z and stores it in the variable N.\n",
    "#            Generate index range: It creates a range of numbers from 0 to N-1 using range(N). This range represents the indices of the elements in the list.\n",
    "#              Separate elements based on even/odd indices:\n",
    "#          The function uses list comprehension to create two sublists:\n",
    "#              x: This sublist contains elements from the original list z at even indices (0, 2, 4, ...). The condition i % 2 == 0 in the list comprehension filters the indices to select only even numbers.\n",
    "#              y: This sublist contains elements from the original list z at odd indices (1, 3, 5, ...). The condition i % 2 == 1 selects only odd indices.\n",
    "#          Return: The function returns a list containing the two sublists x and y.\n",
    "#            the output will be :\n",
    "#            ['a', 1, 'b', 2, 'c', 3] \n",
    "#            ['a', 'b', 'c'] \n",
    "#            [1, 2, 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "a07eb129",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10] [0, 13, 24, 33, 40, 45, 48, 49, 48, 45, 40]\n"
     ]
    }
   ],
   "source": [
    "#PlOT WITH MATPLOTLIB\n",
    "#create dataset 1\n",
    "x1 = [*range(0,10+1)]\n",
    "y1 = [-i*(i-14) for i in x1]\n",
    "print(x1, y1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "fcd2b3c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 2, 3, 4, 5] [0, 3, 6, 9, 12, 15]\n"
     ]
    }
   ],
   "source": [
    "#create dataset 2\n",
    "x2 = [*range(0,5+1)]\n",
    "y2 = [3*i for i in x2]\n",
    "print(x2, y2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "7565298d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5, 6, 7, 8, 9, 10] [15, 16, 17, 18, 19, 20]\n"
     ]
    }
   ],
   "source": [
    "#create dataset 3\n",
    "x3 = [*range(5,10+1)]\n",
    "y3 = [15+(i-5) for i in x3]\n",
    "print(x3, y3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "281a1276",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WOpnw+gqe/XY7hgHjBqbw0d1DdG8WkTMQHOjP337Tk39c1YvgQD8WbS80ZxNzDlkdrdmofIjIKa3IPnJ9ekchIYH+PH1NL2Zc2UO3EBc5S7/pl8Rnk4bRsW0Y+Y5Krpm9jFcWZuFht1xrEiofInJSbrfBSwt2Mu6VZRSUOOkUG87ce4ZyRZ8kq6OJeI3O8RHMvWcYl/ZKxOU2+OuRHWTF5dVWR2tSKh8icoLD5VXc8c4q/vbVT7jcBpf3TuSzSUNJj4uwOpqI1wmzB/Dstb35y+UZBPn7MX/zfi6ZtYgNu4utjtZkVD5EpI51eYe5+LnFfLOlgKAAP6Zf0YOnr+lNmD3A6mgiXstms3H9oPZ8dPcQkqNDyDtUwW9eXMI7y3K98jKMyoeIAGAYBm8vzeGql5ay53AFKdGhfHz3EMZnpmCz6bRSkebQIymKL+45l1Hd4qhyufmfTzdy33trKXXWWB2tUal8iAilzhom/2sNf/psE1UuN7/qHscX9w4jo12U1dFEfE5UaCCzb+jHIxd1xd/Pxtx1e7l01mK25pdYHa3RqHyI+Lif8h1cOnMxX6zfR4CfjT9e3JWXru9HZHCg1dFEfJbNZuP24Wm8f8cg4iODyTpQxmXPL+aj1butjtYoVD5EfNiHq/K4/PkfyCosIyEqmPfvHMxt56bpMouIh+jfIZov7x3GuekxVFa7+X8fruPBf6+nstpldbSzovIh4oMqqlz8/sN1/P7f66msdjP8nLZ8ee+59Gvf2upoIvIzbcLtvHnzQO6/8BxsNnj/yC8N2YVlVkc7YyofIj4m60ApV7zwAx+u3o2fDf7fqHN486YBRIcFWR1NROrh72fjvgvTeffWTGLCg/gpv4SxMxfznw37rI52RlQ+RHzIF+v3MnbmYn7KLyEm3M67t2Yy+YJ0/Px0mUWkJRjaKYYv7z2XgR2iKXXWMPGfPzJt7iaqatxWR2sQlQ8RH+CscfHoZxu5Z84ayqpcDEyN5j/3DmNIpxiro4lIA8VFBjPn9kzuGtERgDeX5HDVy0vZXVRucbLTp/Ih4uXyDpVz9UtLeWtpLgATR3Zkzm2ZxEYGW5xMRM5UgL8fD43pwqs39icqJLD2cMDvftpvdbTTovIh4sW+3bKfS2YuZt3uYqJCAnn9pv488OsuBPjrn76IN7iwWxxfTB5Gr6QoiiuqueXNVTz59U/UuDz7Mox+Aol4oRqXmxlfbeHWt1ZRXFFNr+RWfHnvMM7vEmd1NBFpZMnRoXxw12AmDG4PwAvf7+S6V5dT4Ki0OFn9VD5EvMx+RyXjX1nOywuyALhpSAc+vHMwSa1DLU4mIk3FHuDPY5dlMHNcH8KC/FmefYiLnlvEkh2FVkc7KZUPES+yeHshFz27iBU5hwi3B/DCdX2Zdml3ggL0T13EF4ztlcjnk4fRJT6CwtIqrn9tOTO/3Y7b7Vk3pzurn0gzZszAZrMxZcqU2jHDMJg2bRqJiYmEhIQwcuRINm3adLY5ReQUXG6DZ7/Zzg2vL+dgWRVd4iP4fPIwLuqRYHU0EWlmaW3D+WTiUK7ql4TbgH/M38ZNb67kUFmV1dFqnXH5WLlyJbNnz6Znz551xp988kmeeuopZs2axcqVK4mPj2fUqFGUlHjPDXFEPMnBUic3vbGCp7/ZhmHAtQOS+XTSUFJjwqyOJiIWCQny5+9X9eLJ3/YkONCPhdsOcPFzi1ide8jqaMAZlo/S0lKuu+46XnnlFVq3PnYcs2EYPPPMMzzyyCNceeWVZGRk8NZbb1FeXs6cOXMaLbSImFblHOLi5xazaHshwYF+/O9Vvfjbb3oSHOhvdTQR8QBX9zd/GUmLCWNfcSXXvLyMVxdlYRjWXoY5o/IxadIkLr74Yi688MI649nZ2eTn5zN69OjaMbvdzogRI1iyZMlJP5bT6cThcNR5EZFf9v7KXVwzexn5jkrS2obx2aRh/LZfktWxRMTDdImPZO7kYVzSM4Eat8FfvtzCXe+uprii2rJMDS4f7733Hj/++CMzZsw44W35+fkAxMXV3c4XFxdX+7afmzFjBlFRUbUvycnJDY0k4nM+W7uHhz7egMttMLZXInPvGUbn+AirY4mIhwq3BzBzXB8ev6w7gf42VucexmnhnXEDGvLOeXl53HfffcybN4/g4PpPR/z57bgNw6j3Ft0PP/wwU6dOrX3d4XCogIicwjeb9zP1g3UYBtw4uD2PXdq93n9fIiJH2Ww2bhzcgV5JrXDWuC095bhB5WP16tUUFBTQr1+/2jGXy8XChQuZNWsWW7duBcwZkISEY6vsCwoKTpgNOcput2O3288ku4jPWbKzkIlzfsTlNriiTzumjVXxEJGG6ZXcyuoIDbvscsEFF7BhwwbWrl1b+9K/f3+uu+461q5dS1paGvHx8cyfP7/2MVVVVSxYsIAhQ4Y0engRX7I27zC3v7WKqho3o7rF8fff9tTdaEWkRWrQzEdERAQZGRl1xsLCwmjTpk3t+JQpU5g+fTrp6emkp6czffp0QkNDGT9+fOOlFvExW/NLmPD6CsqqXAzt1IaZ4/ro/iwi0mI1qHycjgceeICKigomTpxIUVERmZmZzJs3j4gILYYTORO5B8u4/rXlFFdU0zu5FbNv6K+ttCLSotkMqzf7/ozD4SAqKori4mIiIyOtjiNiqX3FFVz10lJ2F1XQJT6C9+4YRKvQIKtjiYicoCHP35q3FfFQB0udXP/qcnYXVdChTShv3zpQxUNEvILKh4gHclRWM+GNFew8UEZCVDDv3pZJbIR12+JERBqTyoeIh6mocnHbm6vYuMdBm7Ag3rk1k6TWoVbHEhFpNCofIh6kqsbNXe+uZkXOISKCA3jrloF0ig23OpaISKNS+RDxEC63wf3vr2XBtgMEB/rxxk0DyGgXZXUsEZFGp/Ih4gEMw+APH2/gyw37CPS38fIN/enfIdrqWCIiTULlQ8RihmHeZfL9VXn42eC5a/sw4py2VscSEWkyKh8iFnvu2x28tjgbgCd+05MxPRJ+4REiIi2byoeIhV5fnM3T32wD4E+XdOOq/rqjs4h4P5UPEYt8sCqPx7/YDMD9F57DLcNSLU4kItI8VD5ELPDVhn089NF6AG4blsq9F3SyOJGISPNR+RBpZgu2HeDe99bgNuCa/sk8cnFXbDab1bFERJqNyodIM1qZc4g731lFtcvg4h4JTL+yh4qHiPgclQ+RZrJxTzG3vLGSymo3Izu35elreuPvp+IhIr5H5UOkGewoKGXC6ysocdYwsEM0L17Xj6AA/fMTEd+kn34iTWx3UTk3vLacg2VVZLSL5NWb+hMS5G91LBERy6h8iDShgpJKrn91OfuKK+kUG87bt2QSGRxodSwREUupfIg0keLyam58bQU5B8tJah3Cu7dmEh0WZHUsERHLqXyINIEyZw03vbmCn/JLaBth55+3ZRIfFWx1LBERj6DyIdLIKqtd3PHOKtbsOkyr0EDevTWT9m3CrI4lIuIxVD5EGlG1y83kf63hhx0HCQvy582bB9I5PsLqWCIiHkXlQ6SRuN0GD/x7PfM37ycowI9XJwygd3Irq2OJiHgclQ+RRmAYBo/O3cQna/YQ4Gfjxev6MrhjG6tjiYh4JJUPkUbwv/O28s6yXGw2+MfVvbiga5zVkUREPJbKh8hZemnBTp7/704A/nJ5Bpf1bmdxIhERz6byIXIW/rk8l7999RMAD43pwnWZ7S1OJCLi+VQ+RM7QZ2v38MdPNwIw6byO3DWio8WJRERaBpUPkTPwzeb9TP1gHYYBNw5uz+9Gd7Y6kohIi6HyIdJAS3YWMnHOj7jcBlf0ace0sd2x2WxWxxIRaTFUPkQaYG3eYW5/axVVNW5GdYvj77/tiZ+fioeISEOofIicpq35JUx4fQVlVS6GdmrDzHF9CPDXPyERkYbST06R05B7sIzrX1tOcUU1vZNbMfuG/gQH+lsdS0SkRVL5EPkF+4oruO7V5RwocdIlPoI3bx5AmD3A6lgiIi2WyofIKRwsdXL9q8vZXVRBhzahvH3rQFqFBlkdS0SkRVP5EKmHo7KaCW+sYOeBMhKignn3tkxiI4KtjiUi0uKpfIicREWVi9veXMXGPQ7ahAXxzq2ZJLUOtTqWiIhXUPkQ+ZmqGjd3vbuaFTmHiAgO4K1bBtIpNtzqWCIiXkPlQ+Q4LrfB/e+vZcG2AwQH+vHGTQPIaBdldSwREa+i8iFyhGEY/OHjDXy5YR+B/jZevqE//TtEWx1LRMTrqHyIYBaPv365hfdX5eFng+eu7cOIc9paHUtExCupfIgALy3I4tXF2QA88ZuejOmRYHEiERHvpfIhPm951kH+/n8/AfCnS7pxVf9kixOJiHg3lQ/xaUVlVUx5fy1uA37TN4lbhqVaHUlExOupfIjPMgyDBz5az77iStJiwnj8su5WRxIR8QkqH+Kz3l6ay/zN+wny9+O5cX10vxYRkWai8iE+afNeB3/9zxYAHhrTRWd5iIg0I5UP8TnlVTXc868fqapxc0GXWG4e2sHqSCIiPkXlQ3zOtLmbyDpQRlyknb9f1QubzWZ1JBERn6LyIT5l7rq9fLBqNzYbPH1Nb6LDgqyOJCLic1Q+xGfsOljOHz7eAMA953ViSMcYixOJiPgmlQ/xCdUuN5PfW0Ops4b+7Vtz3wXpVkcSEfFZKh/iE/533lbW5R0mMjiAZ67tTYC//uqLiFhFP4HF6y3cdoCXF2QB8ORve5LUOtTiRCIivk3lQ7zagRInUz9YB8B1mSn8OkM3jBMRsZrKh3gtt9tg6gdrKSx10jkugv+5pJvVkUREBJUP8WKvLMpi0fZCggP9mDm+D8GB/lZHEhERVD7ES63NO8zf/28rAH+6pDvnxEVYnEhERI5S+RCvU1JZzb3/WkON2+DiHgmMG5hsdSQRETlOg8rHiy++SM+ePYmMjCQyMpLBgwfz1Vdf1b7dMAymTZtGYmIiISEhjBw5kk2bNjV6aJH6GIbBI59sZNehctq1CmH6lT10fLqIiIdpUPlISkrib3/7G6tWrWLVqlWcf/75XHbZZbUF48knn+Spp55i1qxZrFy5kvj4eEaNGkVJSUmThBf5uQ9X72buur34+9l4blwfokICrY4kIiI/YzMMwzibDxAdHc3f//53brnlFhITE5kyZQoPPvggAE6nk7i4OJ544gnuvPPO0/p4DoeDqKgoiouLiYyMPJto4mN2FJQyduZiKqpd/P5XnZl0XierI4mI+IyGPH+f8ZoPl8vFe++9R1lZGYMHDyY7O5v8/HxGjx5d+z52u50RI0awZMmSej+O0+nE4XDUeRFpqMpqF5P/tYaKahdDO7Xh7hEdrY4kIiL1aHD52LBhA+Hh4djtdu666y4++eQTunXrRn5+PgBxcXF13j8uLq72bSczY8YMoqKial+Sk7U4UBrub1/9xJZ9DtqEBfH01b3x89M6DxERT9Xg8tG5c2fWrl3LsmXLuPvuu5kwYQKbN2+uffvPF/cZhnHKBX8PP/wwxcXFtS95eXkNjSQ+bv7m/by5JAeA/726F7GRwdYGEhGRUwpo6AOCgoLo1Mm8lt6/f39WrlzJs88+W7vOIz8/n4SEY0dYFxQUnDAbcjy73Y7dbm9oDBEA9hVX8Pt/m8en3zYslfM6x1qcSEREfslZn/NhGAZOp5PU1FTi4+OZP39+7duqqqpYsGABQ4YMOdtPI3ICl9tgyntrOVxeTY92UTzw6y5WRxIRkdPQoJmPP/zhD4wZM4bk5GRKSkp47733+P777/n666+x2WxMmTKF6dOnk56eTnp6OtOnTyc0NJTx48c3VX7xYbO+28Hy7EOEBfkzc1wfggJ0Zp6ISEvQoPKxf/9+brjhBvbt20dUVBQ9e/bk66+/ZtSoUQA88MADVFRUMHHiRIqKisjMzGTevHlEROhoa2lcK7IP8ey32wD4yxUZdIgJsziRiIicrrM+56Ox6ZwP+SWHy6u46NlF7C2u5Mq+7Xjq6t5WRxIR8XnNcs6HiBUMw+CBf69nb3ElqTFh/PmyDKsjiYhIA6l8SIvy7rJc5m3eT6C/jZnj+hBmb/CGLRERsZjKh7QYm/c6+POXWwB4aExXMtpFWZxIRETOhMqHtAjlVTVM/tePVNW4Ob9LLLcM7WB1JBEROUMqH9IiPDZ3MzsPlBEbYefvv+15ylNzRUTEs6l8iMf7fN1e3l+Vh80Gz1zbmzbhOhFXRKQlU/kQj5Z3qJw/fLwBgEkjOzGkY4zFiURE5GypfIjHqna5mfyvNZQ4a+jXvjVTLky3OpKIiDQClQ/xWP+Yt421eYeJDA7g2Wt7E+Cvv64iIt5AP83FIy3afoCXFuwE4Inf9CSpdajFiUREpLGofIjHOVDi5P731wFwXWYKY3okWJxIREQak8qHeBS32+D/fbiOwlInneMi+J9LulkdSUREGpnKh3iUVxdnsXDbAYID/Zg5vg/Bgf5WRxIRkUam8iEeY13eYZ78eisAf7qkO+fERVicSEREmoLKh3iEkspqJv9rDTVug4t7JDBuYLLVkUREpImofIjlDMPgj59uZNehctq1CmH6lT10fLqIiBdT+RDL/Xv1bj5buxd/PxvPjetDVEig1ZFERKQJqXyIpXYeKOVPn20CYOqoc+jXvrXFiUREpKmpfIhlnDUuJs9ZQ0W1iyEd23DXiI5WRxIRkWag8iGWmfGfn9i8z0F0WBBPX9Mbfz+t8xAR8QUqH2KJbzbv580lOQD846pexEUGWxtIRESajcqHNLv84kp+/2/z+PRbh6VyXpdYixOJiEhzUvmQZuVyG9z33hqKyqvJaBfJA7/ubHUkERFpZiof0qye/+8OlmcfIizIn5nj+mIP0PHpIiK+RuVDms3KnEM88802AP58eQapMWEWJxIRESuofEizOFxexX3/WoPbgCv7tOPKvklWRxIREYuofEiTMwyDBz9az97iSjq0CeXxyzOsjiQiIhZS+ZAm9+7yXfzfpv0E+tuYOa4v4fYAqyOJiIiFVD6kSW3Z5+DPX2wG4MFfd6FHUpTFiURExGoqH9JkyqtqmPyvNVTVuDmvc1tuHZZqdSQREfEAKh/SZB7/fDM7CkqJjbDzv1f1wmbT8ekiIqLyIU3k83V7eW9lHjYbPHNNb9qE262OJCIiHkLlQxpd3qFy/vDxBgAmjezEkE4xFicSERFPovIhjcrtNpj6wVpKnDX0a9+aKRemWx1JREQ8jMqHNKqPftzNypwiQoP8eeaa3gT466+YiIjUpWcGaTTFFdX87aufALj3gnSSo0MtTiQiIp5I5UMazdPzt3GwrIqObcO4Zai21YqIyMmpfEij2LzXwdtLcwB47NIMggL0V0tERE5OzxBy1gzD4NG5G3EbcFGPeIala3eLiIjUT+VDztona/awMqeIkEB//nhxN6vjiIiIh1P5kLPiqKxm+n/MRab3nN+JxFYhFicSERFPp/IhZ+WZ+dspLHWSGhPGbedqkamIiPwylQ85Yz/lO3jryCLTaZd2xx7gb20gERFpEVQ+5IwYhsGfPtuEy23wq+5xjDinrdWRRESkhVD5kDMyd91eVmQfIjjQj/+5RItMRUTk9Kl8SIOVVFbz1y+3AOaN45Ja6yRTERE5fSof0mDPfbudghIn7duEcvvwNKvjiIhIC6PyIQ2yfX8Jb/yQA8C0sd0JDtQiUxERaRiVDzltRxeZ1rgNLuwax3ldYq2OJCIiLZDKh5y2L9bvY2nWQewBfjw6VotMRUTkzKh8yGkpc9bULjKdOLITydFaZCoiImdG5UNOy3PfbSffUUlKdCh3jtAiUxEROXMqH/KLdhSU8tqibAAeHdtNi0xFROSsqHzIKRmGwbS55iLTC7rEckHXOKsjiYhIC6fyIaf01cZ8Fu8oJCjAj0fHdrc6joiIeAGVD6lXeVUNf/liMwB3jehIShstMhURkbOn8iH1mvXdDvYWV5LUOoSJIztaHUdERLxEg8rHjBkzGDBgABEREcTGxnL55ZezdevWOu9jGAbTpk0jMTGRkJAQRo4cyaZNmxo1tDS9rAOlvLIoC4A/XaJFpiIi0ngaVD4WLFjApEmTWLZsGfPnz6empobRo0dTVlZW+z5PPvkkTz31FLNmzWLlypXEx8czatQoSkpKGj28NA3DMHh07iaqXQYjO7dlVDctMhURkcZjMwzDONMHHzhwgNjYWBYsWMDw4cMxDIPExESmTJnCgw8+CIDT6SQuLo4nnniCO++88xc/psPhICoqiuLiYiIjI880mpyFrzfu4653fyTI34959w+nQ0yY1ZFERMTDNeT5O+BsPlFxcTEA0dHRAGRnZ5Ofn8/o0aNr38dutzNixAiWLFly0vLhdDpxOp11wot1Kqpc/PkL8yTTO4anqXiIiLRwZdVl5BTnkOM48lKcQ6Wrkpnnz7Qs0xmXD8MwmDp1KsOGDSMjIwOA/Px8AOLi6k7Tx8XFkZube9KPM2PGDB577LEzjSGN7Pn/7mDP4QratQph0nmdrI4jIiKnweV2sbd0L9mO7BOKxoGKAye8v7/Nn2pXNYH+gRakPYvycc8997B+/XoWL158wttsNlud1w3DOGHsqIcffpipU6fWvu5wOEhOTj7TWHIWsgvLmL3QXGT6P5d0JSRIi0xFRDxJsbOY7OLs2mJx9M9dJbuodlfX+7jo4Gg6RHYgNSqVDpEd6BDVoflCn8QZlY/Jkyczd+5cFi5cSFJSUu14fHw8YM6AJCQk1I4XFBScMBtylN1ux263n0kMaUSGYfDY55uocrk5Nz2GX3WPtzqSiIhPqnZVk1eSV3cW48ifh52H632c3d9OSmSKWS6OKxrto9oTGeRZaygbVD4Mw2Dy5Ml88sknfP/996SmptZ5e2pqKvHx8cyfP58+ffoAUFVVxYIFC3jiiScaL7U0uvmb9/P91gME+tt47NLu9c5UiYjI2TMMg4OVB086i7GndA8uw1XvY+NC4+gQ1eGEmYyEsAT8bC3j+K4GlY9JkyYxZ84cPvvsMyIiImrXeERFRRESEoLNZmPKlClMnz6d9PR00tPTmT59OqGhoYwfP75JvgA5e5XVLh4/cpLpbeemkdY23OJEIiLeoaKmgl2OXScUjBxHDqXVpfU+LjQgtLZgHC0XHSI70D6yPaGBLf+06QaVjxdffBGAkSNH1hl/4403uOmmmwB44IEHqKioYOLEiRQVFZGZmcm8efOIiIholMDS+F74fie7iypIiApm8vlaZCoi0hBuw83+sv0nvUyyr2xfvY/zs/mRGJZ40lmMtiFtvXoG+qzO+WgKOuejeeUeLGPU0wupqnHz/Pi+XNwz4ZcfJCLig0qrSsl15J5QMnIduVS6Kut9XGRQ5IkFI7IDKZEpBPkHNeNX0LSa7ZwPafke/3wzVTVuhnZqw0U9tMhURHxbjbuGvaV7yXHknLAeo7CisN7HBfgFkByRXDtzkRqZSoco8zJJa3trr57FOBMqHz7s2y37+fanAgL8tMhURHxLUWWROYvxs4Kxq2QXNe6aeh/XJrjNSS+TtAtvR4CfnlJPl75TPqqy2sVjn5uLTG8dlkqnWK3JERHvUuWqIq8kj5zinBMO3yp2Ftf7OLu/nfaR7ess9Dz63xFB+lnZGFQ+fNTLC7LYdaicuEg7ky9ItzqOiMgZMQyDworCk14m2VO6B7fhrvex8WHxdYrF0Usl8WHxLWbLakul8uGD8g6V88L3OwB45OJuhNv110BEPFtFTQW5jtwTZjFyHbmUVZfV+7iwwLC6MxhH/kyJSPGKLastlZ51fNDjX2zGWeNmcFobxmp3i4h4CLfhZl/ZPnKLc0+4TJJfll/v4/xsfrQLb1enXBxdjxETEqP1bB5I5cPH/HdrAfM37zcXmV6mRaYi0vxKqkpqi0XtpRJHDrscu3C6nPU+rpW91UnXYSRHJHvVllVfoPLhQ5w1Lh6buwmAm4Z04Jw4LZwSkaZR465hT+meE0tGcQ4HKw/W+7gAvwBSIlJOOovRKrhV830B0qRUPnzIKwuzyDlYTtsIO/ddqEWmInJ2DMOgyFlU58Cto5dLdpfspsaof8tq25C2Jz0+PDE8UVtWfYD+D/uI3UXlzPrvkUWmF3UlIjjQ4kQi0lI4XU52OXaZCz5/NovhqHLU+7hg/2Bzy2pU3R0l7SPbEx6ke0j5MpUPH/GXL7ZQWe1mYGo0l/VOtDqOiHgYwzAoKC+os1X16CzGvrJ9p9yymhCWcMKOktTIVOLC4rRlVU5K5cMHLNx2gK835ePvZ+PPl2VokamIDyuvLq9zT5KjBSPXkUt5TXm9jwsPDD/5ltXIFEICQprxKxBvoPLh5Zw1LqYdWWQ6YXAHOsdrkamIt3O5Xewr23fCbdyzHdkUlBfU+zh/m7+5ZfVnBSM1KpU2wW30i4s0GpUPL/fa4myyCsuICbczZZQWmYp4k2JncZ1ZjKPrMXY5dlHlrqr3ca3trWtvenb8ZZLkiGQC/bUeTJqeyocX23u4gpnfmotM/3BRFyK1yFSkxal2V7O7ZHedA7eO/vehykP1Pi7QL9DcsnqSWYwoe1QzfgUiJ1L58GJ//XILFdUuBnRozRV92lkdR0TqYRgGhyoPnXCZJMdxZltWUyNTSQxPxN/Pvxm/CpHTp/LhpRZvL+TLDfvws8Fjl2qRqYgncLqctfcnOXpfkqNrMUqqSup9XEhAyIl3WT3yZ1hgWDN+BSKNQ+XDC1XVuHl07kYAbhzcgW6JkRYnEvEdhmGwv3z/SWcx9pbuxcA46eNs2Mwtqye5TBIbGqstq+JVVD680Bs/ZLPzQBkx4UHcP+ocq+OIeKXjt6zW+dORQ0VNRb2PiwiMOKFgdIgy77IaHBDcjF+BiHVUPrzMvuIKnv12OwAP/roLUSFaZCpyplxuF3vL9p5YMIpzKKg49ZbV5Ijkk14q0ZZVEZUPr/PXL7dQXuWib0orftM3yeo4Ii3C8VtWj//zl7asRgdHn/Quq0kRSQT6qfiL1Eflw4ss2VnIF+vNRaaPX5aBn59+uxI5qtpdTV5J3gkFI9eRe8otq0F+QaREppx0sae2rIqcGZUPL1HtcvPoZ+ZJptdltiejnX4oiu8xDIODlQdPug5jd8luXIar3sfGhsaSGpl6QsFICEvQllWRRqby4SXe/CGH7QWlRIcF8bvRna2OI9KkKmsqa0/0/Pm21ZLqU29Z/fkt3I/+GRoY2oxfgYhvU/nwAvsdlTzzzTYAHvx1Z6JCda1ZWj634aagvKDO7duP/rmvbN8pt6wmhifWHrZ1tGC0j2xPXGicFnuKeACVDy8w/T9bKKty0Tu5FVf1S7Y6jkiDlFWX1R60dXzB2FWy69RbVoMiTnqZJCUyBbu/vRm/AhFpKJWPFm5Z1kE+W7sXmw0ev6y7FpmKR1uX8y3rfnyVnOgkcpzm2owDFQfqff8AWwBJEUkn7CbpENmB6OBozWKItFAqHy3Y8YtMxw1MoWdSK2sDidRn/yZYMpNP9szjo4gwKNlY581Ht6ymRqXWKRjtItppy6qIF1L5aMHeXprL1v0ltAoN5PdaZCqexjAgeyEseQ52fANA3/AwHOExdEgeSocO59UWjcgg3QJAxJeofLRQBSWVPDPfXGT6wK+60DosyOJEIke4amDzp2bp2LfOHLP5QddLuXTIvVya1M/SeCJiPZWPFupv//mJEmcNPZOiuGaAFpmKB3CWwpp3YOkLULzLHAsIgT7Xw+CJEJ1mbT4R8RgqHy3QypxDfLxmz5FFphn4a5GpWKlkP6x4GVa+BpWHzbHQGMi8E/rfCmFtLI0nIp5H5aOFqXG5+Z9PzcV61/RPpndyK2sDie86sBWWzIT174PryP1PojvCkHug1zgIDLE2n4h4LJWPFubdZbn8lF9CVEggD/y6i9VxxNcYBuQuMUvHtq+OjSdnwpB7ofMY0FHkIvILVD5akAMlTv5xZJHp737VmWgtMpXm4nbBls/NRaR7Vh8ZtEGXi83SkZJpaTwRaVlUPlqQJ77+iZLKGjLaRTJ+YIrVccQXVJXD2n/C0uehKNsc87dD7/Ew+B6I6WRtPhFpkVQ+WojVuUX8e/VuAB67VItMpYmVFcKK2bDiFag4crv5kNYw4HYYeAeEt7U2n4i0aCofLYDLbfCnz8xFplf1S6Jf+9YWJxKvdXAnLJ0Fa+dATaU51qq9OcvR5zoICrM2n4h4BZWPFmDO8lw27XUQGRzAg2O0yFSaQN4Kcz3Hli/g6N1iE/vC0Huh66VaRCoijUrlw8MdLHXy9//bCpiLTGPCdbdOaSRut7lj5YfnIG/ZsfH0X5mlo/1Q0I3bRKQJqHx4uCe/3oqjsoZuCZFcl9ne6jjiDaorYf17sGQWHNxujvkHQc+rYfBkiNXsmog0LZUPD7ajoIQPV+cB8Phl3bXIVM5O+SHzFNIVL0PZkdvY26NgwC2QeRdExFubT0R8hsqHB3v22x24DRjVLY7+HaKtjiMtVVGOeb+VNe9Adbk5FpUMgyZC3xvAHmFpPBHxPSofHmrb/hK+WL8XgCkXplucRlqkPT+ai0g3fwaG2xyL7wFD7oPul4N/oKXxRMR3qXx4qGe/2Y5hwK+7x9M9McrqONJSuN2w4xuzdOQsOjbe8QJzEWnqCC0iFRHLqXx4oJ/yHXy5YR8AU0Zp1kNOQ40TNnxo3nPlwE/mmF8AZPwWhkyG+Axr84mIHEflwwM9M9/cgXBxjwS6xEdanEY8WsVhWP0GLHsJSvPNsaAI6DcBBt0NUUmWxhMRORmVDw+zaW8xX2/Kx2aD+7TWQ+pzOA+WvwSr34SqUnMsIsEsHP1ugmBdqhMRz6Xy4WGe+cac9bikZyLnxGkXgvzMvvXmpZWNH4HhMsdiu5mXVjJ+CwG607GIeD6VDw+yYXcx8zfvx88G912gWQ85wjBg53fmItKs74+Npw43b2ff6UItIhWRFkXlw4M88802AC7tlUin2HCL04jlXNWw8WNzpmP/BnPM5g/drzBnOhJ7WxpPRORMqXx4iHV5h/n2pwL8bHCvZj18W6UDfnwLlr0Ijj3mWGAY9L3RXNPRWsfsi0jLpvLhIZ4+MutxeZ92pLXVrIdPcuyD5S/CqjfBWWyOhcVC5p3Q/xYI1Sm3IuIdVD48wI+7ivh+6wH8/Wzce75mPXxOwRbz0sr6D8BdbY7FnGNeWulxNQQGW5tPRKSRqXx4gKfnm7MeV/ZpR4eYMIvTSLMwDPME0h+egx3zj42nDDFPIk3/Ffj5WZdPRKQJqXxYbFXOIRZtLyTAz8ZkzXp4P1cNbP7UnOnYt9Ycs/lB17HmzpWk/lamExFpFiofFju61uO3/ZJIaRNqcRppMs5SWPMuLHseDu8yxwJCoM91MHgSRKdZm09EpBmpfFhoedZBfthxkEB/G5PO62R1HGkKJfthxWxY+SpUHjbHQtvAwDthwG0Q1sbSeCIiVmjwReWFCxcyduxYEhMTsdlsfPrpp3XebhgG06ZNIzExkZCQEEaOHMmmTZsaK69XOTrrcVX/ZJKjNevhVQ5sg7mT4ZkesOh/zeIRnQYXPwX3b4KRD6p4iIjPanD5KCsro1evXsyaNeukb3/yySd56qmnmDVrFitXriQ+Pp5Ro0ZRUlJy1mG9yZKdhSzLOkSQv59mPbyFYUDuUvjXOHh+APz4NrickDQArn4H7lkFA26FwBCrk4qIWKrBl13GjBnDmDFjTvo2wzB45plneOSRR7jyyisBeOutt4iLi2POnDnceeedZ5fWSxiGUXvn2msGJNOulZ6MWjS3C376wlxEunvlkUEbdL7I3LmSMsjSeCIinqZR13xkZ2eTn5/P6NGja8fsdjsjRoxgyZIlJy0fTqcTp9NZ+7rD4WjMSB5pyc6DrMg5RFCAHxPP62h1HDlTVeWwbg4sfR4OZZlj/nboPQ4G3wMx2r0kInIyjVo+8vPzAYiLi6szHhcXR25u7kkfM2PGDB577LHGjOHRDMPgqSPneowfmEJClGY9WpyyQljxCqx8BcoPmmMhrc0FpAPvgPBYa/OJiHi4JtntYvvZHTYNwzhh7KiHH36YqVOn1r7ucDhITk5uilgeYdH2QlbnFmEP8OPukZr1aFEO7jRnOdb+E2oqzbFWKeYsR5/rIUgHxImInI5GLR/x8fGAOQOSkJBQO15QUHDCbMhRdrsdu93emDE81vGzHtdlticuUsdmtwh5K83b2W/5HDDMscQ+5qFgXS8Ff+1YFxFpiEb9qZmamkp8fDzz58+nT58+AFRVVbFgwQKeeOKJxvxULdL32w6wNu8wwYF+3DVSh0p5NLcbtn1tlo5dS4+Np482S0eHYVDPbJ6IiJxag8tHaWkpO3bsqH09OzubtWvXEh0dTUpKClOmTGH69Omkp6eTnp7O9OnTCQ0NZfz48Y0avKUxDKP2Hi43DGpPbIRmPTxSdSWsfx+WzoJC8/8XfoHQ8xoYcg/EdrU2n4iIF2hw+Vi1ahXnnXde7etH12tMmDCBN998kwceeICKigomTpxIUVERmZmZzJs3j4iIiMZL3QJ991MB63cXExLoz50jtNbD45QfglWvwfLZUFZgjtmjoP/NkHkXRCac+vEiInLabIZhGFaHOJ7D4SAqKori4mIiIyOtjtMoDMNg7KzFbNzj4M4RaTw8Rr89e4yiHFj6Aqx5B6rLzbHIJBg8EfreCHbfLs0iIqerIc/fWinXDOZv3s/GPQ7Cgvy5c7hmPTzC3jXm7ew3fwqG2xyL62EeCtb9CvAPtDSeiIg3U/loYm63wdPfmKeZThjSgeiwIIsT+TDDgB3fwA/PQs6iY+MdzzcXkaaN1CJSEZFmoPLRxOZtzmfLPgfh9gBuP1c7XCxRUwUbPjSPPz+wxRzzC4CM38CQyRDfw9p8IiI+RuWjCbndBk8fuYfLzUM70FqzHs2r4jCsfhOWvwQl+8yxoAjoNwEG3Q1RSVamExHxWSofTeirjfls3V9ChD2A24Zp1qPZFO+GZS/C6reg6sjdlCMSzF0r/W6CkFZWphMR8XkqH03E5TZ45hvznIhbhqUSFaoFjE0uf4N5aWXjR+CuMcfadjUvrfS4CgI08yQi4glUPprIlxv2sb2glMjgAG4Zlmp1HO9lGJD1X3PnStZ/j413OBeG3gedLtQiUhERD6Py0QRcboNnj8x63HZuGlEhmvVodK5q2PSJefx5/gZzzOZnbpMdMtm894qIiHgklY8m8Pm6vew8UEZUSCA3D+1gdRzv4iwx13IsexEcu82xwFDzQLBBE6F1e2vziYjIL1L5aGQ1LjfPfWvucLljeBoRwZr1aBSOfeaulVVvgLPYHAuLhcw7oP+tEBptbT4RETltKh+N7LO1e8kqLKN1aCAThnSwOk7LV7DFXES6/gNwV5tjbdLNSys9r4FA3aBPRKSlUfloRDUuN899d3TWoyPhdn17z4hhQM5icz3H9nnHxlMGmyeRnvNr8POzLp+IiJwVPTs2oo/X7CH3YDnRYUHcOFhrDxrMVQNbPjNnOvauOTJog65jzdKRPMDSeCIi0jhUPhpJtcvNzCOzHneNSCNMsx6nr6oM1rwLS5+Hw7nmWEAw9L4OBk+CNroZn4iIN9EzZCP5aPVu8g5VEBMexPWDNOtxWkoLYPnLsPJVqDxsjoW2gYF3wIDbICzG0ngiItI0VD4aQVWNm5nf7QDgrhEdCQ3St/WUCrebl1bWvQcupznWOhWG3AO9xkNQqLX5RESkSelZshF8uDqPPYcraBth16xHfQwDdi0zF5Fu/c+x8Xb9Yei90OUS8PO3Lp+IiDQblY+z5Kxx8fyRWY+JIzsSHKgn0DrcLvjpS7N07F55bLzzReYi0pRBOv5cRMTHqHycpQ9W5rG3uJK4SDvjBqZYHcdzVFfA2jmwdBYcyjLH/IOg17UweDK0PcfafCIiYhmVj7NQWe3i+f/uBGDSeZ006wFQdhBWvgIrZkP5QXMsuJW5gHTgHRARZ2k8ERGxnsrHWXhvxS7yHZUkRAVzzYBkq+NY6+BOc6vs2jlQU2GOtUqBwfeYW2bt4dbmExERj6HycYYqq1288P2xWQ97gI/OeuxeBT88C1s+BwxzLKG3efx5t8vBX3/FRESkLj0znKF/Lt9FQYmTdq1CuLq/j816uN2w7Wtzu+yuJcfGO40yd650OFeLSEVEpF4qH2egosrFi0dmPe45vxNBAT5yn5HqSlj/vrmItHCbOeYXCD2vNi+vxHWzNp+IiLQIKh9n4N1luRSWOklqHcJv+yVZHafpVRTBytfM00jLCswxeyT0vxky74LIRGvziYhIi6Ly0UDlVTW8tMCc9bj3/HQC/b141qMoF5a9AD++A9Vl5lhkOxg0EfreCMGR1uYTEZEWSeWjgd5emsvBsipSokO5om87q+M0jb1rzPUcmz4Fw2WOxWWYh4JlXAn+gZbGExGRlk3lowFKnTW8fHTW4wIvm/UwDNjxjblzJWfRsfG0kWbp6Hi+FpGKiEijUPlogLeW5FBUXk1qTBiX9/aSdQ41VbDx3+ZMR8Fmc8zmDxm/MbfLJvS0Np+IiHgdlY/TVFJZzSuLzGPC772gEwEtfdajshhWvQHLX4KSfeZYUDj0u8lcRNrKx7YPi4hIs1H5OE1v/pDD4fJq0tqGcWmvFrzWo3g3LHsRVr8FVSXmWHg8DLoL+t0MIa0sjSciIt5P5eM0OI6b9bjvgnT8/Vrg2of8jeallY3/BneNOda2i3lppcdVEGC3Np+IiPgMlY/T8PribByVNXSKDeeSni1orYdhQNb35u3sd353bLzDueYi0k4Xgl8Lv3wkIiItjsrHLygur+a1xdkATLmwhcx6uKph0ydm6cjfYI7Z/KDbZWbpaNfX2nwiIuLTVD5+wWuLsyiprKFzXAQXZSRYHefUnCXw49vmmo7iPHMsMBT63ACD7oboVGvziYiIoPJxSofLq3j9hxzAnPXw89RZD8c+c9fKqjfAWWyOhbWFgXfCgFshNNrafCIiIsdR+TiFVxZlUeqsoUt8BL/qHm91nBMV/GQuIl3/PrirzbE2ncybvPUaB4HB1uYTERE5CZWPehwqq+LNI7Me9486x3NmPQwDcn+AH56D7f93bDx5kHk7+3PGaBGpiIh4NJWPesxemEVZlYvuiZGM7hZndRxw1cCWueZMx94fjwzaoMvFMPQ+SB5oaTwREZHTpfJxEoWlTt5emgPA/Reeg83Ke5pUlcGaf8LSWXA41xwLCIbe42HQJIjpZF02ERGRM6DycRKzF2ZRXuWiZ1IUF3SNtSZEaQGsmA0rX4WKInMsJBoG3g4DbofwttbkEhEROUsqHz9zoMTiWY/C7eYsx9p/gctpjrXuYC4i7X0dBIU2bx4REZFGpvLxMy8t2ElltZveya0Y2bkZZxd2LTMXkW79D2CYY+36mYeCdR0Lfv7Nl0VERKQJqXwcp8BRybvLzHUV949qhlkPt8ssGz88B7tXHBs/Z4x5z5X2Q8DK9SYiIiJNQOXjOC98vxNnjZu+Ka0Ynh7TdJ+ougLWzoGlz8OhneaYfxD0vMYsHW07N93nFhERsZjKxxH5xZXMWbELgKmjOjfNrEfZQXMB6YrZUF5ojgVHQf9bIfNOiPDAg8xEREQamcrHES98v4OqGjcDOrRmaKc2jfvBD2XB0hdgzbtQU2GORaXA4InmfVfs4Y37+URERDyYygew93AF760wb8TWqGs9dq+GJc/Cls/BcJtj8T3NQ8G6XQ7++vaLiIjv0bMf8Px/d1DlcpOZGs2Qjme51sPtNo89XzLTPAb9qE4Xmus5UkdoEamIiPg0ny8fu4vK+WDVsVmPM1bjNG/wtmQWFG41x/wCoMdVZumI694IaUVERFo+ny8fz/93B9UugyEd2zAo7QzWelQUwarXYfnLULrfHAuKgP43QebdENWuUfOKiIi0dD5dPvIOlfPhqt3AGcx6HN5lLiL98W2oLjPHIhJh0N3Qb4K5i0VERERO4NPlY+Z326lxG5ybHsOADtGn96B968xDwTZ9AobLHIvtbl5ayfgNBAQ1XWAREREv4LPlI/dgGR/9uAeAKRf+wqyHYcCOb2HJc5C94Nh46ggYei90vECLSEVERE6Tz5aP577dgcttMOKctvRr3/rk71RTBRs/MneuFGwyx2z+0P0Kc6YjsXez5RUREfEWPlk+sgvL+GTNKdZ6VBbD6jdh2UtQstccCwwz13IMuhtapTRfWBERES/jk+XjuW+34zbg/C6x9E5udewNxXtg+Yuw6k2oKjHHwuPMo8/73wIh9cyQiIiIyGnza6oP/MILL5CamkpwcDD9+vVj0aJFTfWpGmRHQSmfrTXXetx/dK1H/kb4+E54tqd5iaWqBGI6w6WzYMoGOPf/qXiIiIg0kiaZ+Xj//feZMmUKL7zwAkOHDuXll19mzJgxbN68mZQUay9ZHJ31uLBLLD2q1sA7z8HOb4+9Q/uhMOReSB8Nfk3WzURERHyWzTAMo7E/aGZmJn379uXFF1+sHevatSuXX345M2bMOOVjHQ4HUVFRFBcXExkZ2ai5tu8vYcwz/+Ui23KeSPiekIMbzTfY/KDrWBhyHyT1a9TPKSIi4gsa8vzd6DMfVVVVrF69moceeqjO+OjRo1myZMkJ7+90OnE6nbWvOxyOxo505BOVsu7fM/g+6H2SbIVwEAgIgT7Xm3eXjU5rms8rIiIidTR6+SgsLMTlchEXF1dnPC4ujvz8/BPef8aMGTz22GONHeME2btyuKLgBfxtBjXBbQgYdCcMuA3CzuBIdRERETljTbao4ee3pTcM46S3qn/44YcpLi6ufcnLy2uSPElp3djecQJfpvyegP+3CUY+qOIhIiJigUaf+YiJicHf3/+EWY6CgoITZkMA7HY7dru9sWOcINDfjy43PkuXJv9MIiIiciqNPvMRFBREv379mD9/fp3x+fPnM2TIkMb+dCIiItLCNMlW26lTp3LDDTfQv39/Bg8ezOzZs9m1axd33XVXU3w6ERERaUGapHxcc801HDx4kMcff5x9+/aRkZHBf/7zH9q3b98Un05ERERakCY55+NsNOU5HyIiItI0GvL8rSM8RUREpFmpfIiIiEizUvkQERGRZqXyISIiIs1K5UNERESalcqHiIiINCuVDxEREWlWKh8iIiLSrFQ+REREpFk1yfHqZ+PogasOh8PiJCIiInK6jj5vn87B6R5XPkpKSgBITk62OImIiIg0VElJCVFRUad8H4+7t4vb7Wbv3r1ERERgs9ka9WM7HA6Sk5PJy8vTfWOakL7PzUPf5+aj73Xz0Pe5eTTV99kwDEpKSkhMTMTP79SrOjxu5sPPz4+kpKQm/RyRkZH6i90M9H1uHvo+Nx99r5uHvs/Noym+z78043GUFpyKiIhIs1L5EBERkWblU+XDbrfz6KOPYrfbrY7i1fR9bh76Pjcffa+bh77PzcMTvs8et+BUREREvJtPzXyIiIiI9VQ+REREpFmpfIiIiEizUvkQERGRZuUz5eOFF14gNTWV4OBg+vXrx6JFi6yO5HVmzJjBgAEDiIiIIDY2lssvv5ytW7daHcvrzZgxA5vNxpQpU6yO4nX27NnD9ddfT5s2bQgNDaV3796sXr3a6lhepaamhj/+8Y+kpqYSEhJCWloajz/+OG632+poLd7ChQsZO3YsiYmJ2Gw2Pv300zpvNwyDadOmkZiYSEhICCNHjmTTpk3Nks0nysf777/PlClTeOSRR1izZg3nnnsuY8aMYdeuXVZH8yoLFixg0qRJLFu2jPnz51NTU8Po0aMpKyuzOprXWrlyJbNnz6Znz55WR/E6RUVFDB06lMDAQL766is2b97MP/7xD1q1amV1NK/yxBNP8NJLLzFr1iy2bNnCk08+yd///ndmzpxpdbQWr6ysjF69ejFr1qyTvv3JJ5/kqaeeYtasWaxcuZL4+HhGjRpVe4+1JmX4gIEDBxp33XVXnbEuXboYDz30kEWJfENBQYEBGAsWLLA6ilcqKSkx0tPTjfnz5xsjRoww7rvvPqsjeZUHH3zQGDZsmNUxvN7FF19s3HLLLXXGrrzySuP666+3KJF3AoxPPvmk9nW3223Ex8cbf/vb32rHKisrjaioKOOll15q8jxeP/NRVVXF6tWrGT16dJ3x0aNHs2TJEotS+Ybi4mIAoqOjLU7inSZNmsTFF1/MhRdeaHUUrzR37lz69+/PVVddRWxsLH369OGVV16xOpbXGTZsGN9++y3btm0DYN26dSxevJiLLrrI4mTeLTs7m/z8/DrPjXa7nREjRjTLc6PH3ViusRUWFuJyuYiLi6szHhcXR35+vkWpvJ9hGEydOpVhw4aRkZFhdRyv89577/Hjjz+ycuVKq6N4raysLF588UWmTp3KH/7wB1asWMG9996L3W7nxhtvtDqe13jwwQcpLi6mS5cu+Pv743K5+Otf/8q4ceOsjubVjj7/ney5MTc3t8k/v9eXj6NsNlud1w3DOGFMGs8999zD+vXrWbx4sdVRvE5eXh733Xcf8+bNIzg42Oo4XsvtdtO/f3+mT58OQJ8+fdi0aRMvvviiykcjev/993n33XeZM2cO3bt3Z+3atUyZMoXExEQmTJhgdTyvZ9Vzo9eXj5iYGPz9/U+Y5SgoKDih8UnjmDx5MnPnzmXhwoUkJSVZHcfrrF69moKCAvr161c75nK5WLhwIbNmzcLpdOLv729hQu+QkJBAt27d6ox17dqVjz76yKJE3un3v/89Dz30ENdeey0APXr0IDc3lxkzZqh8NKH4+HjAnAFJSEioHW+u50avX/MRFBREv379mD9/fp3x+fPnM2TIEItSeSfDMLjnnnv4+OOP+e6770hNTbU6kle64IIL2LBhA2vXrq196d+/P9dddx1r165V8WgkQ4cOPWGr+LZt22jfvr1FibxTeXk5fn51n4r8/f211baJpaamEh8fX+e5saqqigULFjTLc6PXz3wATJ06lRtuuIH+/fszePBgZs+eza5du7jrrrusjuZVJk2axJw5c/jss8+IiIionW2KiooiJCTE4nTeIyIi4oR1NGFhYbRp00braxrR/fffz5AhQ5g+fTpXX301K1asYPbs2cyePdvqaF5l7Nix/PWvfyUlJYXu3buzZs0annrqKW655Raro7V4paWl7Nixo/b17Oxs1q5dS3R0NCkpKUyZMoXp06eTnp5Oeno606dPJzQ0lPHjxzd9uCbfT+Mhnn/+eaN9+/ZGUFCQ0bdvX23/bALASV/eeOMNq6N5PW21bRqff/65kZGRYdjtdqNLly7G7NmzrY7kdRwOh3HfffcZKSkpRnBwsJGWlmY88sgjhtPptDpai/ff//73pD+TJ0yYYBiGud320UcfNeLj4w273W4MHz7c2LBhQ7NksxmGYTR9xRERERExef2aDxEREfEsKh8iIiLSrFQ+REREpFmpfIiIiEizUvkQERGRZqXyISIiIs1K5UNERESalcqHiIiINCuVDxEREWlWKh8iIiLSrFQ+REREpFmpfIiIiEiz+v9+0x9jy71JzgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(x1, y1, x2, y2, x3, y3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "8298f85f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#AXES LABELS AND GRID\n",
    "plt.plot(x1, y1, x2, y2, x3, y3)\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "398b973a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 840x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#LINE, SCATTER, CONTOUR PLOTS IN MATPLOTLIB\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "Police = [5, 8, 10, 9, 7, 3, 5, 8, 4]\n",
    "Soldier = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
    "Doctor = [4, 1, 0, 1, 4, 9, 16, 25]\n",
    "Writer = [8, 7, 6, 5, 4, 7, 9, 20]\n",
    "\n",
    "plt.figure(figsize=(8.4, 4.8))\n",
    "\n",
    "plt.plot(Police, '-', color='tab:blue', label='Police')\n",
    "plt.plot(Soldier, ':', color='tab:pink', label='Soldier')\n",
    "plt.plot(Doctor, '--', color='tab:red', label='Doctor')\n",
    "plt.plot(Writer, '-.', color='tab:green', label='Writer')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "02b93ce1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 840x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Police1 = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
    "Police2 = [5, 5, 5, 5, 5, 5, 5, 5, 5, 5]\n",
    "\n",
    "Soldier1 = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
    "Soldier2 = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
    "\n",
    "Doctor1 = [3, 4, 5, 6, 7, 8, 9, 10]\n",
    "Doctor2 = [4, 1, 0, 1, 4, 9, 16, 25]\n",
    "\n",
    "Writer1 = [3, 4, 5, 6, 7]\n",
    "Writer2 = [8, 7, 6, 5, 4]\n",
    "\n",
    "plt.figure(figsize=(8.4, 4.8))\n",
    "\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.grid()\n",
    "\n",
    "plt.scatter(Police1, Police2,marker='o', color='tab:red', label='Police')\n",
    "plt.scatter(Soldier1, Soldier2, marker='^', color='tab:green', label='Soldier')\n",
    "plt.scatter(Doctor1, Doctor2, marker='s', color='tab:blue', label='Doctor')\n",
    "plt.scatter(Writer1, Writer2, marker='*', color='tab:pink', label='Writer')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "0a532ab4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 840x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Police1 = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
    "Police2 = [5, 5, 5, 5, 5, 5, 5, 5, 5, 5]\n",
    "\n",
    "Soldier1 = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
    "Soldier2 = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
    "\n",
    "Doctor1 = [3, 4, 5, 6, 7, 8, 9, 10]\n",
    "Doctor2 = [4, 1, 0, 1, 4, 9, 16, 25]\n",
    "\n",
    "Writer1 = [3, 4, 5, 6, 7]\n",
    "Writer2 = [8, 7, 6, 5, 4]\n",
    "\n",
    "plt.figure(figsize=(8.4, 4.8))\n",
    "\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.grid()\n",
    "\n",
    "plt.plot(Police1, Police2, 'o-', color='tab:red', label='Police')\n",
    "plt.plot(Soldier1, Soldier2, '^', color='tab:green', label='Soldier')\n",
    "plt.plot(Doctor1, Doctor2, 's', color='tab:blue', label='Doctor')\n",
    "plt.plot(Writer1, Writer2, '*-', color='tab:pink', label='Writer')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "de301857",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 840x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Police = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
    "Soldier = [99, 96, 91, 84, 75, 64, 51, 64, 75, 84]\n",
    "s = [50*(1+i) for i in range(10)]\n",
    "\n",
    "c = []\n",
    "for i in range(10):\n",
    "    r = 0.1 * i\n",
    "    g = 0.5\n",
    "    b = 1 - 0.1 * i\n",
    "    c.append([r, g, b])\n",
    "\n",
    "plt.figure(figsize=(8.4, 4.8))\n",
    "\n",
    "plt.xlabel('Police')\n",
    "plt.ylabel('Soldier')\n",
    "\n",
    "plt.scatter(Police, Soldier, s=s, c=c)\n",
    "plt.colorbar(label='Intensity')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "b999ed5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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