charlesmana/issue7_done.ipynb

## issue7_done.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cb9e2c93",
   "metadata": {},
   "outputs": [],
   "source": [
    "import statistics as stat\n",
    "\n",
    "dataset = [0, 0, 2, 2, 4, 3, 5, 0, 0, 3]\n",
    "mean = stat.mean(dataset)\n",
    "median = stat.median(dataset)\n",
    "mode = stat.mode(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cd8c9249",
   "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": [
    "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": 7,
   "id": "9c91918a",
   "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": [
    "y = sorted(x)\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "77404d3f",
   "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": 9,
   "id": "7d1a5393",
   "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": 10,
   "id": "23b8530c",
   "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": [
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2404799f",
   "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": 12,
   "id": "45d35e8c",
   "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": 13,
   "id": "f3835c28",
   "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": [
    "y = sorted(x)\n",
    "print(y) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "99849d1f",
   "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": 15,
   "id": "90011748",
   "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": "9abc56d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean = 40.95238095238095\n"
     ]
    }
   ],
   "source": [
    "sx = sum(x)\n",
    "N = len(x)\n",
    "mean = sx / N\n",
    "print('mean =', mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1bed5ee4",
   "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": "ac8f4a0c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean = 40.95238095238095\n"
     ]
    }
   ],
   "source": [
    "sy = sum(y)\n",
    "N = len(y)\n",
    "mean = sy / N\n",
    "print('mean =', mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "042836e7",
   "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": "e4542f8e",
   "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": [
    "y = sorted(x)\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1021b366",
   "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": "71c2e42c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40\n"
     ]
    }
   ],
   "source": [
    "import statistics as stat\n",
    "print(stat.mode(y))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d941361",
   "metadata": {},
   "source": [
    "# Exercise 1\n",
    "\n",
    "1. Dalam statistik, median mewakili nilai tengah dalam dataset yang telah diurutkan. Ini membagi data menjadi dua bagian yang sama, dengan setengah nilai lebih kecil dari median dan setengah lainnya lebih besar. Sedangkan, middle value mungkin hanya mengacu pada nilai aritmatika pusat dalam dataset yang tidak di urutkan. Namun, ini tidak selalu sama dengan median, terutama ketika dataset memiliki jumlah elemen genap. Kita dapat menemukan nilai tengah dengan mengindeks data yang diurutkan pada posisi tengah. 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. Hal ini disebabkan karena mean mewakili rata-rata dari semua nilai dalam dataset.Ini dihitung dengan menjumlahkan semua nilai dan membaginya dengan jumlah total nilai. Pada mean, Mengurutkan data tidak memengaruhi jumlah nilai, sehingga mean tetap sama apa pun urutan datanya. Sedangkan mode values sendiri mewakili nilai paling sering dalam data set, pada mode values mengurutkan data dapat mengubahfrekuensi setiap nilai. Misalnya, jika nilai paling sering awalnya muncul beberapa kali di posisi berbeda, pengurutan mungkinmengelompokkannya bersama, membuatnya tampak lebih sering.\n",
    "3. Jika kita menghitung range hanya sebagai selisih antara titik data terakhir dan titik data pertama dalam dataset, maka pengurutan tidak lah diperlukan karena urutan data tidak memengaruhi elemen pertama atau terakhir. Apapun pengurutannya, elemen pertama akan tetap menjadi yang pertama dan elemen terakhir akan tetap menjadi yang terakhir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "22d45144",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.hist(x, bins=7 , range=[10,80])\n",
    "plt.yticks(range(0, 11,1))\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "aa9fd567",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.hist(y, bins=7 , range=[10,80])\n",
    "plt.yticks(range(0, 11,1))\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "490e339e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rVq2KqVOnHtDgAIDGVFVYLFiwIDo7O+MnP/lJjB49Op588smIiBgzZkwcdNBBNRkgAFA/qvo7FsuWLYvu7u445ZRTYuLEiZXLrbfeWqvxAQB1pOq3QgAA9sZ3hQAA2QgLACAbYQEAZCMsAIBshAUAkI2wAACyERYAQDbCAgDIRlgAANkICwAgG2EBAGQjLACAbIQFAJCNsAAAshEWAEA2wgIAyEZYAADZCAsAIBthAQBkIywAgGyEBQCQjbAAALIRFgBANsICAMhGWAAA2QgLACAbYQEAZCMsAIBshAUAkI2wAACyERYAQDbCAgDIRlgAANkICwAgG2EBAGQjLACAbIQFAJCNsAAAshEWAEA2wgIAyEZYAADZCAsAIBthAQBkIywAgGyEBQCQjbAAALIRFgBANsICAMhGWAAA2QgLACAbYQEAZFN1WKxZsybOOOOMaG9vj0KhEHfccUcNhgUA1KOqw+K5556LY489Nm644YZajAcAqGMjqr3DnDlzYs6cObUYCwBQ56oOi2qVSqUolUqV6z09PRERUS6Xo1wuZ3uc/mMVh6Vsx6w3/XPPua71pH/e5t+c84+wBs0+/wjPBbV8HtjfYxZSSge8+oVCIVauXBlnnXXWXvdZvHhxLFmyZND2zs7OaG1tPdCHBgBeQb29vTFv3rzo7u6Otra2ve5X87DY0ysWkyZNip07d+5zYNUql8vR1dUVX14/LEp9hWzHrSfFYSmu7OiL2bNnR0tLy1AP5xXX7OdAs//8I5wDzgHnQC3PgZ6enhg3btxLhkXN3wopFotRLBYHbW9paanJiV/qK0Rpd/OdTC9Uq7WtF81+DjT7zz/COeAccA7U4hzY3+P5OxYAQDZVv2Lx7LPPxiOPPFK5vm3btti0aVMceuihMXny5KyDAwDqS9VhsX79+jj11FMr1xcuXBgREfPnz48f/OAH2QYGANSfqsPilFNOiZfx+54AQAPzOxYAQDbCAgDIRlgAANkICwAgG2EBAGQjLACAbIQFAJCNsAAAshEWAEA2wgIAyEZYAADZCAsAIBthAQBkIywAgGyEBQCQjbAAALIRFgBANsICAMhGWAAA2QgLACAbYQEAZCMsAIBshAUAkI2wAACyERYAQDbCAgDIRlgAANkICwAgG2EBAGQjLACAbIQFAJCNsAAAshEWAEA2wgIAyEZYAADZCAsAIBthAQBkIywAgGyEBQCQjbAAALIRFgBANsICAMhGWAAA2QgLACAbYQEAZCMsAIBshAUAkI2wAACyERYAQDbCAgDIRlgAANkcUFh861vfiqlTp8aoUaPi+OOPj/vvvz/3uACAOlR1WNx6661xySWXxBVXXBEbN26Mt7/97TFnzpx4/PHHazE+AKCOjKj2Dtddd1189KMfjY997GMREXH99dfHL3/5y1i2bFksXbp00P6lUilKpVLlend3d0REPP3001Eulw903IOUy+Xo7e2NEeVhsbuvkO249WREX4re3r7417/+FS0tLUM9nFdcs58Dzf7zj3AOOAecA7U8B3bt2hURESmlfe+YqlAqldLw4cPT7bffPmD7RRddlE4++eQ93mfRokUpIlxcXFxcXFwa4LJ9+/Z9tkJVr1js3Lkzdu/eHa997WsHbH/ta18bTz755B7vc9lll8XChQsr1/v6+uLpp5+OsWPHRqGQryZ7enpi0qRJsX379mhra8t23HrS7Gtg/s09/whr0Ozzj7AGtZx/Sil27doV7e3t+9yv6rdCImJQEKSU9hoJxWIxisXigG2vetWrDuRh90tbW1tTnkwv1OxrYP7NPf8Ia9Ds84+wBrWa/5gxY15yn6p+eXPcuHExfPjwQa9O7NixY9CrGABA86kqLEaOHBnHH398dHV1Ddje1dUVb3vb27IODACoP1W/FbJw4cI4//zzo6OjI2bNmhU33nhjPP744/GJT3yiFuPbb8ViMRYtWjTobZdm0uxrYP7NPf8Ia9Ds84+wBv8L8y+kl/zcyGDf+ta34qtf/Wo88cQTMX369Pj6178eJ598ci3GBwDUkQMKCwCAPfFdIQBANsICAMhGWAAA2QgLACCbuguLNWvWxBlnnBHt7e1RKBTijjvuGHB7SikWL14c7e3tcdBBB8Upp5wSW7ZsGZrB1sDSpUvjhBNOiNGjR8f48ePjrLPOiq1btw7Yp5HXYNmyZTFz5szKX5WbNWtW3HXXXZXbG3nue7J06dIoFApxySWXVLY1+hosXrw4CoXCgMuECRMqtzf6/CMi/vnPf8Z5550XY8eOjdbW1jjuuONiw4YNldsbfQ0OP/zwQedAoVCIBQsWRETjz/+///1vfOlLX4qpU6fGQQcdFEcccUR85Stfib6+vso+Q7oG1XwJ2f+CX/ziF+mKK65IK1asSBGRVq5cOeD2a6+9No0ePTqtWLEibd68OX3gAx9IEydOTD09PUMz4Mze8573pJtuuin9+c9/Tps2bUpz585NkydPTs8++2xln0ZegzvvvDP9/Oc/T1u3bk1bt25Nl19+eWppaUl//vOfU0qNPfcXe/DBB9Phhx+eZs6cmS6++OLK9kZfg0WLFqVjjjkmPfHEE5XLjh07Krc3+vyffvrpNGXKlPThD384/f73v0/btm1L9957b3rkkUcq+zT6GuzYsWPAz7+rqytFRLrvvvtSSo0//6uuuiqNHTs2/exnP0vbtm1Lt912WzrkkEPS9ddfX9lnKNeg7sLihV4cFn19fWnChAnp2muvrWx7/vnn05gxY9K3v/3tIRhh7e3YsSNFRFq9enVKqTnX4NWvfnX63ve+11Rz37VrV3rDG96Qurq60jve8Y5KWDTDGixatCgde+yxe7ytGeZ/6aWXppNOOmmvtzfDGrzYxRdfnI488sjU19fXFPOfO3duuuCCCwZsO/vss9N5552XUhr6c6Du3grZl23btsWTTz4Z7373uyvbisVivOMd74jf/va3Qziy2unu7o6IiEMPPTQimmsNdu/eHcuXL4/nnnsuZs2a1VRzX7BgQcydOzfe9a53DdjeLGvw8MMPR3t7e0ydOjU++MEPxqOPPhoRzTH/O++8Mzo6OuL9739/jB8/Pt785jfHd7/73crtzbAGL/Sf//wnbrnllrjggguiUCg0xfxPOumk+NWvfhUPPfRQRET88Y9/jAceeCDe+973RsTQnwMH9O2m/6v6vxxtT1/r/thjjw3FkGoqpRQLFy6Mk046KaZPnx4RzbEGmzdvjlmzZsXzzz8fhxxySKxcuTKmTZtW+QfTyHOPiFi+fHn84Q9/iHXr1g26rRl+/m95y1vihz/8YRx11FHx1FNPxVVXXRVve9vbYsuWLU0x/0cffTSWLVsWCxcujMsvvzwefPDBuOiii6JYLMaHPvShpliDF7rjjjvimWeeiQ9/+MMR0Rz/Bi699NLo7u6Oo48+OoYPHx67d++Oq6++Os4555yIGPo1aKiw6FfN17rXs09/+tPxpz/9KR544IFBtzXyGrzxjW+MTZs2xTPPPBMrVqyI+fPnx+rVqyu3N/Lct2/fHhdffHHcc889MWrUqL3u18hrMGfOnMp/z5gxI2bNmhVHHnlk3HzzzfHWt741Ihp7/n19fdHR0RHXXHNNRES8+c1vji1btsSyZcviQx/6UGW/Rl6DF/r+978fc+bMifb29gHbG3n+t956a9xyyy3R2dkZxxxzTGzatCkuueSSaG9vj/nz51f2G6o1aKi3Qvp/M7wZvtb9wgsvjDvvvDPuu+++OOywwyrbm2ENRo4cGa9//eujo6Mjli5dGscee2x84xvfaIq5b9iwIXbs2BHHH398jBgxIkaMGBGrV6+Ob37zmzFixIjKPBt5DV7s4IMPjhkzZsTDDz/cFOfAxIkTY9q0aQO2velNb4rHH388Iprj/wH9Hnvssbj33nvjYx/7WGVbM8z/85//fHzxi1+MD37wgzFjxow4//zz4zOf+UwsXbo0IoZ+DRoqLKZOnRoTJkwY8LXu//nPf2L16tUN87XuKaX49Kc/Hbfffnv8+te/jqlTpw64vRnW4MVSSlEqlZpi7qeddlps3rw5Nm3aVLl0dHTEueeeG5s2bYojjjii4dfgxUqlUvzlL3+JiRMnNsU5cOKJJw76iPlDDz0UU6ZMiYjm+n/ATTfdFOPHj4+5c+dWtjXD/Ht7e2PYsIFP38OHD6983HTI16Dmvx6a2a5du9LGjRvTxo0bU0Sk6667Lm3cuDE99thjKaX//4jNmDFj0u233542b96czjnnnIb6mNEnP/nJNGbMmLRq1aoBH7fq7e2t7NPIa3DZZZelNWvWpG3btqU//elP6fLLL0/Dhg1L99xzT0qpsee+Ny/8VEhKjb8Gn/3sZ9OqVavSo48+mtauXZtOP/30NHr06PT3v/89pdT483/wwQfTiBEj0tVXX50efvjh9KMf/Si1tramW265pbJPo69BSint3r07TZ48OV166aWDbmv0+c+fPz+97nWvq3zc9Pbbb0/jxo1LX/jCFyr7DOUa1F1Y3HfffSkiBl3mz5+fUvr/j9ksWrQoTZgwIRWLxXTyySenzZs3D+2gM9rT3CMi3XTTTZV9GnkNLrjggjRlypQ0cuTI9JrXvCaddtpplahIqbHnvjcvDotGX4P+z+O3tLSk9vb2dPbZZ6ctW7ZUbm/0+aeU0k9/+tM0ffr0VCwW09FHH51uvPHGAbc3wxr88pe/TBGRtm7dOui2Rp9/T09Puvjii9PkyZPTqFGj0hFHHJGuuOKKVCqVKvsM5Rr42nQAIJuG+h0LAGBoCQsAIBthAQBkIywAgGyEBQCQjbAAALIRFgBANsICAMhGWAAA2QgLACAbYQEAZPN/X21rCVdQxzkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8c966b53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "83df4977",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "35c290ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "fde6031c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{10, 11, 12, 13, 14, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 32, 33, 34, 35, 36, 38, 39, 40, 41, 43, 44, 46, 48, 49, 52, 53, 54, 56, 57, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 71, 72, 73, 74, 75, 78, 79, 80}\n"
     ]
    }
   ],
   "source": [
    "import random as rnd\n",
    "x = {\n",
    "    rnd.randint(10,80)\n",
    "    for i in range(0,100)\n",
    "}\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7cce5302",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9e860e29",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "N = 1000\n",
      "xmin = 20\n",
      "xmax = 60\n",
      "mean = 40.552\n"
     ]
    }
   ],
   "source": [
    "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": 32,
   "id": "dfaa6e24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "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": 33,
   "id": "4bb9babf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EAAAAK1FiAACAlSgxAADASpQYAABgJUoMAACwEiUGAABYiRIDAACsRIkBAABWosQAAAArUWIAAICVKDEAAMBKlBgAAGAlSgwAALASJQYAAFiJEgMAAKxEiQEAAFaixAAAACtRYgAAgJUoMQAAwEqUGAAAYCVKDAAAsBIlBgAAWIkSAwAArESJAQAAVqLEAAAAK1FiAACAlSgxAADASpQYAABgJUoMAACwEiUGAABYiRIDAACsRIkBAABWosQAAAArUWIAAICVKDEAAMBKlBgAAGAlSgwAALASJQYAAFiJEgMAAKxEiQEAAFYakRLT2tqqO+64Q5mZmUpLS9M111yjpqamyP3GGAUCAfn9fqWmpqqwsFDNzc0jMQoAAHApx0tMR0eHZs6cqeTkZL300kv685//rCeeeEKXXHJJ5JiqqipVV1drw4YNamhokM/nU1FRkY4dO+b0OAAAwKWSnD7hunXrlJOTo02bNkX2fepTn4r8tzFGNTU1Wr16tRYuXChJ2rx5s7KyslRbW6ulS5c6PRIAAHAhx0vMtm3bNHfuXN16662qq6vTJz/5SS1btkzf+ta3JEktLS1qa2tTcXFx5DFer1ezZs1SfX39kCUmFAopFApFtru6uk49LsEoMdE4HeGi8SaYqD9t5YYcbsgguSOHGzJI5IgnbsgguSNHUtjZ2T3GGEfPmJKSIkkqKyvTrbfeqt27d6u0tFQ//vGPtXjxYtXX12vmzJlqbW2V3++PPO6ee+7RoUOH9Morrww6ZyAQ0Nq1awftr62tVVpampPjAwCAEdLd3a2SkhJ1dnZqzJgxH/l8jl+JCYfDmj59uioqKiRJU6dOVXNzs55++mktXrw4cpzH44l6nDFm0L4B5eXlKisri2x3dXUpJydHj+5N0MnkRKcjXDTeBKNHpoe1pjFBofDQ2W3ghhxuyCC5I4cbMkjkiCduyCC5I0dSn7NPxXW8xGRnZ+uzn/1s1L6rrrpKv/zlLyVJPp9PktTW1qbs7OzIMe3t7crKyhrynF6vV16vd9D+UNijk/12fiJPFwp7FCJHXHBDBskdOdyQQSJHPHFDBsnuHP0Oly/HX500c+ZM7d+/P2rfgQMHNGHCBElSbm6ufD6fgsFg5P7e3l7V1dWpoKDA6XEAAIBLOX4l5r777lNBQYEqKiq0aNEi7d69Wxs3btTGjRslnfoxUmlpqSoqKpSXl6e8vDxVVFQoLS1NJSUlTo8DAABcyvESc+211+qFF15QeXm5Hn74YeXm5qqmpka333575JhVq1app6dHy5YtU0dHh2bMmKHt27crPT3d6XEAAIBLOV5iJGnevHmaN2/eWe/3eDwKBAIKBAIj8eEBAMDHAO+dBAAArESJAQAAVqLEAAAAK1FiAACAlSgxAADASpQYAABgJUoMAACwEiUGAABYiRIDAACsRIkBAABWosQAAAArUWIAAICVKDEAAMBKlBgAAGAlSgwAALASJQYAAFiJEgMAAKxEiQEAAFaixAAAACtRYgAAgJUoMQAAwEqUGAAAYCVKDAAAsBIlBgAAWIkSAwAArESJAQAAVqLEAAAAK1FiAACAlSgxAADASpQYAABgJUoMAACwEiUGAABYiRIDAACsRIkBAABWosQAAAArUWIAAICVKDEAAMBKlBgAAGAlSgwAALASJQYAAFiJEgMAAKxEiQEAAFaixAAAACtRYgAAgJUoMQAAwEqUGAAAYCVKDAAAsBIlBgAAWIkSAwAArESJAQAAVhrxElNZWSmPx6PS0tLIPmOMAoGA/H6/UlNTVVhYqObm5pEeBQAAuMiIlpiGhgZt3LhRV199ddT+qqoqVVdXa8OGDWpoaJDP51NRUZGOHTs2kuMAAAAXGbESc/z4cd1+++36yU9+oksvvTSy3xijmpoarV69WgsXLlR+fr42b96s7u5u1dbWjtQ4AADAZZJG6sTLly/XTTfdpDlz5ujRRx+N7G9paVFbW5uKi4sj+7xer2bNmqX6+notXbp00LlCoZBCoVBku6ur69TjEowSE81IRRhx3gQT9aet3JDDDRkkd+RwQwaJHPHEDRkkd+RICjs7+4iUmC1btmjPnj1qaGgYdF9bW5skKSsrK2p/VlaWDh06NOT5KisrtXbt2kH7H5waVlpavwMTx9Yj08OxHsERbsjhhgySO3K4IYNEjnjihgyS3Tm6u8MqcfB8jpeYI0eO6Dvf+Y62b9+ulJSUsx7n8Xiito0xg/YNKC8vV1lZWWS7q6tLOTk5enRvgk4mJzozeAx4E4wemR7WmsYEhcJDZ7eBG3K4IYPkjhxuyCCRI564IYPkjhxJfc4+i8XxEtPU1KT29nZNmzYtsq+/v1+7du3Shg0btH//fkmnrshkZ2dHjmlvbx90dWaA1+uV1+sdtD8U9uhkv52fyNOFwh6FyBEX3JBBckcON2SQyBFP3JBBsjtHv8Ply/En9s6ePVtvvvmm9u3bF7lNnz5dt99+u/bt26crrrhCPp9PwWAw8pje3l7V1dWpoKDA6XEAAIBLOX4lJj09Xfn5+VH7Ro8erczMzMj+0tJSVVRUKC8vT3l5eaqoqFBaWppKSpz8SRkAAHCzEXt10rmsWrVKPT09WrZsmTo6OjRjxgxt375d6enpsRgHAABY6KKUmJ07d0ZtezweBQIBBQKBi/HhAQCAC/HeSQAAwEqUGAAAYCVKDAAAsBIlBgAAWIkSAwAArESJAQAAVqLEAAAAK1FiAACAlSgxAADASpQYAABgJUoMAACwEiUGAABYiRIDAACsRIkBAABWosQAAAArUWIAAICVKDEAAMBKlBgAAGAlSgwAALASJQYAAFiJEgMAAKxEiQEAAFaixAAAACtRYgAAgJUoMQAAwEqUGAAAYCVKDAAAsBIlBgAAWIkSAwAArESJAQAAVqLEAAAAK1FiAACAlSgxAADASpQYAABgJUoMAACwEiUGAABYiRIDAACsRIkBAABWosQAAAArUWIAAICVKDEAAMBKlBgAAGAlSgwAALASJQYAAFiJEgMAAKxEiQEAAFaixAAAACtRYgAAgJUoMQAAwEqUGAAAYCXHS0xlZaWuvfZapaena9y4cbrlllu0f//+qGOMMQoEAvL7/UpNTVVhYaGam5udHgUAALiY4yWmrq5Oy5cv12uvvaZgMKiTJ0+quLhYJ06ciBxTVVWl6upqbdiwQQ0NDfL5fCoqKtKxY8ecHgcAALhUktMnfPnll6O2N23apHHjxqmpqUlf/OIXZYxRTU2NVq9erYULF0qSNm/erKysLNXW1mrp0qVOjwQAAFzI8RJzps7OTknS2LFjJUktLS1qa2tTcXFx5Biv16tZs2apvr5+yBITCoUUCoUi211dXacel2CUmGhGcvwR5U0wUX/ayg053JBBckcON2SQyBFP3JBBckeOpLCzs3uMMSP2f8MYo5tvvlkdHR169dVXJUn19fWaOXOmWltb5ff7I8fec889OnTokF555ZVB5wkEAlq7du2g/bW1tUpLSxup8QEAgIO6u7tVUlKizs5OjRkz5iOfb0SvxNx7771644039Ic//GHQfR6PJ2rbGDNo34Dy8nKVlZVFtru6upSTk6NH9yboZHKis0NfRN4Eo0emh7WmMUGh8NDZbeCGHG7IILkjhxsySOSIJ27IILkjR1Kfs0/FHbESs2LFCm3btk27du3S+PHjI/t9Pp8kqa2tTdnZ2ZH97e3tysrKGvJcXq9XXq930P5Q2KOT/XZ+Ik8XCnsUIkdccEMGyR053JBBIkc8cUMGye4c/Q6XL8dfnWSM0b333qvnn39ev//975Wbmxt1f25urnw+n4LBYGRfb2+v6urqVFBQ4PQ4AADApRy/ErN8+XLV1tbqV7/6ldLT09XW1iZJysjIUGpqqjwej0pLS1VRUaG8vDzl5eWpoqJCaWlpKikpcXocAADgUo6XmKefflqSVFhYGLV/06ZN+vrXvy5JWrVqlXp6erRs2TJ1dHRoxowZ2r59u9LT050eBwAAuJTjJWY4L3byeDwKBAIKBAJOf3gAAPAxwXsnAQAAK1FiAACAlSgxAADASpQYAABgJUoMAACwEiUGAABYiRIDAACsRIkBAABWosQAAAArUWIAAICVKDEAAMBKlBgAAGAlSgwAALASJQYAAFiJEgMAAKxEiQEAAFaixAAAACtRYgAAgJUoMQAAwEqUGAAAYCVKDAAAsBIlBgAAWIkSAwAArESJAQAAVqLEAAAAK1FiAACAlSgxAADASpQYAABgJUoMAACwEiUGAABYiRIDAACsRIkBAABWosQAAAArUWIAAICVKDEAAMBKlBgAAGAlSgwAALASJQYAAFiJEgMAAKxEiQEAAFaixAAAACtRYgAAgJUoMQAAwEqUGAAAYCVKDAAAsBIlBgAAWIkSAwAArESJAQAAVqLEAAAAK1FiAACAlWJaYp566inl5uYqJSVF06ZN06uvvhrLcQAAgEViVmJ+/vOfq7S0VKtXr9bevXv1hS98QTfeeKMOHz4cq5EAAIBFYlZiqqurtWTJEn3zm9/UVVddpZqaGuXk5Ojpp5+O1UgAAMAiSbH4oL29vWpqatIDDzwQtb+4uFj19fWDjg+FQgqFQpHtzs5OSVJS34mRHXSEJYWNurvDSupLUH/YE+txPjQ35HBDBskdOdyQQSJHPHFDBskdOQb+3jbGOHNCEwOtra1GkvnjH/8Ytf+xxx4zEydOHHT8Qw89ZCRx48aNGzdu3Fxwe/fddx3pEzG5EjPA44luksaYQfskqby8XGVlZZHtf/zjH5owYYIOHz6sjIyMEZ9zpHR1dSknJ0dHjhzRmDFjYj3Oh+aGHG7IILkjhxsySOSIJ27IILkjR2dnpy6//HKNHTvWkfPFpMRcdtllSkxMVFtbW9T+9vZ2ZWVlDTre6/XK6/UO2p+RkWHtJ/J0Y8aMIUeccEMGyR053JBBIkc8cUMGyR05EhKceUpuTJ7YO2rUKE2bNk3BYDBqfzAYVEFBQSxGAgAAlonZj5PKysp05513avr06bruuuu0ceNGHT58WN/+9rdjNRIAALBIzErMV7/6Vf3tb3/Tww8/rKNHjyo/P1+//e1vNWHChPM+1uv16qGHHhryR0w2IUf8cEMGyR053JBBIkc8cUMGyR05nM7gMcap1zkBAABcPLx3EgAAsBIlBgAAWIkSAwAArESJAQAAVqLEAAAAK8VtiamsrNS1116r9PR0jRs3Trfccov2798fdYwxRoFAQH6/X6mpqSosLFRzc3OMJh7acHKcbunSpfJ4PKqpqbl4Qw7DcHIcP35c9957r8aPH6/U1FRdddVVcfWu5E8//bSuvvrqyG+7vO666/TSSy9Jkvr6+nT//fdr8uTJGj16tPx+vxYvXqwPPvggxlMPdq4cA95++20tWLBAGRkZSk9P1+c//3kdPnw4RhOfX2VlpTwej0pLSyP7bFjfZxoqx+nidX2fbqgM8b62JSkQCMjj8UTdfD6fJLvW97lyDLBhfbe2tuqOO+5QZmam0tLSdM0116ipqSlyv1PrO25LTF1dnZYvX67XXntNwWBQJ0+eVHFxsU6c+Nc7V1dVVam6ulobNmxQQ0ODfD6fioqKdOzYsRhOHm04OQZs3bpVf/rTn+T3+2Mw6bkNJ8d9992nl19+Wc8995zefvtt3XfffVqxYoV+9atfxXDyfxk/frwef/xxNTY2qrGxUV/60pd08803q7m5Wd3d3dqzZ4/WrFmjPXv26Pnnn9eBAwe0YMGCWI89yLlySNK7776r66+/XldeeaV27typ119/XWvWrFFKSkqMJx9aQ0ODNm7cqKuvvjpqvw3r+3RnyzEgntf3gLNliPe1PWDSpEk6evRo5Pbmm29KklXrWzp7DsmO9d3R0aGZM2cqOTlZL730kv785z/riSee0CWXXBI5xrH17cjbSF4E7e3tRpKpq6szxhgTDoeNz+czjz/+eOSYf/7znyYjI8M888wzsRrzvM7MMeD99983n/zkJ81bb71lJkyYYJ588snYDDhMQ+WYNGmSefjhh6OO+/d//3fz4IMPXuzxhu3SSy81P/3pT4e8b/fu3UaSOXTo0EWe6sKdnuOrX/2queOOO2I80fAcO3bM5OXlmWAwaGbNmmW+853vGGPsW99nyzHAhvV9rgw2rO2HHnrITJkyZdjHx+v6Pl8OG9b3/fffb66//vqz3u/k+o7bKzFn6uzslKTIO1+2tLSora1NxcXFkWO8Xq9mzZql+vr6mMw4HGfmkKRwOKw777xT3/ve9zRp0qRYjXZBhspx/fXXa9u2bWptbZUxRjt27NCBAwc0d+7cWI15Vv39/dqyZYtOnDih6667bshjOjs75fF4ov71EG/OzBEOh/Wb3/xGEydO1Ny5czVu3DjNmDFDW7dujfWoQ1q+fLluuukmzZkzJ2q/bev7bDkke9b3uTLYsrYPHjwov9+v3Nxc3XbbbXrvvffOemw8r++z5bBlfW/btk3Tp0/XrbfeqnHjxmnq1Kn6yU9+Ernf0fX9IYvWRRUOh838+fOjmt0f//hHI8m0trZGHfutb33LFBcXX+wRh2WoHMYYU1FRYYqKikw4HDbGmLj9l9qAs+UIhUJm8eLFRpJJSkoyo0aNMv/5n/8ZoymH9sYbb5jRo0ebxMREk5GRYX7zm98MeVxPT4+ZNm2auf322y/yhMNzthxHjx41kkxaWpqprq42e/fuNZWVlcbj8ZidO3fGeOpo//M//2Py8/NNT0+PMcZE/evfpvV9rhzG2LG+z5fBhrX929/+1vziF78wb7zxRuRqUlZWlvnrX/866Nh4Xt/nymHL+vZ6vcbr9Zry8nKzZ88e88wzz5iUlBSzefNmY4yz69uKErNs2TIzYcIEc+TIkci+gf8JH3zwQdSx3/zmN83cuXMv9ojDMlSOxsZGk5WVFfXJjMdvcqcbKocxxvzwhz80EydONNu2bTOvv/66Wb9+vfnEJz5hgsFgjCYdLBQKmYMHD5qGhgbzwAMPmMsuu8w0NzdHHdPb22tuvvlmM3XqVNPZ2RmjSc/tbDlaW1uNJPO1r30t6vj58+eb2267LUbTDnb48GEzbtw4s2/fvsi+oUpMvK/v8+WwYX2fL4MxdqztMx0/ftxkZWWZJ554Imq/Dev7dKfnsGV9Jycnm+uuuy5q34oVK8znP/95Y4yz6zvuS8y9995rxo8fb957772o/e+++66RZPbs2RO1f8GCBWbx4sUXc8RhOVuOJ5980ng8HpOYmBi5STIJCQlmwoQJsRn2HM6Wo7u72yQnJ5tf//rXUfuXLFkSV3/pnGn27NnmnnvuiWz39vaaW265xVx99dVD/gsuXg3kCIVCJikpyTzyyCNR969atcoUFBTEaLrBXnjhBSNp0Nf9wFp45513rFjf58vxox/9KO7X9/kyHD9+3Mq1bYwxc+bMMd/+9rcj27au74Ectqzvyy+/3CxZsiRq31NPPWX8fr8xxtm/v2P2LtbnY4zRihUr9MILL2jnzp3Kzc2Nuj83N1c+n0/BYFBTp06VJPX29qqurk7r1q2LxchDOl+OO++8c9DPoOfOnas777xTd99998Uc9ZzOl6Ovr099fX1KSIh+mlViYqLC4fDFHPWCGGMUCoUkncqwaNEiHTx4UDt27FBmZmaMpxu+gRyjRo3StddeO+jl7wcOHBjWO8RfLLNnz456xYUk3X333bryyit1//3364orrrBifZ8vR3Z29qDnjcTb+j5fhv7+fivXdigU0ttvv60vfOELkuxd36fnsGV9z5w585wzOvr394euWiPsP/7jP0xGRobZuXOnOXr0aOTW3d0dOebxxx83GRkZ5vnnnzdvvvmm+drXvmays7NNV1dXDCePNpwcZ4q3y83GDC/HrFmzzKRJk8yOHTvMe++9ZzZt2mRSUlLMU089FcPJ/6W8vNzs2rXLtLS0mDfeeMN8//vfNwkJCWb79u2mr6/PLFiwwIwfP97s27cvKmMoFIr16FHOlcMYY55//nmTnJxsNm7caA4ePGjWr19vEhMTzauvvhrjyc/tzB9h2LC+hzLUq5NOF4/r+0xnZoj3tW2MMStXrjQ7d+407733nnnttdfMvHnzTHp6uvnLX/5i1fo+Vw5j7Fjfu3fvNklJSeaxxx4zBw8eNP/93/9t0tLSzHPPPRc5xqn1HbclRtKQt02bNkWOCYfD5qGHHjI+n894vV7zxS9+0bz55puxG3oIw8lxpnj8JjecHEePHjVf//rXjd/vNykpKeYzn/mMeeKJJyJPaIy1b3zjG2bChAlm1KhR5t/+7d/M7NmzI3/xt7S0nDXjjh07Yjv4Gc6VY8DPfvYz8+lPf9qkpKSYKVOmmK1bt8Zo2uE78y9OG9b3UNxYYuJ9bRtz6qXH2dnZJjk52fj9frNw4cLI891sWt/nyjHAhvX94osvmvz8fOP1es2VV15pNm7cGHW/U+vbY4wxF3btBgAAIPas+T0xAAAAp6PEAAAAK1FiAACAlSgxAADASpQYAABgJUoMAACwEiUGAABYiRIDAACsRIkBAABWosQAAAArUWIAAICV/h9Fl6uXrdRyfAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 1000; a = 20; b = 60; x = []\n",
    "for i in range(N):\n",
    "    xi = rnd.randint(a, b)\n",
    "    x.append(xi)\n",
    "    \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": 34,
   "id": "9246f7d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 1000; a = 20; b = 60; x = []\n",
    "for i in range(N):\n",
    "    xi = rnd.randint(a, b)\n",
    "    x.append(xi)\n",
    "    \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": 35,
   "id": "9580768b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 1000; a = 20; b = 60; x = []\n",
    "for i in range(N):\n",
    "    xi = rnd.randint(a, b)\n",
    "    x.append(xi)\n",
    "    \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": 36,
   "id": "c24d724d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 1000; a = 20; b = 60; x = []\n",
    "for i in range(N):\n",
    "    xi = rnd.randint(a, b)\n",
    "    x.append(xi)\n",
    "    \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": 37,
   "id": "2c8dbb4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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n3Mfj8SgQCCgQCDj95QEAwKcEn50EAACsRIkBAABWosQAAAArUWIAAICVKDEAAMBKlBgAAGAlSgwAALASJQYAAFiJEgMAAKxEiQEAAFaixAAAACtRYgAAgJUoMQAAwEqUGAAAYCVKDAAAsBIlBgAAWIkSAwAArESJAQAAVqLEAAAAK1FiAACAlSgxAADASpQYAABgJUoMAACwEiUGAABYiRIDAACsRIkBAABWosQAAAArUWIAAICVKDEAAMBKlBgAAGAlSgwAALASJQYAAFiJEgMAAKxEiQEAAFaixAAAACtRYgAAgJUoMQAAwEqUGAAAYCVKDAAAsBIlBgAAWIkSAwAArESJAQAAVqLEAAAAK1FiAACAlSgxAADASpQYAABgJUoMAACwEiUGAABYiRIDAACs1C8l5sCBA7rttts0bNgwpaWl6corr9SOHTsitxtjFAgE5Pf7lZqaqvz8fDU2NvbHKAAAwKUcLzGtra2aPHmykpOT9dJLL+kf//iHHn30UV1wwQWRfcrLy1VRUaHq6mo1NDTI5/OpoKBAhw8fdnocAADgUklOH3D16tXKzs7W2rVrI9suvvjiyH8bY1RZWakVK1Zo7ty5kqR169YpMzNTNTU1WrRokdMjAQAAF3K8xGzatEkzZszQTTfdpLq6On3uc5/T4sWL9Z3vfEeS1NTUpObmZhUWFkbu4/V6NXXqVNXX1/dZYoLBoILBYOR6e3v78fslGCUmGqcjDBhvgon601ZuyOGGDJI7crghg0SOeOKGDJI7ciSFnZ3dY4xx9IgpKSmSpOLiYt1000164403VFRUpJ///OdasGCB6uvrNXnyZB04cEB+vz9yv+9+97vat2+fXnnllV7HDAQCWrVqVa/tNTU1SktLc3J8AADQTzo6OjR//ny1tbVpyJAhn/h4jp+JCYfDmjBhgkpLSyVJ48aNU2Njo5544gktWLAgsp/H44m6nzGm17YeJSUlKi4ujlxvb29Xdna2HtqVoGPJiU5HGDDeBKMHJ4S1cnuCguG+s9vADTnckEFyRw43ZJDIEU/ckEFyR46kkLNPxXW8xGRlZemLX/xi1LbLLrtMv/3tbyVJPp9PktTc3KysrKzIPi0tLcrMzOzzmF6vV16vt9f2YNijY912fiNPFAx7FCRHXHBDBskdOdyQQSJHPHFDBsnuHN0Oly/HX500efJkvffee1Hb9uzZo1GjRkmScnJy5PP5VFtbG7m9q6tLdXV1mjRpktPjAAAAl3L8TMzdd9+tSZMmqbS0VPPmzdMbb7yhNWvWaM2aNZKOP4xUVFSk0tJS5ebmKjc3V6WlpUpLS9P8+fOdHgcAALiU4yXmqquu0oYNG1RSUqIHHnhAOTk5qqys1K233hrZZ9myZers7NTixYvV2tqqiRMnavPmzUpPT3d6HAAA4FKOlxhJmjVrlmbNmnXK2z0ejwKBgAKBQH98eQAA8CnAZycBAAArUWIAAICVKDEAAMBKlBgAAGAlSgwAALASJQYAAFiJEgMAAKxEiQEAAFaixAAAACtRYgAAgJUoMQAAwEqUGAAAYCVKDAAAsBIlBgAAWIkSAwAArESJAQAAVqLEAAAAK1FiAACAlSgxAADASpQYAABgJUoMAACwEiUGAABYiRIDAACsRIkBAABWosQAAAArUWIAAICVKDEAAMBKlBgAAGAlSgwAALASJQYAAFiJEgMAAKxEiQEAAFaixAAAACtRYgAAgJUoMQAAwEqUGAAAYCVKDAAAsBIlBgAAWIkSAwAArESJAQAAVqLEAAAAK1FiAACAlSgxAADASpQYAABgJUoMAACwEiUGAABYiRIDAACsRIkBAABWosQAAAAr9XuJKSsrk8fjUVFRUWSbMUaBQEB+v1+pqanKz89XY2Njf48CAABcpF9LTENDg9asWaPLL788ant5ebkqKipUXV2thoYG+Xw+FRQU6PDhw/05DgAAcJF+KzFHjhzRrbfeqqeeekoXXnhhZLsxRpWVlVqxYoXmzp2rvLw8rVu3Th0dHaqpqemvcQAAgMsk9deBlyxZouuuu07Tp0/XQw89FNne1NSk5uZmFRYWRrZ5vV5NnTpV9fX1WrRoUa9jBYNBBYPByPX29vbj90swSkw0/RWh33kTTNSftnJDDjdkkNyRww0ZJHLEEzdkkNyRIyns7Oz9UmKee+457dy5Uw0NDb1ua25uliRlZmZGbc/MzNS+ffv6PF5ZWZlWrVrVa/t948JKS+t2YOLYenBCONYjOMINOdyQQXJHDjdkkMgRT9yQQbI7R0dHWPMdPJ7jJeajjz7SD37wA23evFkpKSmn3M/j8URdN8b02tajpKRExcXFkevt7e3Kzs7WQ7sSdCw50ZnBY8CbYPTghLBWbk9QMNx3dhu4IYcbMkjuyOGGDBI54okbMkjuyJEUcvZZLI6XmB07dqilpUXjx4+PbOvu7ta2bdtUXV2t9957T9LxMzJZWVmRfVpaWnqdnenh9Xrl9Xp7bQ+GPTrWbec38kTBsEdBcsQFN2SQ3JHDDRkkcsQTN2SQ7M7R7XD5cvyJvdOmTdPbb7+t3bt3Ry4TJkzQrbfeqt27d+uSSy6Rz+dTbW1t5D5dXV2qq6vTpEmTnB4HAAC4lONnYtLT05WXlxe1bfDgwRo2bFhke1FRkUpLS5Wbm6vc3FyVlpYqLS1N8+c7+UgZAABws357ddLpLFu2TJ2dnVq8eLFaW1s1ceJEbd68Wenp6bEYBwAAWGhASszWrVujrns8HgUCAQUCgYH48gAAwIX47CQAAGAlSgwAALASJQYAAFiJEgMAAKxEiQEAAFaixAAAACtRYgAAgJUoMQAAwEqUGAAAYCVKDAAAsBIlBgAAWIkSAwAArESJAQAAVqLEAAAAK1FiAACAlSgxAADASpQYAABgJUoMAACwEiUGAABYiRIDAACsRIkBAABWosQAAAArUWIAAICVKDEAAMBKlBgAAGAlSgwAALASJQYAAFiJEgMAAKxEiQEAAFaixAAAACtRYgAAgJUoMQAAwEqUGAAAYCVKDAAAsBIlBgAAWIkSAwAArESJAQAAVqLEAAAAK1FiAACAlSgxAADASpQYAABgJUoMAACwEiUGAABYiRIDAACsRIkBAABWosQAAAArUWIAAICVKDEAAMBKjpeYsrIyXXXVVUpPT9fw4cN1ww036L333ovaxxijQCAgv9+v1NRU5efnq7Gx0elRAACAizleYurq6rRkyRK9/vrrqq2t1bFjx1RYWKijR49G9ikvL1dFRYWqq6vV0NAgn8+ngoICHT582OlxAACASyU5fcCXX3456vratWs1fPhw7dixQ1/96ldljFFlZaVWrFihuXPnSpLWrVunzMxM1dTUaNGiRU6PBAAAXKjfnxPT1tYmSRo6dKgkqampSc3NzSosLIzs4/V6NXXqVNXX1/f3OAAAwCUcPxNzImOMiouLNWXKFOXl5UmSmpubJUmZmZlR+2ZmZmrfvn19HicYDCoYDEaut7e3S5K8CUaJiaY/Rh8Q3gQT9aet3JDDDRkkd+RwQwaJHPHEDRkkd+RICjs7e7+WmLvuuktvvfWWXnvttV63eTyeqOvGmF7bepSVlWnVqlW9tt83Lqy0tG5nho2hByeEYz2CI9yQww0ZJHfkcEMGiRzxxA0ZJLtzdHSENd/B4/VbiVm6dKk2bdqkbdu2acSIEZHtPp9P0vEzMllZWZHtLS0tvc7O9CgpKVFxcXHkent7u7Kzs/XQrgQdS07spwT9z5tg9OCEsFZuT1Aw3HeBs4Ebcrghg+SOHG7IIJEjnrghg+SOHEkhZ5/F4niJMcZo6dKl2rBhg7Zu3aqcnJyo23NycuTz+VRbW6tx48ZJkrq6ulRXV6fVq1f3eUyv1yuv19trezDs0bFuO7+RJwqGPQqSIy64IYPkjhxuyCCRI564IYNkd45uh8uX4yVmyZIlqqmp0e9+9zulp6dHngOTkZGh1NRUeTweFRUVqbS0VLm5ucrNzVVpaanS0tI0f76TJ5kAAICbOV5innjiCUlSfn5+1Pa1a9fqm9/8piRp2bJl6uzs1OLFi9Xa2qqJEydq8+bNSk9Pd3ocAADgUv3ycNKZeDweBQIBBQIBp788AAD4lOCzkwAAgJUoMQAAwEqUGAAAYCVKDAAAsBIlBgAAWIkSAwAArESJAQAAVqLEAAAAK1FiAACAlSgxAADASpQYAABgJUoMAACwEiUGAABYiRIDAACsRIkBAABWosQAAAArUWIAAICVKDEAAMBKlBgAAGAlSgwAALASJQYAAFiJEgMAAKxEiQEAAFaixAAAACtRYgAAgJUoMQAAwEqUGAAAYCVKDAAAsBIlBgAAWIkSAwAArESJAQAAVqLEAAAAK1FiAACAlSgxAADASpQYAABgJUoMAACwEiUGAABYiRIDAACsRIkBAABWosQAAAArUWIAAICVKDEAAMBKlBgAAGAlSgwAALASJQYAAFiJEgMAAKxEiQEAAFaixAAAACtRYgAAgJViWmIef/xx5eTkKCUlRePHj9df/vKXWI4DAAAsErMS8/zzz6uoqEgrVqzQrl279JWvfEUzZ87U/v37YzUSAACwSMxKTEVFhRYuXKhvf/vbuuyyy1RZWans7Gw98cQTsRoJAABYJCkWX7Srq0s7duzQvffeG7W9sLBQ9fX1vfYPBoMKBoOR621tbZKkpNDR/h20nyWFjTo6wkoKJag77In1OOfNDTnckEFyRw43ZJDIEU/ckEFyR46e39vGGGcOaGLgwIEDRpL561//GrX94YcfNpdeemmv/e+//34jiQsXLly4cOHigssHH3zgSJ+IyZmYHh5PdJM0xvTaJkklJSUqLi6OXP/vf/+rUaNGaf/+/crIyOj3OftLe3u7srOz9dFHH2nIkCGxHue8uSGHGzJI7sjhhgwSOeKJGzJI7sjR1tamkSNHaujQoY4cLyYl5qKLLlJiYqKam5ujtre0tCgzM7PX/l6vV16vt9f2jIwMa7+RJxoyZAg54oQbMkjuyOGGDBI54okbMkjuyJGQ4MxTcmPyxN5BgwZp/Pjxqq2tjdpeW1urSZMmxWIkAABgmZg9nFRcXKzbb79dEyZM0DXXXKM1a9Zo//79+t73vherkQAAgEViVmK+8Y1v6N///rceeOABHTp0SHl5eXrxxRc1atSoM97X6/Xq/vvv7/MhJpuQI364IYPkjhxuyCCRI564IYPkjhxOZ/AY49TrnAAAAAYOn50EAACsRIkBAABWosQAAAArUWIAAICV4rbElJWV6aqrrlJ6erqGDx+uG264Qe+9917UPsYYBQIB+f1+paamKj8/X42NjTGauG9nk+NEixYtksfjUWVl5cANeRbOJseRI0d01113acSIEUpNTdVll10WVx/o+cQTT+jyyy+PvFHUNddco5deekmSFAqFtHz5co0dO1aDBw+W3+/XggULdPDgwRhP3dvpcvR49913NWfOHGVkZCg9PV1f/vKX4/oT4svKyuTxeFRUVBTZZsP6PllfOU4Ur+v7RH1liPe1LUmBQEAejyfq4vP5JNm1vk+Xo4cN6/vAgQO67bbbNGzYMKWlpenKK6/Ujh07Irc7tb7jtsTU1dVpyZIlev3111VbW6tjx46psLBQR4/+/4c+lpeXq6KiQtXV1WpoaJDP51NBQYEOHz4cw8mjnU2OHhs3btTf//53+f3+GEx6emeT4+6779bLL7+s9evX691339Xdd9+tpUuX6ne/+10MJ/9/I0aM0COPPKLt27dr+/bt+trXvqbrr79ejY2N6ujo0M6dO7Vy5Urt3LlTL7zwgvbs2aM5c+bEeuxeTpdDkj744ANNmTJFo0eP1tatW/Xmm29q5cqVSklJifHkfWtoaNCaNWt0+eWXR223YX2f6FQ5esTz+u5xqgzxvrZ7jBkzRocOHYpc3n77bUmyan1Lp84h2bG+W1tbNXnyZCUnJ+ull17SP/7xDz366KO64IILIvs4tr4d+QSmAdDS0mIkmbq6OmOMMeFw2Ph8PvPII49E9vnf//5nMjIyzJNPPhmrMc/o5Bw9Pv74Y/O5z33OvPPOO2bUqFHmsccei82AZ6mvHGPGjDEPPPBA1H5f+tKXzH333TfQ4521Cy+80PziF7/o87Y33njDSDL79u0b4KnO3Yk5vvGNb5jbbrstxhOdncOHD5vc3FxTW1trpk6dan7wgx8YY+xb36fK0cOG9X26DDas7fvvv99cccUVZ71/vK7vM+WwYX0vX77cTJky5ZS3O7m+4/ZMzMna2tokKfKhUU1NTWpublZhYWFkH6/Xq6lTp6q+vj4mM56Nk3NIUjgc1u23364f/ehHGjNmTKxGOyd95ZgyZYo2bdqkAwcOyBijLVu2aM+ePZoxY0asxjyl7u5uPffcczp69KiuueaaPvdpa2uTx+OJ+tdDvDk5Rzgc1h//+EddeumlmjFjhoYPH66JEydq48aNsR61T0uWLNF1112n6dOnR223bX2fKodkz/o+XQZb1vbevXvl9/uVk5Ojm2++WR9++OEp943n9X2qHLas702bNmnChAm66aabNHz4cI0bN05PPfVU5HZH1/d5Fq0BFQ6HzezZs6Oa3V//+lcjyRw4cCBq3+985zumsLBwoEc8K33lMMaY0tJSU1BQYMLhsDHGxO2/1HqcKkcwGDQLFiwwkkxSUpIZNGiQ+eUvfxmjKfv21ltvmcGDB5vExESTkZFh/vjHP/a5X2dnpxk/fry59dZbB3jCs3OqHIcOHTKSTFpamqmoqDC7du0yZWVlxuPxmK1bt8Z46mi//vWvTV5enuns7DTGmKh//du0vk+Xwxg71veZMtiwtl988UXzm9/8xrz11luRs0mZmZnmX//6V69943l9ny6HLevb6/Uar9drSkpKzM6dO82TTz5pUlJSzLp164wxzq5vK0rM4sWLzahRo8xHH30U2dbzP+HgwYNR+3772982M2bMGOgRz0pfObZv324yMzOjvpnx+JfcifrKYYwxP/3pT82ll15qNm3aZN58801TVVVlPvOZz5ja2toYTdpbMBg0e/fuNQ0NDebee+81F110kWlsbIzap6ury1x//fVm3Lhxpq2tLUaTnt6pchw4cMBIMrfcckvU/rNnzzY333xzjKbtbf/+/Wb48OFm9+7dkW19lZh4X99nymHD+j5TBmPsWNsnO3LkiMnMzDSPPvpo1HYb1veJTsxhy/pOTk4211xzTdS2pUuXmi9/+cvGGGfXd9yXmLvuusuMGDHCfPjhh1HbP/jgAyPJ7Ny5M2r7nDlzzIIFCwZyxLNyqhyPPfaY8Xg8JjExMXKRZBISEsyoUaNiM+xpnCpHR0eHSU5ONn/4wx+iti9cuDCufumcbNq0aea73/1u5HpXV5e54YYbzOWXX97nv+DiVU+OYDBokpKSzIMPPhh1+7Jly8ykSZNiNF1vGzZsMJJ6/dz3rIX333/fivV9phw/+9nP4n59nynDkSNHrFzbxhgzffp0873vfS9y3db13ZPDlvU9cuRIs3Dhwqhtjz/+uPH7/cYYZ39/x+wDIM/EGKOlS5dqw4YN2rp1q3JycqJuz8nJkc/nU21trcaNGydJ6urqUl1dnVavXh2Lkft0phy33357r8egZ8yYodtvv1133nnnQI56WmfKEQqFFAqFlJAQ/TSrxMREhcPhgRz1nBhjFAwGJR3PMG/ePO3du1dbtmzRsGHDYjzd2evJMWjQIF111VW9Xv6+Z8+es/pw1YEybdq0qFdcSNKdd96p0aNHa/ny5brkkkusWN9nypGVldXreSPxtr7PlKG7u9vKtR0MBvXuu+/qK1/5iiR71/eJOWxZ35MnTz7tjI7+/j7vqtXPvv/975uMjAyzdetWc+jQocilo6Mjss8jjzxiMjIyzAsvvGDefvttc8stt5isrCzT3t4ew8mjnU2Ok8Xb6WZjzi7H1KlTzZgxY8yWLVvMhx9+aNauXWtSUlLM448/HsPJ/19JSYnZtm2baWpqMm+99Zb58Y9/bBISEszmzZtNKBQyc+bMMSNGjDC7d++OyhgMBmM9epTT5TDGmBdeeMEkJyebNWvWmL1795qqqiqTmJho/vKXv8R48tM7+SEMG9Z3X/p6ddKJ4nF9n+zkDPG+to0x5p577jFbt241H374oXn99dfNrFmzTHp6uvnnP/9p1fo+XQ5j7Fjfb7zxhklKSjIPP/yw2bt3r/nVr35l0tLSzPr16yP7OLW+47bESOrzsnbt2sg+4XDY3H///cbn8xmv12u++tWvmrfffjt2Q/fhbHKcLB7/kjubHIcOHTLf/OY3jd/vNykpKeYLX/iCefTRRyNPaIy1b33rW2bUqFFm0KBB5rOf/ayZNm1a5Bd/U1PTKTNu2bIltoOf5HQ5ejz99NPm85//vElJSTFXXHGF2bhxY4ymPXsn/+K0YX33xY0lJt7XtjHHX3qclZVlkpOTjd/vN3Pnzo08382m9X26HD1sWN+///3vTV5envF6vWb06NFmzZo1Ubc7tb49xhhzbuduAAAAYs+a94kBAAA4ESUGAABYiRIDAACsRIkBAABWosQAAAArUWIAAICVKDEAAMBKlBgAAGAlSgwAALASJQYAAFiJEgMAAKxEiQEAAFb6P6d3QsZHCP79AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "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": "markdown",
   "id": "46c3caa7",
   "metadata": {},
   "source": [
    "# Exercise 3\n",
    "1. N mewakili jumlah titik data yang akan dihasilkan untuk dataset. 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 = b-a+1 = 60-20+1=41Average value = N * 1/n * the width of the bins = 1000 * 1/41 * 4 = 97,5609  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "2929e733",
   "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; 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": 39,
   "id": "4f44114f",
   "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; 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": 40,
   "id": "3da02898",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 1000; a = 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": 41,
   "id": "daf5fe26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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": 42,
   "id": "c7c56174",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RQgAAwFiEEAAAMBYhBAAAjEUIAQAAYxFCAADAWIQQAAAwFiEEAACMRQgBAABjEUIAAMBYhBAAADAWIQQAAIxFCAEAAGMRQgAAwFiEEAAAMBYhBAAAjEUIAQAAYxFCAADAWIQQAAAwFiEEAACMRQgBAABjEUIAAMBYhBAAADAWIQQAAIxFCAEAAGMRQgAAwFiEEAAAMBYhBAAAjEUIAQAAYxFCAADAWIQQAAAwFiEEAACMRQgBAABjEUIAAMBYhBAAADAWIQQAAIxFCAEAAGMRQgAAwFiEEAAAMBYhBAAAjEUIAQAAYxFCAADAWIQQAAAwFiEEAACMRQgBAABjEUIAAMBYhBAAADAWIQQAAIxFCAEAAGP1Swh9+eWXuv/++5WamqrExER997vfVUtLi/1+y7JUXl4un8+nhIQEjR07Vnv37g25jUAgoHnz5mnw4MFKSkrSlClTdOjQoZA1HR0dKikpkcfjkcfjUUlJiY4ePdofIwEAgCjkeAh1dHTo9ttvV3x8vN5//319/PHHeuWVV/Stb33LXlNVVaWlS5eqpqZGzc3N8nq9mjBhgo4dO2avKS0t1aZNm7Rx40Zt3bpVx48fV1FRkXp6euw1xcXFam1tVV1dnerq6tTa2qqSkhKnRwIAAFEqzukbfOmll5SZmak1a9bYx6655hr7vy3L0rJly7R48WJNnTpVkrRu3Tqlp6drw4YNmj17tvx+v1avXq3169dr/PjxkqTa2lplZmZq8+bNKiws1L59+1RXV6ft27crNzdXkrRq1Srl5eVp//79GjZsmNOjAQCAKON4CL3zzjsqLCzU3XffrcbGRn3nO9/RnDlzNGvWLEnSgQMH1NbWpoKCAvs6brdbY8aMUVNTk2bPnq2WlhZ1d3eHrPH5fMrJyVFTU5MKCwu1bds2eTweO4Ik6bbbbpPH41FTU9N5QygQCCgQCNiXOzs7z9x/jKXYWMvpD0VYuWOskD+jCbNFrmiej9kiE7NFprigczM5HkKfffaZli9frrKyMj311FPauXOn5s+fL7fbrQceeEBtbW2SpPT09JDrpaen6+DBg5KktrY2DRgwQIMGDeq15uz129ralJaW1uv+09LS7DXnqqys1HPPPdfr+NMjg0pM7DnPNSLfC6OD4d5Cv2G2yBXN8zFbZGK2yHLyZFDFDt2W4yEUDAY1evRoVVRUSJJGjhypvXv3avny5XrggQfsdS6XK+R6lmX1Onauc9ecb/3FbmfRokUqKyuzL3d2diozM1MvfhSj0/GxXz9cBHHHWHphdFDP7IpRIHjxj2ukYbbIFc3zMVtkYrbIFNft3FOcHQ+hjIwM3XjjjSHHhg8frjfffFOS5PV6JZ05o5ORkWGvaW9vt88Seb1edXV1qaOjI+SsUHt7u/Lz8+01R44c6XX/X331Va+zTWe53W653e5exwNBl073RNeD5KxA0KUAs0WcaJ5Niu75mC0yMVtk6XEw7Bx/1djtt9+u/fv3hxz75JNPNHToUElSVlaWvF6vGhoa7Pd3dXWpsbHRjpxRo0YpPj4+ZM3hw4e1Z88ee01eXp78fr927txpr9mxY4f8fr+9BgAA4GIcPyP04x//WPn5+aqoqNC0adO0c+dOrVy5UitXrpR05ttZpaWlqqioUHZ2trKzs1VRUaHExEQVF5/5jp/H49HMmTO1YMECpaamKiUlRQsXLtSIESPsV5ENHz5cd955p2bNmqUVK1ZIkh5++GEVFRXxijEAAHBJHA+hW2+9VZs2bdKiRYv0/PPPKysrS8uWLdN9991nr3n88cd16tQpzZkzRx0dHcrNzVV9fb2Sk5PtNa+++qri4uI0bdo0nTp1SuPGjdPatWsVG/uX5/K8/vrrmj9/vv3qsilTpqimpsbpkQAAQJRyPIQkqaioSEVFRRd8v8vlUnl5ucrLyy+4ZuDAgaqurlZ1dfUF16SkpKi2trYvWwUAAAbjd40BAABjEUIAAMBYhBAAADAWIQQAAIxFCAEAAGMRQgAAwFiEEAAAMBYhBAAAjEUIAQAAYxFCAADAWIQQAAAwFiEEAACMRQgBAABjEUIAAMBYhBAAADAWIQQAAIxFCAEAAGMRQgAAwFiEEAAAMBYhBAAAjEUIAQAAYxFCAADAWIQQAAAwFiEEAACMRQgBAABjEUIAAMBYhBAAADAWIQQAAIxFCAEAAGMRQgAAwFiEEAAAMBYhBAAAjEUIAQAAYxFCAADAWIQQAAAwFiEEAACMRQgBAABjEUIAAMBYhBAAADAWIQQAAIxFCAEAAGMRQgAAwFiEEAAAMBYhBAAAjEUIAQAAYxFCAADAWIQQAAAwFiEEAACMRQgBAABjEUIAAMBYhBAAADAWIQQAAIxFCAEAAGMRQgAAwFiEEAAAMBYhBAAAjEUIAQAAYxFCAADAWP0eQpWVlXK5XCotLbWPWZal8vJy+Xw+JSQkaOzYsdq7d2/I9QKBgObNm6fBgwcrKSlJU6ZM0aFDh0LWdHR0qKSkRB6PRx6PRyUlJTp69Gh/jwQAAKJEv4ZQc3OzVq5cqZtvvjnkeFVVlZYuXaqamho1NzfL6/VqwoQJOnbsmL2mtLRUmzZt0saNG7V161YdP35cRUVF6unpsdcUFxertbVVdXV1qqurU2trq0pKSvpzJAAAEEX6LYSOHz+u++67T6tWrdKgQYPs45ZladmyZVq8eLGmTp2qnJwcrVu3TidPntSGDRskSX6/X6tXr9Yrr7yi8ePHa+TIkaqtrdXu3bu1efNmSdK+fftUV1enn/70p8rLy1NeXp5WrVql9957T/v37++vsQAAQBTptxCaO3euJk2apPHjx4ccP3DggNra2lRQUGAfc7vdGjNmjJqamiRJLS0t6u7uDlnj8/mUk5Njr9m2bZs8Ho9yc3PtNbfddps8Ho+9BgAA4GLi+uNGN27cqA8//FDNzc293tfW1iZJSk9PDzmenp6ugwcP2msGDBgQcibp7Jqz129ra1NaWlqv209LS7PXnCsQCCgQCNiXOzs7JUnuGEuxsdaljhcR3DFWyJ/RhNkiVzTPx2yRidkiU1zQuZkcD6EvvvhCjz76qOrr6zVw4MALrnO5XCGXLcvqdexc56453/qL3U5lZaWee+65XsefHhlUYmLPea4R+V4YHQz3FvoNs0WuaJ6P2SITs0WWkyeDKnbothwPoZaWFrW3t2vUqFH2sZ6eHm3ZskU1NTX283fa2tqUkZFhr2lvb7fPEnm9XnV1damjoyPkrFB7e7vy8/PtNUeOHOl1/1999VWvs01nLVq0SGVlZfblzs5OZWZm6sWPYnQ6PrYPU1953DGWXhgd1DO7YhQIXjwwIw2zRa5ono/ZIhOzRaa4buee2eN4CI0bN067d+8OOfbQQw/phhtu0BNPPKFrr71WXq9XDQ0NGjlypCSpq6tLjY2NeumllyRJo0aNUnx8vBoaGjRt2jRJ0uHDh7Vnzx5VVVVJkvLy8uT3+7Vz505973vfkyTt2LFDfr/fjqVzud1uud3uXscDQZdO90TXg+SsQNClALNFnGieTYru+ZgtMjFbZOlxMOwcD6Hk5GTl5OSEHEtKSlJqaqp9vLS0VBUVFcrOzlZ2drYqKiqUmJio4uIzJ7o8Ho9mzpypBQsWKDU1VSkpKVq4cKFGjBhhP/l6+PDhuvPOOzVr1iytWLFCkvTwww+rqKhIw4YNc3osAAAQhfrlydJf5/HHH9epU6c0Z84cdXR0KDc3V/X19UpOTrbXvPrqq4qLi9O0adN06tQpjRs3TmvXrlVs7F++hfX6669r/vz59qvLpkyZopqamr/6PAAAIDL9VULoN7/5Tchll8ul8vJylZeXX/A6AwcOVHV1taqrqy+4JiUlRbW1tQ7tEgAAmIbfNQYAAIxFCAEAAGMRQgAAwFiEEAAAMBYhBAAAjEUIAQAAYxFCAADAWIQQAAAwFiEEAACMRQgBAABjEUIAAMBYhBAAADAWIQQAAIxFCAEAAGMRQgAAwFiEEAAAMBYhBAAAjEUIAQAAYxFCAADAWIQQAAAwFiEEAACMRQgBAABjEUIAAMBYhBAAADAWIQQAAIxFCAEAAGMRQgAAwFiEEAAAMBYhBAAAjEUIAQAAYxFCAADAWIQQAAAwFiEEAACMRQgBAABjEUIAAMBYhBAAADAWIQQAAIxFCAEAAGMRQgAAwFiEEAAAMBYhBAAAjEUIAQAAYxFCAADAWIQQAAAwFiEEAACMRQgBAABjEUIAAMBYhBAAADAWIQQAAIxFCAEAAGMRQgAAwFiEEAAAMBYhBAAAjEUIAQAAYxFCAADAWIQQAAAwFiEEAACMRQgBAABjEUIAAMBYjodQZWWlbr31ViUnJystLU0/+MEPtH///pA1lmWpvLxcPp9PCQkJGjt2rPbu3RuyJhAIaN68eRo8eLCSkpI0ZcoUHTp0KGRNR0eHSkpK5PF45PF4VFJSoqNHjzo9EgAAiFKOh1BjY6Pmzp2r7du3q6GhQadPn1ZBQYFOnDhhr6mqqtLSpUtVU1Oj5uZmeb1eTZgwQceOHbPXlJaWatOmTdq4caO2bt2q48ePq6ioSD09Pfaa4uJitba2qq6uTnV1dWptbVVJSYnTIwEAgCgV5/QN1tXVhVxes2aN0tLS1NLSou9///uyLEvLli3T4sWLNXXqVEnSunXrlJ6erg0bNmj27Nny+/1avXq11q9fr/Hjx0uSamtrlZmZqc2bN6uwsFD79u1TXV2dtm/frtzcXEnSqlWrlJeXp/3792vYsGFOjwYAAKKM4yF0Lr/fL0lKSUmRJB04cEBtbW0qKCiw17jdbo0ZM0ZNTU2aPXu2Wlpa1N3dHbLG5/MpJydHTU1NKiws1LZt2+TxeOwIkqTbbrtNHo9HTU1N5w2hQCCgQCBgX+7s7Dxz/zGWYmMtZwcPM3eMFfJnNGG2yBXN8zFbZGK2yBQXdG6mfg0hy7JUVlamO+64Qzk5OZKktrY2SVJ6enrI2vT0dB08eNBeM2DAAA0aNKjXmrPXb2trU1paWq/7TEtLs9ecq7KyUs8991yv40+PDCoxsec814h8L4wOhnsL/YbZIlc0z8dskYnZIsvJk0EVO3Rb/RpCjzzyiH73u99p69atvd7ncrlCLluW1evYuc5dc771F7udRYsWqayszL7c2dmpzMxMvfhRjE7Hx170viONO8bSC6ODemZXjALBi39cIw2zRa5ono/ZIhOzRaa4buee4txvITRv3jy988472rJli66++mr7uNfrlXTmjE5GRoZ9vL293T5L5PV61dXVpY6OjpCzQu3t7crPz7fXHDlypNf9fvXVV73ONp3ldrvldrt7HQ8EXTrdE10PkrMCQZcCzBZxonk2KbrnY7bIxGyRpcfBsHP8VWOWZemRRx7RW2+9pV//+tfKysoKeX9WVpa8Xq8aGhrsY11dXWpsbLQjZ9SoUYqPjw9Zc/jwYe3Zs8dek5eXJ7/fr507d9prduzYIb/fb68BAAC4GMfPCM2dO1cbNmzQz3/+cyUnJ9vP1/F4PEpISJDL5VJpaakqKiqUnZ2t7OxsVVRUKDExUcXFxfbamTNnasGCBUpNTVVKSooWLlyoESNG2K8iGz58uO68807NmjVLK1askCQ9/PDDKioq4hVjAADgkjgeQsuXL5ckjR07NuT4mjVr9OCDD0qSHn/8cZ06dUpz5sxRR0eHcnNzVV9fr+TkZHv9q6++qri4OE2bNk2nTp3SuHHjtHbtWsXG/uW5PK+//rrmz59vv7psypQpqqmpcXokAAAQpRwPIcv6+pe0uVwulZeXq7y8/IJrBg4cqOrqalVXV19wTUpKimpra7/JNgEAAPhdYwAAwFyEEAAAMBYhBAAAjEUIAQAAYxFCAADAWIQQAAAwFiEEAACMRQgBAABjEUIAAMBYhBAAADAWIQQAAIxFCAEAAGMRQgAAwFiEEAAAMBYhBAAAjEUIAQAAYxFCAADAWIQQAAAwFiEEAACMRQgBAABjEUIAAMBYhBAAADAWIQQAAIxFCAEAAGMRQgAAwFiEEAAAMBYhBAAAjEUIAQAAYxFCAADAWIQQAAAwFiEEAACMRQgBAABjEUIAAMBYhBAAADAWIQQAAIxFCAEAAGMRQgAAwFiEEAAAMBYhBAAAjEUIAQAAYxFCAADAWIQQAAAwFiEEAACMRQgBAABjEUIAAMBYhBAAADAWIQQAAIxFCAEAAGMRQgAAwFiEEAAAMBYhBAAAjEUIAQAAYxFCAADAWIQQAAAwFiEEAACMRQgBAABjEUIAAMBYhBAAADBWxIfQf/zHfygrK0sDBw7UqFGj9N///d/h3hIAAIgQER1Cb7zxhkpLS7V48WJ99NFH+vu//3tNnDhRn3/+ebi3BgAAIkBEh9DSpUs1c+ZM/ehHP9Lw4cO1bNkyZWZmavny5eHeGgAAiABx4d7AN9XV1aWWlhY9+eSTIccLCgrU1NR03usEAgEFAgH7st/vlyTFdZ/ov42GSVzQ0smTQcV1x6gn6Ar3dhzFbJErmudjtsjEbJHp7Ndty7L6fmNWhPryyy8tSdb//M//hBxfsmSJdf3115/3Os8++6wliTfeeOONN954i4K3//3f/+1zT0TsGaGzXK7QyrUsq9exsxYtWqSysjL78tGjRzV06FB9/vnn8ng8/brPv7bOzk5lZmbqiy++0FVXXRXu7TiK2SJXNM/HbJGJ2SKT3+/XkCFDlJKS0ufbitgQGjx4sGJjY9XW1hZyvL29Xenp6ee9jtvtltvt7nXc4/FE3YPkrKuuuorZIlA0zyZF93zMFpmYLTLFxPT9qc4R+2TpAQMGaNSoUWpoaAg53tDQoPz8/DDtCgAARJKIPSMkSWVlZSopKdHo0aOVl5enlStX6vPPP9e//Mu/hHtrAAAgAkR0CN1zzz364x//qOeff16HDx9WTk6OfvnLX2ro0KGXdH23261nn332vN8ui3TMFpmieTYpuudjtsjEbJHJydlcluXEa88AAAAiT8Q+RwgAAKCvCCEAAGAsQggAABiLEAIAAMYyMoS2bNmiyZMny+fzyeVy6e233w73lhxTWVmpW2+9VcnJyUpLS9MPfvAD7d+/P9zbcsTy5ct188032z8cLC8vT++//364t9UvKisr5XK5VFpaGu6t9Fl5eblcLlfIm9frDfe2HPPll1/q/vvvV2pqqhITE/Xd735XLS0t4d5Wn11zzTW9/t5cLpfmzp0b7q312enTp/X0008rKytLCQkJuvbaa/X8888rGAyGe2uOOHbsmEpLSzV06FAlJCQoPz9fzc3N4d7WN/J1X68ty1J5ebl8Pp8SEhI0duxY7d2797Luw8gQOnHihG655RbV1NSEeyuOa2xs1Ny5c7V9+3Y1NDTo9OnTKigo0IkTkf+LZa+++mr95Cc/0a5du7Rr1y794z/+o+66667LftBf6Zqbm7Vy5UrdfPPN4d6KY2666SYdPnzYftu9e3e4t+SIjo4O3X777YqPj9f777+vjz/+WK+88oq+9a1vhXtrfdbc3Bzyd3b2h9fefffdYd5Z37300kt67bXXVFNTo3379qmqqkovv/yyqqurw701R/zoRz9SQ0OD1q9fr927d6ugoEDjx4/Xl19+Ge6tXbav+3pdVVWlpUuXqqamRs3NzfJ6vZowYYKOHTt26XfS599WFuEkWZs2bQr3NvpNe3u7JclqbGwM91b6xaBBg6yf/vSn4d6GY44dO2ZlZ2dbDQ0N1pgxY6xHH3003Fvqs2effda65ZZbwr2NfvHEE09Yd9xxR7i38Vfx6KOPWtddd50VDAbDvZU+mzRpkjVjxoyQY1OnTrXuv//+MO3IOSdPnrRiY2Ot9957L+T4LbfcYi1evDhMu3LGuV+vg8Gg5fV6rZ/85Cf2sT//+c+Wx+OxXnvttUu+XSPPCJnE7/dLkiO/mO5K0tPTo40bN+rEiRPKy8sL93YcM3fuXE2aNEnjx48P91Yc9fvf/14+n09ZWVm699579dlnn4V7S4545513NHr0aN19991KS0vTyJEjtWrVqnBvy3FdXV2qra3VjBkzLvhLrSPJHXfcoV/96lf65JNPJEm//e1vtXXrVv3TP/1TmHfWd6dPn1ZPT48GDhwYcjwhIUFbt24N0676x4EDB9TW1qaCggL7mNvt1pgxY9TU1HTJtxPRP1kaF2dZlsrKynTHHXcoJycn3NtxxO7du5WXl6c///nP+pu/+Rtt2rRJN954Y7i35YiNGzfqww8/jNjv5V9Ibm6u/vM//1PXX3+9jhw5ohdffFH5+fnau3evUlNTw729Pvnss8+0fPlylZWV6amnntLOnTs1f/58ud1uPfDAA+HenmPefvttHT16VA8++GC4t+KIJ554Qn6/XzfccINiY2PV09OjJUuW6Ic//GG4t9ZnycnJysvL0wsvvKDhw4crPT1dP/vZz7Rjxw5lZ2eHe3uOOvtL18/9Revp6ek6ePDgJd8OIRTFHnnkEf3ud7+Lqn8FDBs2TK2trTp69KjefPNNTZ8+XY2NjREfQ1988YUeffRR1dfX9/qXXKSbOHGi/d8jRoxQXl6errvuOq1bt05lZWVh3FnfBYNBjR49WhUVFZKkkSNHau/evVq+fHlUhdDq1as1ceJE+Xy+cG/FEW+88YZqa2u1YcMG3XTTTWptbVVpaal8Pp+mT58e7u312fr16zVjxgx95zvfUWxsrP7u7/5OxcXF+vDDD8O9tX5x7llKy7Iu68wlIRSl5s2bp3feeUdbtmzR1VdfHe7tOGbAgAH627/9W0nS6NGj1dzcrH//93/XihUrwryzvmlpaVF7e7tGjRplH+vp6dGWLVtUU1OjQCCg2NjYMO7QOUlJSRoxYoR+//vfh3srfZaRkdErwocPH64333wzTDty3sGDB7V582a99dZb4d6KYx577DE9+eSTuvfeeyWdCfSDBw+qsrIyKkLouuuuU2Njo06cOKHOzk5lZGTonnvuUVZWVri35qizrz5ta2tTRkaGfby9vb3XWaKL4TlCUcayLD3yyCN666239Otf/zrqHvjnsixLgUAg3Nvos3Hjxmn37t1qbW2130aPHq377rtPra2tURNBkhQIBLRv376QT1yR6vbbb+/14yk++eSTS/7Fz5FgzZo1SktL06RJk8K9FcecPHlSMTGhX/5iY2Oj5uXzZyUlJSkjI0MdHR364IMPdNddd4V7S47KysqS1+u1X9EonXk+W2Njo/Lz8y/5dow8I3T8+HF9+umn9uUDBw6otbVVKSkpGjJkSBh31ndz587Vhg0b9POf/1zJycn291A9Ho8SEhLCvLu+eeqppzRx4kRlZmbq2LFj2rhxo37zm9+orq4u3Fvrs+Tk5F7P40pKSlJqamrEP79r4cKFmjx5soYMGaL29na9+OKL6uzsjIp/ef/4xz9Wfn6+KioqNG3aNO3cuVMrV67UypUrw701RwSDQa1Zs0bTp09XXFz0fLmYPHmylixZoiFDhuimm27SRx99pKVLl2rGjBnh3pojPvjgA1mWpWHDhunTTz/VY489pmHDhumhhx4K99Yu29d9vS4tLVVFRYWys7OVnZ2tiooKJSYmqri4+NLvxKFXtUWU//qv/7Ik9XqbPn16uLfWZ+ebS5K1Zs2acG+tz2bMmGENHTrUGjBggPXtb3/bGjdunFVfXx/ubfWbaHn5/D333GNlZGRY8fHxls/ns6ZOnWrt3bs33NtyzLvvvmvl5ORYbrfbuuGGG6yVK1eGe0uO+eCDDyxJ1v79+8O9FUd1dnZajz76qDVkyBBr4MCB1rXXXmstXrzYCgQC4d6aI9544w3r2muvtQYMGGB5vV5r7ty51tGjR8O9rW/k675eB4NB69lnn7W8Xq/ldrut73//+9bu3bsv6z5clmVZfW82AACAyMNzhAAAgLEIIQAAYCxCCAAAGIsQAgAAxiKEAACAsQghAABgLEIIAAAYixACAADGIoQAAICxCCEAAGAsQggAABiLEAIAAMb6f5ffGiVsdJJ6AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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": 43,
   "id": "6ea18799",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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": 44,
   "id": "a4e6b6c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 6000; a = 1; b = 6; x = []\n",
    "for i in range(N):\n",
    "    xi = rnd.randint(a, b)\n",
    "    x.append(xi)\n",
    "plt.hist(x, bins=6)\n",
    "plt.xticks(range(1, 7,1))\n",
    "plt.xlim([1, 6])\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "98a97fdb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 1000; a = 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": 47,
   "id": "194428dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 1000; a = 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, color='tab:red')\n",
    "plt.xticks(range(1, 11,1))\n",
    "plt.xlim([1, 10])\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "6a7ef988",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "x and y must be the same size",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m----------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                     Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[49], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m plt\u001b[38;5;241m.\u001b[39mscatter(x, y)\n\u001b[0;32m      3\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n\u001b[0;32m      5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\matplotlib\\pyplot.py:2862\u001b[0m, in \u001b[0;36mscatter\u001b[1;34m(x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, edgecolors, plotnonfinite, data, **kwargs)\u001b[0m\n\u001b[0;32m   2857\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[38;5;241m.\u001b[39mscatter)\n\u001b[0;32m   2858\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mscatter\u001b[39m(\n\u001b[0;32m   2859\u001b[0m         x, y, s\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, c\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, marker\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, cmap\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, norm\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m   2860\u001b[0m         vmin\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, vmax\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, alpha\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, linewidths\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m,\n\u001b[0;32m   2861\u001b[0m         edgecolors\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, plotnonfinite\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m-> 2862\u001b[0m     __ret \u001b[38;5;241m=\u001b[39m gca()\u001b[38;5;241m.\u001b[39mscatter(\n\u001b[0;32m   2863\u001b[0m         x, y, s\u001b[38;5;241m=\u001b[39ms, c\u001b[38;5;241m=\u001b[39mc, marker\u001b[38;5;241m=\u001b[39mmarker, cmap\u001b[38;5;241m=\u001b[39mcmap, norm\u001b[38;5;241m=\u001b[39mnorm,\n\u001b[0;32m   2864\u001b[0m         vmin\u001b[38;5;241m=\u001b[39mvmin, vmax\u001b[38;5;241m=\u001b[39mvmax, alpha\u001b[38;5;241m=\u001b[39malpha, linewidths\u001b[38;5;241m=\u001b[39mlinewidths,\n\u001b[0;32m   2865\u001b[0m         edgecolors\u001b[38;5;241m=\u001b[39medgecolors, plotnonfinite\u001b[38;5;241m=\u001b[39mplotnonfinite,\n\u001b[0;32m   2866\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m({\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata\u001b[39m\u001b[38;5;124m\"\u001b[39m: data} \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m {}), \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m   2867\u001b[0m     sci(__ret)\n\u001b[0;32m   2868\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m __ret\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\matplotlib\\__init__.py:1442\u001b[0m, in \u001b[0;36m_preprocess_data.<locals>.inner\u001b[1;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1439\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[0;32m   1440\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minner\u001b[39m(ax, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m   1441\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 1442\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m func(ax, \u001b[38;5;241m*\u001b[39m\u001b[38;5;28mmap\u001b[39m(sanitize_sequence, args), \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m   1444\u001b[0m     bound \u001b[38;5;241m=\u001b[39m new_sig\u001b[38;5;241m.\u001b[39mbind(ax, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m   1445\u001b[0m     auto_label \u001b[38;5;241m=\u001b[39m (bound\u001b[38;5;241m.\u001b[39marguments\u001b[38;5;241m.\u001b[39mget(label_namer)\n\u001b[0;32m   1446\u001b[0m                   \u001b[38;5;129;01mor\u001b[39;00m bound\u001b[38;5;241m.\u001b[39mkwargs\u001b[38;5;241m.\u001b[39mget(label_namer))\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\matplotlib\\axes\\_axes.py:4584\u001b[0m, in \u001b[0;36mAxes.scatter\u001b[1;34m(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, edgecolors, plotnonfinite, **kwargs)\u001b[0m\n\u001b[0;32m   4582\u001b[0m y \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mma\u001b[38;5;241m.\u001b[39mravel(y)\n\u001b[0;32m   4583\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m!=\u001b[39m y\u001b[38;5;241m.\u001b[39msize:\n\u001b[1;32m-> 4584\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx and y must be the same size\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m   4586\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m s \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   4587\u001b[0m     s \u001b[38;5;241m=\u001b[39m (\u001b[38;5;241m20\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m mpl\u001b[38;5;241m.\u001b[39mrcParams[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_internal.classic_mode\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;28;01melse\u001b[39;00m\n\u001b[0;32m   4588\u001b[0m          mpl\u001b[38;5;241m.\u001b[39mrcParams[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlines.markersize\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m \u001b[38;5;241m2.0\u001b[39m)\n",
      "\u001b[1;31mValueError\u001b[0m: x and y must be the same size"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.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": 50,
   "id": "ffe5c326",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 1000; a = 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": 51,
   "id": "59af1822",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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": 52,
   "id": "623d7879",
   "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": [
    "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": 53,
   "id": "b10e44f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 1000; a = 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": "2523783d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Wuj0OAACIUa5HzLZt2/S9731Pa9asUWdnp4LBoFavXq1/+qd/iuyzbt06nT17VmvWrFFXV5fmzZun+vp6paSkuD0OAACIUa5HTEpKimpqalRTU/Ox+/h8PoVCIYVCIbdvHgAAfE7w3kkAAMAkIgYAAJhExAAAAJOIGAAAYBIRAwAATCJiAACASUQMAAAwiYgBAAAmETEAAMAkIgYAAJhExAAAAJOIGAAAYBIRAwAATCJiAACASUQMAAAwKcHrARAbjk6f8Yn2G/L7pY0bdCx/ruLD4ShPBQCIZZyJAQAAJhExAADAJCIGAACYRMQAAACTiBgAAGASEQMAAEwiYgAAgElEDAAAMImIAQAAJhExAADAJCIGAACYRMQAAACTiBgAAGASEQMAAEwiYgAAgElEDAAAMImIAQAAJhExAADAJCIGAACYRMQAAACTiBgAAGASEQMAAEwiYgAAgElEDAAAMImIAQAAJhExAADAJCIGAACYRMQAAACTiBgAAGASEQMAAEwiYgAAgElEDAAAMImIAQAAJhExAADApKhEzMmTJ3XbbbcpIyNDycnJuuaaa9TS0hL5vOM4CoVCCgaDSkpKUlFRkdra2qIxCgAAiFGuR0xXV5cKCwuVmJiol156Sb///e/1xBNP6JJLLonss3XrVlVXV6u2tlbNzc0KBAIqLi5Wb2+v2+MAAIAYleD2Abds2aLs7Gzt3Lkzsu3yyy+P/LfjOKqpqVFlZaWWL18uSdq1a5cyMzNVV1en1atXuz0SAACIQa5HzL59+3TdddfppptuUmNjo774xS9qzZo1uuuuuyRJ7e3t6ujoUElJSeQ6fr9fCxcuVFNT03kjJhwOKxwORy739PRIkob9fg3Fx7u9BE8N+/0j/ow1sbw+1mYTa7OJtdk0nOBudvgcx3HcPODEiRMlSRUVFbrpppv0+uuvq7y8XE8//bRuv/12NTU1qbCwUCdPnlQwGIxc7+6779bx48e1f//+UccMhULasGHDqO11dXVKTk52c3wAABAlfX19Ki0tVXd3t1JTU8d9PNfPxAwPDys/P19VVVWSpNmzZ6utrU3bt2/X7bffHtnP5/ONuJ7jOKO2nbN+/XpVVFRELvf09Cg7O1tTf/CEUgcH3V6Cp4b9frVXPqScTVWK+9DZp1gRy+tjbTaxNptYm009Lp+JcT1isrKy9OUvf3nEthkzZuiFF16QJAUCAUlSR0eHsrKyIvt0dnYqMzPzvMf0+/3yn+e0Wlw4rPgYi5hz4sJhxcfYN++HxfL6WJtNrM0m1mZL3NCQu8dz9WiSCgsLdezYsRHb3nzzTU2dOlWSlJOTo0AgoIaGhsjn+/v71djYqIKCArfHAQAAMcr1MzHf/e53VVBQoKqqKq1YsUKvv/66duzYoR07dkj6091I5eXlqqqqUm5urnJzc1VVVaXk5GSVlpa6PQ4AAIhRrkfM3LlztXfvXq1fv14bN25UTk6OampqdOutt0b2Wbdunc6ePas1a9aoq6tL8+bNU319vVJSUtweBwAAxCjXI0aSrr/+el1//fUf+3mfz6dQKKRQKBSNmwcAAJ8DvHcSAAAwiYgBAAAmETEAAMAkIgYAAJhExAAAAJOIGAAAYBIRAwAATCJiAACASUQMAAAwiYgBAAAmETEAAMAkIgYAAJhExAAAAJOIGAAAYBIRAwAATCJiAACASUQMAAAwiYgBAAAmETEAAMAkIgYAAJhExAAAAJOIGAAAYBIRAwAATCJiAACASUQMAAAwiYgBAAAmETEAAMAkIgYAAJhExAAAAJOIGAAAYBIRAwAATCJiAACASUQMAAAwiYgBAAAmETEAAMAkIgYAAJhExAAAAJOIGAAAYBIRAwAATCJiAACASUQMAAAwiYgBAAAmETEAAMAkIgYAAJhExAAAAJOIGAAAYBIRAwAATCJiAACASUQMAAAwiYgBAAAmRT1iNm/eLJ/Pp/Ly8sg2x3EUCoUUDAaVlJSkoqIitbW1RXsUAAAQQ6IaMc3NzdqxY4euvvrqEdu3bt2q6upq1dbWqrm5WYFAQMXFxert7Y3mOAAAIIZELWJOnz6tW2+9VT/60Y906aWXRrY7jqOamhpVVlZq+fLlysvL065du9TX16e6urpojQMAAGJMQrQOXFZWpqVLl2rx4sV69NFHI9vb29vV0dGhkpKSyDa/36+FCxeqqalJq1evHnWscDiscDgcudzT0yNJGvb7NRQfH60leGLY7x/xZ6yJ5fWxNptYm02szabhBHezw+c4juPqESXt2bNHmzZtUnNzsyZOnKiioiJdc801qqmpUVNTkwoLC3Xy5EkFg8HIde6++24dP35c+/fvH3W8UCikDRs2jNpeV1en5ORkt8cHAABR0NfXp9LSUnV3dys1NXXcx3P9TMyJEye0du1a1dfXa+LEiR+7n8/nG3HZcZxR285Zv369KioqIpd7enqUnZ2tqT94QqmDg+4MfpEY9vvVXvmQcjZVKe5DZ59iRSyvj7XZxNpsYm029bh8Jsb1iGlpaVFnZ6fmzJkT2TY0NKQDBw6otrZWx44dkyR1dHQoKysrsk9nZ6cyMzPPe0y/3y//eU6rxYXDio+xiDknLhxWfIx9835YLK+PtdnE2mxibbbEDQ25ezxXjyZp0aJFOnLkiFpbWyMf+fn5uvXWW9Xa2qpp06YpEAiooaEhcp3+/n41NjaqoKDA7XEAAECMcv1MTEpKivLy8kZsmzRpkjIyMiLby8vLVVVVpdzcXOXm5qqqqkrJyckqLS11exwAABCjovbspD9n3bp1Onv2rNasWaOuri7NmzdP9fX1SklJ8WIcAABg0GcSMa+99tqIyz6fT6FQSKFQ6LO4eQAAEIN47yQAAGASEQMAAEwiYgAAgElEDAAAMImIAQAAJhExAADAJCIGAACYRMQAAACTiBgAAGASEQMAAEwiYgAAgElEDAAAMImIAQAAJhExAADAJCIGAACYRMQAAACTiBgAAGASEQMAAEwiYgAAgElEDAAAMImIAQAAJhExAADAJCIGAACYRMQAAACTiBgAAGASEQMAAEwiYgAAgElEDAAAMImIAQAAJhExAADAJCIGAACYRMQAAACTiBgAAGASEQMAAEwiYgAAgElEDAAAMImIAQAAJhExAADAJCIGAACYRMQAAACTiBgAAGASEQMAAEwiYgAAgElEDAAAMImIAQAAJhExAADAJCIGAACYRMQAAACTiBgAAGCS6xGzefNmzZ07VykpKZo8ebJuvPFGHTt2bMQ+juMoFAopGAwqKSlJRUVFamtrc3sUAAAQw1yPmMbGRpWVlek3v/mNGhoaNDg4qJKSEp05cyayz9atW1VdXa3a2lo1NzcrEAiouLhYvb29bo8DAABiVILbB3z55ZdHXN65c6cmT56slpYWffWrX5XjOKqpqVFlZaWWL18uSdq1a5cyMzNVV1en1atXuz0SAACIQVF/TEx3d7ckKT09XZLU3t6ujo4OlZSURPbx+/1auHChmpqaoj0OAACIEa6fifkwx3FUUVGhBQsWKC8vT5LU0dEhScrMzByxb2Zmpo4fP37e44TDYYXD4cjlnp4eSdKw36+h+PhojO6ZYb9/xJ+xJpbXx9psYm02sTabhhPczQ6f4ziOq0f8kLKyMv3bv/2bDh48qMsuu0yS1NTUpMLCQr333nvKysqK7HvXXXfpxIkTo+6OkqRQKKQNGzaM2l5XV6fk5ORojQ8AAFzU19en0tJSdXd3KzU1ddzHi9qZmHvuuUf79u3TgQMHIgEjSYFAQNKfzsh8OGI6OztHnZ05Z/369aqoqIhc7unpUXZ2tqb+4AmlDg5GaQXeGPb71V75kHI2VSnuQ2efYkUsr4+12cTabGJtNvW4fCbG9YhxHEf33HOP9u7dq9dee005OTkjPp+Tk6NAIKCGhgbNnj1bktTf36/GxkZt2bLlvMf0+/3yn+e0Wlw4rPgYi5hz4sJhxcfYN++HxfL6WJtNrM0m1mZL3NCQq8dzPWLKyspUV1enn/70p0pJSYk8BiYtLU1JSUny+XwqLy9XVVWVcnNzlZubq6qqKiUnJ6u0tNTtcQAAQIxyPWK2b98uSSoqKhqxfefOnfrWt74lSVq3bp3Onj2rNWvWqKurS/PmzVN9fb1SUlLcHgcAAMSoqNyddCE+n0+hUEihUMjtmwcAAJ8TvHcSAAAwiYgBAAAmETEAAMAkIgYAAJhExAAAAJOIGAAAYBIRAwAATCJiAACASUQMAAAwiYgBAAAmETEAAMAkIgYAAJhExAAAAJOIGAAAYBIRAwAATCJiAACASUQMAAAwiYgBAAAmETEAAMAkIgYAAJhExAAAAJOIGAAAYBIRAwAATCJiAACASUQMAAAwiYgBAAAmETEAAMAkIgYAAJhExAAAAJOIGAAAYBIRAwAATCJiAACASUQMAAAwiYgBAAAmETEAAMAkIgYAAJhExAAAAJOIGAAAYBIRAwAATCJiAACASUQMAAAwiYgBAAAmETEAAMAkIgYAAJhExAAAAJOIGAAAYBIRAwAATCJiAACASUQMAAAwiYgBAAAmeRoxTz75pHJycjRx4kTNmTNH//7v/+7lOAAAwBDPIub5559XeXm5Kisr9cYbb+hv/uZvtGTJEr377rtejQQAAAzxLGKqq6t155136tvf/rZmzJihmpoaZWdna/v27V6NBAAADEnw4kb7+/vV0tKiBx98cMT2kpISNTU1jdo/HA4rHA5HLnd3d0uSehM8GT+qhhMS1NfXp56EBMUNDXk9jutieX2szSbWZhNrs+nc723Hcdw5oOOBkydPOpKcX//61yO2b9q0yfnSl740av9HHnnEkcQHH3zwwQcffMTAx3//93+70hOensrw+XwjLjuOM2qbJK1fv14VFRWRy++//76mTp2qd999V2lpaVGf87PU09Oj7OxsnThxQqmpqV6P47pYXh9rs4m12cTabOru7taUKVOUnp7uyvE8iZgvfOELio+PV0dHx4jtnZ2dyszMHLW/3++X3+8ftT0tLS3mvsDnpKamxuzapNheH2uzibXZxNpsiotz5yG5njywd8KECZozZ44aGhpGbG9oaFBBQYEXIwEAAGM8uzupoqJCf//3f6/8/HzNnz9fO3bs0Lvvvqt/+Id/8GokAABgiGcRc/PNN+uPf/yjNm7cqFOnTikvL0+/+MUvNHXq1Ate1+/365FHHjnvXUzWxfLapNheH2uzibXZxNpscnttPsdx63lOAAAAnx3eOwkAAJhExAAAAJOIGAAAYBIRAwAATDIVMQcOHNCyZcsUDAbl8/n04osvej2SazZv3qy5c+cqJSVFkydP1o033qhjx455PZYrtm/frquvvjrywk3z58/XSy+95PVYUbF582b5fD6Vl5d7Pcq4hUIh+Xy+ER+BQMDrsVxz8uRJ3XbbbcrIyFBycrKuueYatbS0eD2WKy6//PJRXzufz6eysjKvRxu3wcFBPfzww8rJyVFSUpKmTZumjRs3anh42OvRXNHb26vy8nJNnTpVSUlJKigoUHNzs9djjdmFfl87jqNQKKRgMKikpCQVFRWpra1tzLdjKmLOnDmjWbNmqba21utRXNfY2KiysjL95je/UUNDgwYHB1VSUqIzZ854Pdq4XXbZZXrsscd06NAhHTp0SH/7t3+rG2644VN9w17MmpubtWPHDl199dVej+KamTNn6tSpU5GPI0eOeD2SK7q6ulRYWKjExES99NJL+v3vf68nnnhCl1xyidejuaK5uXnE1+3cC4vedNNNHk82flu2bNFTTz2l2tpaHT16VFu3btXjjz+ubdu2eT2aK7797W+roaFBzz33nI4cOaKSkhItXrxYJ0+e9Hq0MbnQ7+utW7equrpatbW1am5uViAQUHFxsXp7e8d2Q668A5MHJDl79+71eoyo6ezsdCQ5jY2NXo8SFZdeeqnzL//yL16P4Zre3l4nNzfXaWhocBYuXOisXbvW65HG7ZFHHnFmzZrl9RhR8cADDzgLFizweozPzNq1a50rrrjCGR4e9nqUcVu6dKmzatWqEduWL1/u3HbbbR5N5J6+vj4nPj7e+fnPfz5i+6xZs5zKykqPphq/j/6+Hh4edgKBgPPYY49Ftn3wwQdOWlqa89RTT43p2KbOxHyedHd3S5Jrb5J1sRgaGtKePXt05swZzZ8/3+txXFNWVqalS5dq8eLFXo/iqrfeekvBYFA5OTm65ZZb9M4773g9kiv27dun/Px83XTTTZo8ebJmz56tH/3oR16PFRX9/f3avXu3Vq1add432LVmwYIF+uUvf6k333xTkvTb3/5WBw8e1Ne+9jWPJxu/wcFBDQ0NaeLEiSO2JyUl6eDBgx5N5b729nZ1dHSopKQkss3v92vhwoVqamoa07E8fRdrnJ/jOKqoqNCCBQuUl5fn9TiuOHLkiObPn68PPvhAf/EXf6G9e/fqy1/+stdjuWLPnj06fPiwyfut/5x58+bpX//1X/WlL31J//u//6tHH31UBQUFamtrU0ZGhtfjjcs777yj7du3q6KiQg899JBef/11/eM//qP8fr9uv/12r8dz1Ysvvqj3339f3/rWt7wexRUPPPCAuru7NX36dMXHx2toaEibNm3SN7/5Ta9HG7eUlBTNnz9f3//+9zVjxgxlZmbqxz/+sf7zP/9Tubm5Xo/nmnNv/vzRN3zOzMzU8ePHx3QsIuYi9J3vfEf/9V//FVPlfeWVV6q1tVXvv/++XnjhBa1cuVKNjY3mQ+bEiRNau3at6uvrR/3fk3VLliyJ/PdVV12l+fPn64orrtCuXbtUUVHh4WTjNzw8rPz8fFVVVUmSZs+erba2Nm3fvj3mIuaZZ57RkiVLFAwGvR7FFc8//7x2796turo6zZw5U62trSovL1cwGNTKlSu9Hm/cnnvuOa1atUpf/OIXFR8fr6985SsqLS3V4cOHvR7NdR89M+g4zpjPFhIxF5l77rlH+/bt04EDB3TZZZd5PY5rJkyYoL/6q7+SJOXn56u5uVn//M//rKefftrjycanpaVFnZ2dmjNnTmTb0NCQDhw4oNraWoXDYcXHx3s4oXsmTZqkq666Sm+99ZbXo4xbVlbWqICeMWOGXnjhBY8mio7jx4/rlVde0U9+8hOvR3HN/fffrwcffFC33HKLpD8F9vHjx7V58+aYiJgrrrhCjY2NOnPmjHp6epSVlaWbb75ZOTk5Xo/mmnPPcuzo6FBWVlZke2dn56izMxfCY2IuEo7j6Dvf+Y5+8pOf6Fe/+lVMfcOej+M4CofDXo8xbosWLdKRI0fU2toa+cjPz9ett96q1tbWmAkYSQqHwzp69OiIf3SsKiwsHPUSBm+++eYnegNaS3bu3KnJkydr6dKlXo/imr6+PsXFjfzVFR8fHzNPsT5n0qRJysrKUldXl/bv368bbrjB65Fck5OTo0AgEHnWnPSnx241NjaqoKBgTMcydSbm9OnTevvttyOX29vb1draqvT0dE2ZMsXDycavrKxMdXV1+ulPf6qUlJTIfYZpaWlKSkryeLrxeeihh7RkyRJlZ2ert7dXe/bs0WuvvaaXX37Z69HGLSUlZdTjliZNmqSMjAzzj2e67777tGzZMk2ZMkWdnZ169NFH1dPTExP/t/vd735XBQUFqqqq0ooVK/T6669rx44d2rFjh9ejuWZ4eFg7d+7UypUrlZBg6p/6P2vZsmXatGmTpkyZopkzZ+qNN95QdXW1Vq1a5fVorti/f78cx9GVV16pt99+W/fff7+uvPJK3XHHHV6PNiYX+n1dXl6uqqoq5ebmKjc3V1VVVUpOTlZpaenYbsiFZ099Zl599VVH0qiPlStXej3auJ1vXZKcnTt3ej3auK1atcqZOnWqM2HCBOcv//IvnUWLFjn19fVejxU1sfIU65tvvtnJyspyEhMTnWAw6Cxfvtxpa2vzeizX/OxnP3Py8vIcv9/vTJ8+3dmxY4fXI7lq//79jiTn2LFjXo/iqp6eHmft2rXOlClTnIkTJzrTpk1zKisrnXA47PVornj++eedadOmORMmTHACgYBTVlbmvP/++16PNWYX+n09PDzsPPLII04gEHD8fr/z1a9+1Tly5MiYb8fnOI4zvt4CAAD47PGYGAAAYBIRAwAATCJiAACASUQMAAAwiYgBAAAmETEAAMAkIgYAAJhExAAAAJOIGAAAYBIRAwAATCJiAACASUQMAAAw6f8BJ+h92akVw+kAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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": 55,
   "id": "c8816103",
   "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": [
    "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": 56,
   "id": "a908df83",
   "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.         0.96456782]\n",
      " [0.96456782 1.        ]]\n"
     ]
    }
   ],
   "source": [
    "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": 57,
   "id": "88f17a77",
   "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.         0.90389645]\n",
      " [0.90389645 1.        ]]\n"
     ]
    }
   ],
   "source": [
    "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": 58,
   "id": "d7188047",
   "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.         0.82122875]\n",
      " [0.82122875 1.        ]]\n"
     ]
    }
   ],
   "source": [
    "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": 59,
   "id": "7964d317",
   "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.         0.99438046]\n",
      " [0.99438046 1.        ]]\n"
     ]
    }
   ],
   "source": [
    "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": 60,
   "id": "be92bc4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "mu = 100\n",
    "si = 5\n",
    "N = 100000\n",
    "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": 61,
   "id": "cafff1ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "mu = 100\n",
    "si = 5\n",
    "N = 100000\n",
    "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": 62,
   "id": "9ab7c511",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "mu = 100\n",
    "si = 5\n",
    "N = 100000\n",
    "values = np.random.normal(mu, si, N)\n",
    "\n",
    "plt.hist(values, 100)\n",
    "plt.axvline(values.mean(), color='r')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "94a5ab0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "mu = 100\n",
    "si = 5\n",
    "N = 100000\n",
    "values1 = np.random.normal(mu, si, N)\n",
    "\n",
    "plt.hist(values1, 100)\n",
    "plt.axvline(values1.mean(), color='r')\n",
    "plt.xlim([20,160])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "808b94c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "mu = 100\n",
    "si = 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": 65,
   "id": "52c8ceb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Kj493ao4fP97kXCdOnHBqLrRkyRL5/X5nKy8vb8ulAQCAbqxVwce2bc2bN08bN27Uhx9+qP79+//g93zzzTcqLy9XQkKCJGnYsGEKCwtTQUGBU1NZWakDBw5o5MiRkqTU1FT5/X7t2bPHqdm9e7f8fr9Tc6GIiAhFR0cHbQAAAOdr1a2uuXPn6u2339af/vQnRUVFOfNtLMtSZGSk6urqtHTpUt13331KSEjQ0aNH9dRTTyk2Nlb33nuvUztz5kwtWrRIffr0UUxMjBYvXqwhQ4Y4q7wGDRqkiRMnatasWVqzZo0kafbs2crIyGBFFwAAaLNWBZ/Vq1dLksaMGRO0/4033tCMGTMUEhKi/fv366233lJNTY0SEhJ055136p133lFUVJRTv2rVKoWGhmrq1Kmqr6/XuHHjtHbtWoWEhDg1GzZs0IIFC5zVX5mZmcrNzW3rdQIAALQu+Ni2fdHjkZGR+uCDD37wPD169NBLL72kl156qcWamJgYrV+/vjXNAwAAuCje1QUAAIxB8AEAAMZo83N8AABmaelFs0eXT77CLQHajhEfAABgDIIPAAAwBsEHAAAYg+ADAACMQfABAADGIPgAAABjsJwdQJu1tLwZALoqRnwAAIAxCD4AAMAYBB8AAGAMgg8AADAGk5sBAJflYpPceY8XuhpGfAAAgDEIPgAAwBgEHwAAYAyCDwAAMAbBBwAAGIPgAwAAjEHwAQAAxiD4AAAAYxB8AACAMQg+AADAGAQfAABgDIIPAAAwBsEHAAAYg7ezA7ioi715GwCuNoz4AAAAYxB8AACAMQg+AADAGAQfAABgDIIPAAAwBsEHAAAYg+ADAACMQfABAADGIPgAAABj8ORmAECHaenJ30eXT77CLQG+16oRn5ycHN12222KiopSXFyc7rnnHh0+fDioxrZtLV26VB6PR5GRkRozZowOHjwYVBMIBDR//nzFxsaqV69eyszMVEVFRVBNdXW1vF6vLMuSZVnyer2qqalp21UCAAColcGnsLBQc+fOVXFxsQoKCvTdd98pLS1Np06dcmpWrFihlStXKjc3V3v37pXb7daECRN08uRJpyY7O1ubNm1SXl6edu7cqbq6OmVkZOjMmTNOTVZWlkpLS5Wfn6/8/HyVlpbK6/W2wyUDAABTuWzbttv6zSdOnFBcXJwKCwt1xx13yLZteTweZWdn64knnpD0/ehOfHy8nnvuOc2ZM0d+v1/XXXed1q1bp2nTpkmSjh07psTERG3dulXp6ek6dOiQBg8erOLiYqWkpEiSiouLlZqaqi+++EIDBw78wbbV1tbKsiz5/X5FR0e39RIB4/GS0ouLbDitQ6t+Jkka9OgfVR/eo5NbdHXgVhda0tF/vy9rcrPf75ckxcTESJLKysrk8/mUlpbm1ERERGj06NEqKiqSJJWUlKixsTGoxuPxKDk52anZtWuXLMtyQo8kjRgxQpZlOTUAAACt1ebJzbZta+HChRo1apSSk5MlST6fT5IUHx8fVBsfH6+vvvrKqQkPD1fv3r2b1Jz7fp/Pp7i4uCY/My4uzqm5UCAQUCAQcD7X1ta28coAAEB31eYRn3nz5umzzz7Tf/7nfzY55nK5gj7btt1k34UurGmu/mLnycnJcSZCW5alxMTES7kMAABgkDYFn/nz5+vdd9/VRx99pL59+zr73W63JDUZlamqqnJGgdxutxoaGlRdXX3RmuPHjzf5uSdOnGgymnTOkiVL5Pf7na28vLwtlwYAALqxVgUf27Y1b948bdy4UR9++KH69+8fdLx///5yu90qKChw9jU0NKiwsFAjR46UJA0bNkxhYWFBNZWVlTpw4IBTk5qaKr/frz179jg1u3fvlt/vd2ouFBERoejo6KANAADgfK2a4zN37ly9/fbb+tOf/qSoqChnZMeyLEVGRsrlcik7O1vLli3TgAEDNGDAAC1btkw9e/ZUVlaWUztz5kwtWrRIffr0UUxMjBYvXqwhQ4Zo/PjxkqRBgwZp4sSJmjVrltasWSNJmj17tjIyMi5pRRcAAEBzWhV8Vq9eLUkaM2ZM0P433nhDM2bMkCQ9/vjjqq+v18MPP6zq6mqlpKRo27ZtioqKcupXrVql0NBQTZ06VfX19Ro3bpzWrl2rkJAQp2bDhg1asGCBs/orMzNTubm5bblGAAAASZf5HJ+ujOf4AO2D5/hcHM/xaRue44OWdOnn+AAAAFxNeEkpAEmM7AAwAyM+AADAGAQfAABgDIIPAAAwBsEHAAAYg8nNAIArrqXJ9CxzR0djxAcAABiD4AMAAIxB8AEAAMYg+AAAAGMQfAAAgDEIPgAAwBgEHwAAYAyCDwAAMAbBBwAAGIMnNwOGaemJuQBgAkZ8AACAMQg+AADAGAQfAABgDIIPAAAwBsEHAAAYg+ADAACMwXJ2AECX0dLjFo4un3yFW4LuihEfAABgDIIPAAAwBsEHAAAYg+ADAACMQfABAADGIPgAAABjEHwAAIAxCD4AAMAYBB8AAGAMgg8AADAGwQcAABiD4AMAAIxB8AEAAMbg7exAN9XSW64BwGSM+AAAAGO0Ovh8/PHHuuuuu+TxeORyubR58+ag4zNmzJDL5QraRowYEVQTCAQ0f/58xcbGqlevXsrMzFRFRUVQTXV1tbxeryzLkmVZ8nq9qqmpafUFAgAAnNPqW12nTp3S0KFD9a//+q+67777mq2ZOHGi3njjDedzeHh40PHs7Gy99957ysvLU58+fbRo0SJlZGSopKREISEhkqSsrCxVVFQoPz9fkjR79mx5vV699957rW0yAOAqd7Fbt0eXT76CLcHVrtXBZ9KkSZo0adJFayIiIuR2u5s95vf79frrr2vdunUaP368JGn9+vVKTEzU9u3blZ6erkOHDik/P1/FxcVKSUmRJL322mtKTU3V4cOHNXDgwNY2GwAAoGPm+OzYsUNxcXG68cYbNWvWLFVVVTnHSkpK1NjYqLS0NGefx+NRcnKyioqKJEm7du2SZVlO6JGkESNGyLIsp+ZCgUBAtbW1QRsAAMD52j34TJo0SRs2bNCHH36o3//+99q7d6/Gjh2rQCAgSfL5fAoPD1fv3r2Dvi8+Pl4+n8+piYuLa3LuuLg4p+ZCOTk5znwgy7KUmJjYzlcGAACudu2+nH3atGnO18nJyRo+fLiSkpK0ZcsWTZkypcXvs21bLpfL+Xz+1y3VnG/JkiVauHCh87m2tpbwAwAAgnT4cvaEhAQlJSXpyJEjkiS3262GhgZVV1cH1VVVVSk+Pt6pOX78eJNznThxwqm5UEREhKKjo4M2AACA83V48Pnmm29UXl6uhIQESdKwYcMUFhamgoICp6ayslIHDhzQyJEjJUmpqany+/3as2ePU7N79275/X6nBgAAoLVafaurrq5Of/3rX53PZWVlKi0tVUxMjGJiYrR06VLdd999SkhI0NGjR/XUU08pNjZW9957ryTJsizNnDlTixYtUp8+fRQTE6PFixdryJAhziqvQYMGaeLEiZo1a5bWrFkj6fvl7BkZGazoAgAAbdbq4LNv3z7deeedzudz82qmT5+u1atXa//+/XrrrbdUU1OjhIQE3XnnnXrnnXcUFRXlfM+qVasUGhqqqVOnqr6+XuPGjdPatWudZ/hI0oYNG7RgwQJn9VdmZqZyc3PbfKEAAAAu27btzm5ER6itrZVlWfL7/cz3gZF4V9eVEdlwWodW/UySNOjRP6o+vEcnt8g8PMCwe+nov9+8qwsAABiDt7MDAK5qLY1uMhKE5hB8gKsct7QA4NJxqwsAABiD4AMAAIxB8AEAAMYg+AAAAGMQfAAAgDEIPgAAwBgEHwAAYAyCDwAAMAbBBwAAGIPgAwAAjEHwAQAAxiD4AAAAYxB8AACAMQg+AADAGAQfAABgDIIPAAAwBsEHAAAYg+ADAACMQfABAADGIPgAAABjhHZ2AwD8sJ88uaWzmwAA3QIjPgAAwBgEHwAAYAyCDwAAMAZzfAAA3VJLc+OOLp98hVuCroQRHwAAYAyCDwAAMAbBBwAAGIPgAwAAjEHwAQAAxiD4AAAAY7CcHehCeDUFAHQsRnwAAIAxCD4AAMAYBB8AAGAMgg8AADBGq4PPxx9/rLvuuksej0cul0ubN28OOm7btpYuXSqPx6PIyEiNGTNGBw8eDKoJBAKaP3++YmNj1atXL2VmZqqioiKoprq6Wl6vV5ZlybIseb1e1dTUtPoCAQAAzml18Dl16pSGDh2q3NzcZo+vWLFCK1euVG5urvbu3Su3260JEybo5MmTTk12drY2bdqkvLw87dy5U3V1dcrIyNCZM2ecmqysLJWWlio/P1/5+fkqLS2V1+ttwyUCAAB8r9XL2SdNmqRJkyY1e8y2bb3wwgt6+umnNWXKFEnSm2++qfj4eL399tuaM2eO/H6/Xn/9da1bt07jx4+XJK1fv16JiYnavn270tPTdejQIeXn56u4uFgpKSmSpNdee02pqak6fPiwBg4c2NbrBQAABmvX5/iUlZXJ5/MpLS3N2RcREaHRo0erqKhIc+bMUUlJiRobG4NqPB6PkpOTVVRUpPT0dO3atUuWZTmhR5JGjBghy7JUVFTUbPAJBAIKBALO59ra2va8NABAN9HS87KOLp98hVuCztCuk5t9Pp8kKT4+Pmh/fHy8c8zn8yk8PFy9e/e+aE1cXFyT88fFxTk1F8rJyXHmA1mWpcTExMu+HgAA0L10yKoul8sV9Nm27Sb7LnRhTXP1FzvPkiVL5Pf7na28vLwNLQcAAN1ZuwYft9stSU1GZaqqqpxRILfbrYaGBlVXV1+05vjx403Of+LEiSajSedEREQoOjo6aAMAADhfuwaf/v37y+12q6CgwNnX0NCgwsJCjRw5UpI0bNgwhYWFBdVUVlbqwIEDTk1qaqr8fr/27Nnj1OzevVt+v9+pAQAAaK1WT26uq6vTX//6V+dzWVmZSktLFRMTo379+ik7O1vLli3TgAEDNGDAAC1btkw9e/ZUVlaWJMmyLM2cOVOLFi1Snz59FBMTo8WLF2vIkCHOKq9BgwZp4sSJmjVrltasWSNJmj17tjIyMljRBQAA2qzVwWffvn268847nc8LFy6UJE2fPl1r167V448/rvr6ej388MOqrq5WSkqKtm3bpqioKOd7Vq1apdDQUE2dOlX19fUaN26c1q5dq5CQEKdmw4YNWrBggbP6KzMzs8VnBwEAAFwKl23bdmc3oiPU1tbKsiz5/X7m++Cq0dIyW3RdkQ2ndWjVzyRJgx79o+rDe3Ryi9BWLGfvGjr67zfv6gIAAMYg+AAAAGMQfAAAgDEIPgAAwBgEHwAAYIx2fUkpgEvD6i0A6BwEHwAAxFvbTcGtLgAAYAyCDwAAMAa3uoAOxFweAOhaGPEBAADGIPgAAABjcKsLAICLuNgta1Z8XX0Y8QEAAMYg+AAAAGMQfAAAgDEIPgAAwBgEHwAAYAyCDwAAMAbL2YHLxNOZAeDqwYgPAAAwBsEHAAAYg+ADAACMQfABAADGIPgAAABjEHwAAIAxCD4AAMAYBB8AAGAMgg8AADAGT24GLhFPaAZwoZZ+LxxdPvkKtwSXihEfAABgDIIPAAAwBsEHAAAYg+ADAACMQfABAADGIPgAAABjsJwduADL1gFcLpa5d12M+AAAAGMQfAAAgDHaPfgsXbpULpcraHO73c5x27a1dOlSeTweRUZGasyYMTp48GDQOQKBgObPn6/Y2Fj16tVLmZmZqqioaO+mAgAAw3TIiM/NN9+syspKZ9u/f79zbMWKFVq5cqVyc3O1d+9eud1uTZgwQSdPnnRqsrOztWnTJuXl5Wnnzp2qq6tTRkaGzpw50xHNBQAAhuiQyc2hoaFBozzn2LatF154QU8//bSmTJkiSXrzzTcVHx+vt99+W3PmzJHf79frr7+udevWafz48ZKk9evXKzExUdu3b1d6enpHNBkAABigQ0Z8jhw5Io/Ho/79++uBBx7Q3/72N0lSWVmZfD6f0tLSnNqIiAiNHj1aRUVFkqSSkhI1NjYG1Xg8HiUnJzs1zQkEAqqtrQ3aAAAAztfuIz4pKSl66623dOONN+r48eP67W9/q5EjR+rgwYPy+XySpPj4+KDviY+P11dffSVJ8vl8Cg8PV+/evZvUnPv+5uTk5OjZZ59t56sBAKD9sMy987X7iM+kSZN03333aciQIRo/fry2bPn+P/Kbb77p1LhcrqDvsW27yb4L/VDNkiVL5Pf7na28vPwyrgIAAHRHHb6cvVevXhoyZIiOHDnizPu5cOSmqqrKGQVyu91qaGhQdXV1izXNiYiIUHR0dNAGAABwvg4PPoFAQIcOHVJCQoL69+8vt9utgoIC53hDQ4MKCws1cuRISdKwYcMUFhYWVFNZWakDBw44NQAAAG3R7nN8Fi9erLvuukv9+vVTVVWVfvvb36q2tlbTp0+Xy+VSdna2li1bpgEDBmjAgAFatmyZevbsqaysLEmSZVmaOXOmFi1apD59+igmJkaLFy92bp0BAAC0VbsHn4qKCj344IP6xz/+oeuuu04jRoxQcXGxkpKSJEmPP/646uvr9fDDD6u6ulopKSnatm2boqKinHOsWrVKoaGhmjp1qurr6zVu3DitXbtWISEh7d1cAABgEJdt23ZnN6Ij1NbWyrIs+f1+5vugVXhJKVojsuG0Dq36mSRp0KN/VH14j05uEa5GrOr6fx3995t3dQEAAGN0yJObAQDApbvYSDOjQe2LER8AAGAMgg8AADAGt7pgLCYxA4B5GPEBAADGYMQH3R4jOwCAcxjxAQAAxiD4AAAAYxB8AACAMQg+AADAGExuBgCgC2tpgQZPdG4bRnwAAIAxCD4AAMAY3OpCt8CzegAAl4IRHwAAYAyCDwAAMAa3ugAAuAqx2qttCD64qjCXBwBwOQg+6JIIOACAjsAcHwAAYAyCDwAAMAa3ugAA6EaY9HxxjPgAAABjEHwAAIAxuNUFAIABLrZa1qTbYIz4AAAAYzDig07F83oAAFcSwQcAAMOZtBKMW10AAMAYBB8AAGAMgg8AADAGc3zQ4ZjADADoKgg+AACgWd1x0jO3ugAAgDEY8QEAAK1yNY8EEXzQIubmAABa42oIRAQfAADQobrSe8IIPgAAoNNcGIrOBr7t0J/X5YPPyy+/rOeff16VlZW6+eab9cILL+if/umfOrtZVx1uWwEA0MWDzzvvvKPs7Gy9/PLLuv3227VmzRpNmjRJn3/+ufr169fZzeuSCDgAALSsSweflStXaubMmfq3f/s3SdILL7ygDz74QKtXr1ZOTk4nt+7KIMgAANB+umzwaWhoUElJiZ588smg/WlpaSoqKmpSHwgEFAgEnM9+v1+SVFtb27ENbUbyrz+44j8TQOc403Ba537LnAl8q7P22U5tD3C1OzfHx7btDjl/lw0+//jHP3TmzBnFx8cH7Y+Pj5fP52tSn5OTo2effbbJ/sTExA5rIwBIknXui5d/0ZnNALqVb775RpZl/XBhK3XZ4HOOy+UK+mzbdpN9krRkyRItXLjQ+VxTU6OkpCR9/fXXHdJxV7Pa2lolJiaqvLxc0dHRnd2cLoW+aRl90zL6pmX0TfPol5b5/X7169dPMTExHXL+Lht8YmNjFRIS0mR0p6qqqskokCRFREQoIiKiyX7LsvhH1YLo6Gj6pgX0Tcvom5bRNy2jb5pHv7Tsmms65q1aXfZdXeHh4Ro2bJgKCgqC9hcUFGjkyJGd1CoAAHA167IjPpK0cOFCeb1eDR8+XKmpqXr11Vf19ddf66GHHurspgEAgKtQlw4+06ZN0zfffKPf/OY3qqysVHJysrZu3aqkpKQf/N6IiAj9+te/bvb2l+nom5bRNy2jb1pG37SMvmke/dKyju4bl91R68UAAAC6mC47xwcAAKC9EXwAAIAxCD4AAMAYBB8AAGCMqzr45OTk6LbbblNUVJTi4uJ0zz336PDhw0E1tm1r6dKl8ng8ioyM1JgxY3Tw4MFOanHnycnJkcvlUnZ2trPP5L75+9//rp///Ofq06ePevbsqZ/+9KcqKSlxjpvaN999953+/d//Xf3791dkZKSuv/56/eY3v9HZs////ilT+ubjjz/WXXfdJY/HI5fLpc2bNwcdv5R+CAQCmj9/vmJjY9WrVy9lZmaqoqLiCl5Fx7hY3zQ2NuqJJ57QkCFD1KtXL3k8Hv3iF7/QsWPHgs5hYt9caM6cOXK5XHrhhReC9nfHvrmUfjl06JAyMzNlWZaioqI0YsQIff31187x9uqXqzr4FBYWau7cuSouLlZBQYG+++47paWl6dSpU07NihUrtHLlSuXm5mrv3r1yu92aMGGCTp482Yktv7L27t2rV199VbfcckvQflP7prq6WrfffrvCwsL0/vvv6/PPP9fvf/97/ehHP3JqTO2b5557Tq+88opyc3N16NAhrVixQs8//7xeeuklp8aUvjl16pSGDh2q3NzcZo9fSj9kZ2dr06ZNysvL086dO1VXV6eMjAydOXPmSl1Gh7hY33z77bf65JNP9Mwzz+iTTz7Rxo0b9eWXXyozMzOozsS+Od/mzZu1e/dueTyeJse6Y9/8UL/87//+r0aNGqWbbrpJO3bs0F/+8hc988wz6tGjh1PTbv1idyNVVVW2JLuwsNC2bds+e/as7Xa77eXLlzs1p0+fti3Lsl955ZXOauYVdfLkSXvAgAF2QUGBPXr0aPuRRx6xbdvsvnniiSfsUaNGtXjc5L6ZPHmy/ctf/jJo35QpU+yf//zntm2b2zeS7E2bNjmfL6Ufampq7LCwMDsvL8+p+fvf/25fc801dn5+/hVre0e7sG+as2fPHluS/dVXX9m2Td9UVFTYP/7xj+0DBw7YSUlJ9qpVq5xjJvRNc/0ybdo05/dMc9qzX67qEZ8L+f1+SXJebFZWViafz6e0tDSnJiIiQqNHj1ZRUVGntPFKmzt3riZPnqzx48cH7Te5b959910NHz5c999/v+Li4nTrrbfqtddec46b3DejRo3S//zP/+jLL7+UJP3lL3/Rzp079c///M+SzO6b811KP5SUlKixsTGoxuPxKDk52ai+kr7/3exyuZxRVZP75uzZs/J6vXrsscd08803NzluYt+cPXtWW7Zs0Y033qj09HTFxcUpJSUl6HZYe/ZLtwk+tm1r4cKFGjVqlJKTkyXJecHphS81jY+Pb/Ly0+4oLy9Pn3zyiXJycpocM7lv/va3v2n16tUaMGCAPvjgAz300ENasGCB3nrrLUlm980TTzyhBx98UDfddJPCwsJ06623Kjs7Ww8++KAks/vmfJfSDz6fT+Hh4erdu3eLNSY4ffq0nnzySWVlZTkv4zS5b5577jmFhoZqwYIFzR43sW+qqqpUV1en5cuXa+LEidq2bZvuvfdeTZkyRYWFhZLat1+69CsrWmPevHn67LPPtHPnzibHXC5X0Gfbtpvs627Ky8v1yCOPaNu2bUH3SC9kYt+cPXtWw4cP17JlyyRJt956qw4ePKjVq1frF7/4hVNnYt+88847Wr9+vd5++23dfPPNKi0tVXZ2tjwej6ZPn+7Umdg3zWlLP5jUV42NjXrggQd09uxZvfzyyz9Y3937pqSkRC+++KI++eSTVl9nd+6bc4sn7r77bj366KOSpJ/+9KcqKirSK6+8otGjR7f4vW3pl24x4jN//ny9++67+uijj9S3b19nv9vtlqQmabCqqqrJ/6l1NyUlJaqqqtKwYcMUGhqq0NBQFRYW6j/+4z8UGhrqXL+JfZOQkKDBgwcH7Rs0aJCzesDkfzePPfaYnnzyST3wwAMaMmSIvF6vHn30UWfU0OS+Od+l9IPb7VZDQ4Oqq6tbrOnOGhsbNXXqVJWVlamgoMAZ7ZHM7Zs///nPqqqqUr9+/Zzfy1999ZUWLVqkn/zkJ5LM7JvY2FiFhob+4O/l9uqXqzr42LatefPmaePGjfrwww/Vv3//oOP9+/eX2+1WQUGBs6+hoUGFhYUaOXLklW7uFTVu3Djt379fpaWlzjZ8+HD9y7/8i0pLS3X99dcb2ze33357k8cefPnll87Lb03+d/Ptt9/qmmuCfy2EhIQ4/0dmct+c71L6YdiwYQoLCwuqqays1IEDB7p9X50LPUeOHNH27dvVp0+foOOm9o3X69Vnn30W9HvZ4/Hoscce0wcffCDJzL4JDw/XbbfddtHfy+3aL62aCt3F/OpXv7Ity7J37NhhV1ZWOtu3337r1Cxfvty2LMveuHGjvX//fvvBBx+0ExIS7Nra2k5seec4f1WXbZvbN3v27LFDQ0Pt3/3ud/aRI0fsDRs22D179rTXr1/v1JjaN9OnT7d//OMf2//93/9tl5WV2Rs3brRjY2Ptxx9/3KkxpW9Onjxpf/rpp/ann35qS7JXrlxpf/rpp87KpEvph4ceesju27evvX37dvuTTz6xx44daw8dOtT+7rvvOuuy2sXF+qaxsdHOzMy0+/bta5eWlgb9bg4EAs45TOyb5ly4qsu2u2ff/FC/bNy40Q4LC7NfffVV+8iRI/ZLL71kh4SE2H/+85+dc7RXv1zVwUdSs9sbb7zh1Jw9e9b+9a9/bbvdbjsiIsK+44477P3793deozvRhcHH5L5577337OTkZDsiIsK+6aab7FdffTXouKl9U1tbaz/yyCN2v3797B49etjXX3+9/fTTTwf9wTKlbz766KNmf79Mnz7dtu1L64f6+np73rx5dkxMjB0ZGWlnZGTYX3/9dSdcTfu6WN+UlZW1+Lv5o48+cs5hYt80p7ng0x375lL65fXXX7dvuOEGu0ePHvbQoUPtzZs3B52jvfrFZdu23boxIgAAgKvTVT3HBwAAoDUIPgAAwBgEHwAAYAyCDwAAMAbBBwAAGIPgAwAAjEHwAQAAxiD4AAAAYxB8AACAMQg+AADAGAQfAABgDIIPAAAwxv8BJVdRj7m5bC8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "mu = 100\n",
    "si = 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": 66,
   "id": "f2ba67ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "mu = 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": 67,
   "id": "c0967ecf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "mu = 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": 68,
   "id": "cd652c9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "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": 69,
   "id": "8922bf10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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": 70,
   "id": "dce20ba3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "mu = 100\n",
    "si = 5\n",
    "N = 100000\n",
    "values1 = np.random.normal(mu, si, N)\n",
    "\n",
    "plt.hist(values1, 100)\n",
    "plt.axvline(values1.mean(), color='r')\n",
    "plt.xlim([20,350])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "41b13e16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "mu = 200\n",
    "si = 10\n",
    "N = 100000\n",
    "values2 = np.random.normal(mu, si, N)\n",
    "\n",
    "plt.hist(values2, 100)\n",
    "plt.axvline(values2.mean(), color='r')\n",
    "plt.xlim([20,350])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "07d25f6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "mu = 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": 73,
   "id": "6aaa5712",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#fff\n"
     ]
    }
   ],
   "source": [
    "#EXERCISE_1\n",
    "x = [1, 'Hello', True, 1.093E-3, '#fff']\n",
    "print(x[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "6e7be8e6",
   "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": 75,
   "id": "72b8bab0",
   "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": 76,
   "id": "b6f446d8",
   "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": [
    "\n",
    "#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": 77,
   "id": "3a47f063",
   "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": 78,
   "id": "c0a59567",
   "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": 79,
   "id": "9f7baee8",
   "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": 80,
   "id": "683202cd",
   "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": 81,
   "id": "3efc09a6",
   "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": 82,
   "id": "49cee7c2",
   "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": 83,
   "id": "2a0e4142",
   "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": [
    "\n",
    "#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": 84,
   "id": "cb1ae708",
   "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": 85,
   "id": "4fcd8ceb",
   "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": 86,
   "id": "7f8ed859",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(x1, y1, x2, y2, x3, y3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "b671f037",
   "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": 88,
   "id": "76b1ab66",
   "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": 89,
   "id": "58a4a8ca",
   "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": 90,
   "id": "67b4e0ba",
   "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": 91,
   "id": "aa0a5f98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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10EMPadSoUZo3b55XsXsj4Cu7xcXFGjNmjFq1aqXo6Gj16tVL69atcz1umqYmT56s5ORkRUdHq2/fvtqyZYsfI0agiYmw6/fdmsnuxZ92h1O6LqOFdUEBAOAncXFx1Y7akt3mzZvLbrfXqOLm5+fXqPYeN23aNPXu3VsPPfSQunTpooEDB2r27NmaP3++cnNzffJa6iLgk90RI0Zo5cqVeumll7R582YNGDBA/fr10549eyRJ06dP14wZMzRr1iytW7dOiYmJ6t+/v4qLi/0cOQLJHzNaeLwig82QMlrHqnXzKGuDAgCgjqxsY6iLiIgIde/eXStXrqx2fuXKlerVq1etzzly5Ihstuqppd1ul1RVnPSXgE52S0tLtXTpUk2fPl2/+93v1K5dO02ePFlpaWmaM2eOTNPUzJkzNXHiRA0ZMkSdOnXSokWLdOTIES1evNjf4SOApLWI1vDetb8TPRXDkCLDbXpoUIoPogIAoG6csllyuGPcuHF64YUXNH/+fG3btk1jx45Vdna2Ro0aJUmaMGGChg0b5rp+8ODBevPNNzVnzhz9/PPP+vLLL3X//ffrwgsvVHJysqW/H+4I6J7dyspKORwORUVVr6hFR0friy++UFZWlvLy8qrdKRgZGak+ffpozZo1GjlyZK3jlpWVVbvzsKiI9VNDwT2Xn6mCI5VatrFui1vbDCkizKaZN7dT25bRPo4OAIDAcuONN+rAgQOaOnWqcnNz1alTJy1fvlytWrWSJOXm5lZbc/f2229XcXGxZs2apQceeEBNmjTRZZddpieeeMJfL0FSgCe7sbGx6tmzpx599FF16NBBCQkJevXVV/Wf//xH6enprj6S2u4U3LVr10nHnTZtmqZMmeLT2BF4bIahvw5updSmUXrh81wdrXDKkPTbD1aObyuc1iJKjw1JU3pCjD/CBQDAxWEacrjRhnCyMdw1evRojR49utbHFi5cWOPcfffdp/vuu8/teXwpoNsYJOmll16SaZo688wzFRkZqX/+858aOnSoqwdEcu9OQamq7F5YWOg6cnJyfBY/AothGLr94kSteLCLJlyVqrQW1T81CLMZGtCxqebfebZeG3UuiS4AICDUd89uMAnoyq4ktW3bVqtWrVJJSYmKioqUlJSkG2+8UWlpaUpMTJQk5eXlKSkpyfWcU90pKFW1OpxqmQ0Ev5gIu67LaKHrMlroaIVTxUcrFW63qXGUXWHsHgEACDCmaZPTzR3QahsjFDWYV92oUSMlJSWpoKBAH374oa655hpXwnvinYLl5eVatWrVSe8UBH4rKtymFrERahITRqILAECQCfjK7ocffijTNHX22Wdr586deuihh3T22WfrjjvukGEYGjNmjDIzM5Wenq709HRlZmYqJiZGQ4cO9XfoAAAAlnDIkENe9ux6+fyGKuCT3cLCQk2YMEG7d+9W06ZNdd111+nxxx9XeHi4JGn8+PEqLS3V6NGjVVBQoB49emjFihWKjY31c+QAAADWcJryuufW6b+lbv3KMP25ym+AKCoqUnx8vAoLC6ttnwcAAEKbv3OE4/Pf8dkNimgc4dVY5YfLtaDv/4VcvhPwlV0AAIBQ57TgBjVvn99QkewCAAAEOKcMOb3sufX2+Q1VaKb4AAAACAlUdgEAAAKcv3ZQCwYkuwAAAAGOnl3PhearBgAAQEigsgsAABDgnDK8X2c3RG9QI9kFAAAIcKYFqzGYJLsAAAAIRE7TgspuiN6gRs8uAAAAghaVXQAAgADHagyeI9kFAAAIcLQxeC40U3wAAACEBCq7AAAAAc5pwWoMLD0GAACAgEQbg+doYwAAAEDQorILAAAQ4Kjseo5kFwAAIMCR7HqOZBcAADQ4ztICVeZvl7O8RJIpIzxGYS3ay96omb9DQ4Ah2QUAAAHPdDpUtvMTlX77hspz1slZnFfrdbbGLRV+VndFd7lOUWcPkGELjlSHyq7nguNPAAAACEqmo1wl/5mvkq/nViW4hl0yHSe93nk4X2U/rlDZDx/I1qiFYi68U417jZQRFlWPUVvPlPdLh5nWhNLgkOwCAICAVJH3vQ69eZ8q922XK1U7RaLrcuwaZ8k+Hf5sukq//T81+cMzijjrfN8F62NUdj3H0mMAACDglPxnnvbPHaTKAzvlVU3SNOUoyNaBeYN1ePUzMs1QrW+GLiq7AAAgoBxe/U8Vf/L3qi+syE2PVXqLP5km59FCxfabKMNoWFVOKrueI9kFgoRpmvrl12Jt31Og3fsPq7zSqZjIMLVOiNM5KWcooUmMv0MEgNMq+ebF/ya6vhh/zWzZopuo8cX3+mwOXyDZ9RzJLtDAOU1Tn2zarbe++kk5+w5Lkuy2//6D5nBWlUW6pDXT9Renq2ub5n6JEwBOp2LfDhV98Fefz1P8yd8V0eYSRSR39flc8D+SXaAByz90RE+/tUlbsg9Wu0f3eIJ7ou9/Oajvsr5Wv24puvuKjoqO4K8/gMBhOit16K37JDnrYTZDh968Vy1GfSQjLLIe5vMelV3PcYMa0EDtOXBYD7zwhbbtLpB0+rY257GbMj7ZlKOJi77SkbIKH0cIAHVXunGJKnO/k5x1WG3BW6ZDjoM/q2TtfN/PZRHTNCw5QhHJLtAAHSmr1F9f/FrFRyrkrKWKeypOU/o5t1DT39jAXckAAoJpmir5z/OSl+vIujmpSv4zT6ZZH5Vk+BPJLtAALVi5VQeLj7qqte5ymtKGnfv08abdFkcGAO6ryFlXfS3deuIs2quynZ/V65yecsqw5AhFJLtAA5N7sEQfrs+WmwXdWi36eJsqHVQ1APhX6fdvS/7Y1tew6+j3b9f/vB443rPr7RGKSHaBBubf63fJZtH6kIUl5Vq7/VdLxgIAT1Xs3iA5K+t/YtOh8pxv6n9e1CuSXaCB+WZ7vsftC79ltxnauHOfJWMBgCdMR4Uqft3mt/kdBbvkLDvst/nrihvUPEeyCzQg5ZUO7T5g3T/KDqepH46t5gAA/uAo3C05/bk6jCnHgZ/9OH/d0MbgORbaBBqQoiPlsnoBhYLDZdYOCABuMCtK/R2CnBVH/B3CaVlRmaWyCyDg+eKfqQa2PTyAoOP/f4QM/iEMalR2gQYkvlGkwmyGKq1YiuGYhCYxlo0FAO4yIhv7OwQZEf6P4XRMC9oQqOwCCHhhdptaJcRaNp7dZqj9mU0sGw8A3GWPP1NGeLT/AjDsCmve1n/z15EpyTS9PPz9IvyEZBdoYHp2SLKs9cDhNNXj7ERrBgMADxiGTWFJnf02f1iLdBlhUX6bH75Hsgs0MP27pVqyzq5hSElNY9QlrZkFUQGA5yLOypBs9vqf2BamiJQL6n9eD7CDmudIdoEG5ozGkbrxd+lej2Oa0shBnbgxA4DfRXf9o+R01P/EzkpFd72+/uf1AOvseo5kF2iA/nhxO7VLive4wmtIuiIjVee3a2ltYADggfCW5yg85QLJqM+0xFBYy3MUflb3epwT/kCyCzRAYXabJt/aQyktGsvmQb7b+9wkjRzUyfrAAMBDjXuOkkxnPc5oqlHPkQ3m0y02lfAcyS7QQMXFROiJO3ur//mpkiTbabJem2EozG7ojv4d9MB158tu468/gMARec4VimzfTzLqoXfXZld4q4saTAuDZMFKDMeOUMQ6u0ADFhMZpnuu7qLLup6ld7/O0ppteXIe+9fMMP77D1tUuF39uqXo6h5pSm7ayI8RA0DtDMNQ/NVPat+zl8gsK5HvFsoyJFu4mlwzU0a9tk3AX0h2gSDQIaWpOqQ0VWl5pbLyipS9r1gVlU7FRIapdUKcUlvGKtzOP+oAAps9NkFNhsxWwWu3+7QM2eSapxV2RqrPxvcFtgv2HMkuEESiI8J0bmpTnZva1N+hAIBHotr3U5Mhz+rQm/ccS3itSnqrEr34wU8qutM1Fo1Zf0h2PUeyCwAAAkp0p2tkRDbWoTfvqWppML1clsywywiPUvw1Tyv63KutCbKeOU1DhpfJKjeoAQAABIio9MvV4p7Vijy7f9UJT25cO9aTG9HmYrW4Z1WDTXThHSq7AAAgINkbt9AZN8xT2Y6PVfL18yrPWl2V9JpOnby9wZBsNsnpUHjKBWrUY4SiOlzZYJYYOxkrVlNgNQYAAIAAYxiGotr3U1T7fqo88LNKN7+p8t0bVLF3k8zSQ9WvjYxT+JldFXHm+Yrq/AeFt2jvn6B9oCrZ9bZn16JgGhiSXQAA0CCENWuj2L4PSpJM05TzyEGZFUck05QRESNbTLMGX8GF9Uh2AQBAg2MYhuyNmklq5u9Q6gWrMXiOZBcAACDAWbEIW4h2MbAaAwAAAIIXlV0AAIAARxuD50h2AQAAAh19DB6jjQEAACDQHavsenPIg8ru7NmzlZaWpqioKHXv3l2rV68+5fVlZWWaOHGiWrVqpcjISLVt21bz58/39FVbgsouAAAAaliyZInGjBmj2bNnq3fv3vrXv/6lQYMGaevWrUpNTa31OTfccIN+/fVXzZs3T+3atVN+fr4qKyvrOfLqArqyW1lZqb/85S9KS0tTdHS02rRpo6lTp8rpdLquMU1TkydPVnJysqKjo9W3b19t2bLFj1EDAABY6/gOat4e7pgxY4buuusujRgxQh06dNDMmTOVkpKiOXPm1Hr9v//9b61atUrLly9Xv3791Lp1a1144YXq1auXBb8DngvoZPeJJ57Qc889p1mzZmnbtm2aPn26nnzyST3zzDOua6ZPn64ZM2Zo1qxZWrdunRITE9W/f38VFxf7MXIAAADreNvCcOINbkVFRdWOsrKyGvOVl5dr/fr1GjBgQLXzAwYM0Jo1a2qNcdmyZcrIyND06dN15plnqn379nrwwQdVWlpq/W+IGwK6jeGrr77SNddco6uuukqS1Lp1a7366qv65ptvJFVVdWfOnKmJEydqyJAhkqRFixYpISFBixcv1siRI/0WOwAAQCBKSUmp9vWkSZM0efLkauf2798vh8OhhISEaucTEhKUl5dX67g///yzvvjiC0VFRemtt97S/v37NXr0aB08eNCvfbsBXdm9+OKL9fHHH2v79u2SpG+//VZffPGFrrzySklSVlaW8vLyqr3riIyMVJ8+fU76rkOqap7+7bsaAACAgHX8BjNvD0k5OTkqLCx0HRMmTDjptL/dftk0zZNuyex0OmUYhl555RVdeOGFuvLKKzVjxgwtXLjQr9XdgK7sPvzwwyosLNQ555wju90uh8Ohxx9/XDfffLMkud5Z1PauY9euXScdd9q0aZoyZYrvAgcAALCQJz23tY0hSXFxcYqLizvltc2bN5fdbq9Rxc3Pz6+Rdx2XlJSkM888U/Hx8a5zHTp0kGma2r17t9LT0717AR4K6MrukiVL9PLLL2vx4sXasGGDFi1apP/93//VokWLql3nzrsOSZowYUK1dzQ5OTk+iR8AAKAhioiIUPfu3bVy5cpq51euXHnSG8569+6tvXv36vDhw65z27dvl81m01lnneXTeE8loJPdhx56SI888ohuuukmde7cWbfddpvGjh2radOmSZISExMlya13HVJVq8PxdzV1eXcDAADgV6ZFhxvGjRunF154QfPnz9e2bds0duxYZWdna9SoUZKqiofDhg1zXT906FA1a9ZMd9xxh7Zu3arPP/9cDz30kO68805FR0d78eK9E9DJ7pEjR2SzVQ/Rbre7lh5LS0tTYmJitXcd5eXlWrVqld+XuQAAALCKlasx1NWNN96omTNnaurUqTrvvPP0+eefa/ny5WrVqpUkKTc3V9nZ2a7rGzdurJUrV+rQoUPKyMjQLbfcosGDB+uf//ynpb8X7gront3Bgwfr8ccfV2pqqjp27KiNGzdqxowZuvPOOyVVtS+MGTNGmZmZSk9PV3p6ujIzMxUTE6OhQ4f6OXoAAICGbfTo0Ro9enStjy1cuLDGuXPOOadG64O/BXSy+8wzz+ivf/2rRo8erfz8fCUnJ2vkyJH629/+5rpm/PjxKi0t1ejRo1VQUKAePXpoxYoVio2N9WPkAAAAFvPyBrVQZZimt/f2NXxFRUWKj49XYWEh/bsAAMDF3znC8flT/jVJtugor8Zylh5VzsgpIZfvBHRlFwAAAPLoBrNaxwhBAX2DGgAAAELL7bffrs8//9yy8Uh2AQAAAp5h0RH4iouLNWDAANfCA3v27PFqPJJdAACAQOeHdXb9ZenSpdqzZ4/uvfdevf7662rdurUGDRqkN954QxUVFW6PR7ILAACAgNKsWTP96U9/0saNG7V27Vq1a9dOt912m5KTkzV27Fjt2LGjzmO5neyapqldu3aptLTU3acCAADAEyFU2T1Rbm6uVqxYoRUrVshut+vKK6/Uli1bdO655+rpp5+u0xgeJbvp6enavXu32wEDAADAA6ZhzdEAVFRUaOnSpbr66qvVqlUrvf766xo7dqxyc3O1aNEirVixQi+99JKmTp1ap/HcXnrMZrMpPT1dBw4cUHp6utsvAAAAADiZpKQkOZ1O3XzzzVq7dq3OO++8GtcMHDhQTZo0qdN4HvXsTp8+XQ899JC+//57T54OAAAAN5imNUdD8PTTT2vv3r169tlna010JemMM85QVlZWncbzKNm99dZbtXbtWnXt2lXR0dFq2rRptQMAAAAWCqGe3U8//bTWVRdKSkp05513uj2eRzuozZw505OnAQAAAKe0aNEi/f3vf1dsbGy186WlpXrxxRc1f/58t8bzKNkdPny4J08DAACAJ6y4wSzAb1ArKiqSaZoyTVPFxcWKiopyPeZwOLR8+XK1bNnS7XE9SnYl6aefftKCBQv0008/6R//+Idatmypf//730pJSVHHjh09HRYAAFjIdFaqdF+WjuT+oIrifTIdlbKFRSgiPlExyR0U2TRFhsGy+4HOMKsOb8cIZE2aNJFhGDIMQ+3bt6/xuGEYmjJlitvjepTsrlq1SoMGDVLv3r31+eef6/HHH1fLli313Xff6YUXXtAbb7zhybAAAMACpulU8c9rlb/2/1T001cyHcf6H212GTJkypScjqpT4dFqcu7lannB9Wp0JsWqgGVFz22AJ7uffvqpTNPUZZddpqVLl1a7DywiIkKtWrVScnKy2+N6lOw+8sgjeuyxxzRu3Lhq/RSXXnqp/vGPf3gyJAAAsEDhji+V/cF0lRfskWx2V1IrSXI6auQ7zopSHdz8gQ5++55ikjoo9eo/q1Fyh3qNGZCkPn36SJKysrKUmpoqw7Cm7cKjZHfz5s1avHhxjfMtWrTQgQMHvA4KAAC4x3H0sHI+fEoHNr0rHU8STkx0T+XYdUfytuuHF4Yr8eI7lNRnhGz2cB9FC7cFec/ud999p06dOslms6mwsFCbN28+6bVdunRxa2yPkt0mTZooNzdXaWlp1c5v3LhRZ555pidDAkA1pmnqlz37tCM7V9m5+1VYfESSFB8bo9Sk5kpvlaTWyS0se+cPNGTlRfnavmiUygqO7W7q6YKqZlXSm7d6vg5nb1K7oU/LHhFjUZTwSpC3MZx33nnKy8tTy5Ytdd5558kwDJm1/Dk2DEMORx3fxB3jUbI7dOhQPfzww3r99ddlGIacTqe+/PJLPfjggxo2bJgnQwKApKok9+tvd+jDNd8qd19B1c0KkpzH/tGzGYa+2fKzTNNUUoszNLBXV13UNZ2kFyGr4vAB/bhghMoLf5VMp0WjmjqcvVE7Xr5P7W97VrbwqNM/BfBCVlaWWrRo4fq1lTxKdh9//HHdfvvtOvPMM2Waps4991w5HA4NHTpUf/nLXywNEEDoOFh4WAve/lTbf8nV8dTVNM1qxQjnCe/08/YVaOE7n2nNtz/qjmsvVdP4xvUaL+BvptOhn157QOVFv7qqshYOrpLdm7Xr/WlKu9b9O+BhsSCv7LZq1arWX1vBo7VGwsPD9corr2j79u36v//7P7388sv64Ycf9NJLL8lut1saIIDQsDf/oB7711LtzM6TVLd/k49fszM7T4/PfVN78w/6LD4gEOV//apK9nxf995cd5lOHfz2fRVu/8I346PuQmgHtUWLFun99993fT1+/Hg1adJEvXr10q5du9wez6uF9dq2bas//vGPuuGGG5Senu7NUABCWEFRiZ5a9J5Kj5bL6XT/X2On09SR0jI9teg9FRSV+CBCIPCUFezWnk+e9f1Ehk2/vDNFjjL+bqF+ZGZmKjo6WpL01VdfadasWZo+fbqaN2+usWPHuj1endsYxo0bp0cffVSNGjXSuHHjTnntjBkz3A4EQGgyTVMvLlulI6Vl1VoU3OU0TR05WqaXlq3SfbcMoocXQS//69dkOq3q0T0F06nKIwU6+N0HanHBH30/H2oX5KsxnCgnJ0ft2rWTJL399tv64x//qP/3//6fevfurb59+7o9Xp2T3Y0bN6qiosL165PhBwwAd6zf+rO2/rTbkrGcTlNbftqtDVuz1L1jG0vGBAKRo7xU+ze+Y32f7kkZyl/7mppnXMfPeT8JhR3UjmvcuLEOHDig1NRUrVixwlXNjYqKUmlpqdvj1TnZ/fTTT2v9NQB446OvNp90iRlPGIahlV99R7KLoHboh0/lrHD/h77nTB3d/4uO5G5To+Rz63FehKL+/ftrxIgR6tatm7Zv366rrrpKkrRlyxa1bt3a7fHYDBuA3+TuK1DWnnzLEl2pqi0ia0++cvcVWDYmEGhKdm+WbB4tqOQFo2pe+EcI3aD27LPPqmfPntq3b5+WLl2qZs2aSZLWr1+vm2++2e3x6vw3ZciQIXUe9M0333Q7EACh56ecX306dlKLM3w2PuBPJbu/l5yV9TupzaYje7fV75wISU2aNNGsWbNqnJ8yxbMl8Oqc7MbHx7t+bZqm3nrrLcXHxysjI0NSVbZ96NAht5JiAKEtO3e/7DabHBbfZGO3GcrO3W/pmEAgObrf2kX368Tp0JG87fU/LyRJhizo2bUkkvpx6NAhrV27Vvn5+XKe8DPCMAzddtttbo1V52R3wYIFrl8//PDDuuGGG/Tcc8+51tV1OBwaPXq04uLi3AoAQOg6fOSonJbt+PRfTtPU4dKjlo8LBALTNOWsKPPL3M7yI36ZF6Hl3Xff1S233KKSkhLFxsZWuynSk2TXo57d+fPn68EHH6y2gYTdbte4ceM0f/58T4YEEIp8WGZoSBUMwG3++gPOSgz+c3zpMW+PBuCBBx7QnXfeqeLiYh06dEgFBQWu4+BB9zcP8ijZrays1LZtNft2tm3bVq3UDACn0iS2kWyG9ffJ2gxD8bGNLB8XCASGYcgWHuOXue1RsX6ZFwqpG9T27Nmj+++/XzEx1vw59+hWzjvuuEN33nmndu7cqYsuukiS9PXXX+vvf/+77rjjDksCAxD8UpOaW96vK0kOp6lWSc0tHxcIFNGJ6SrJ3lSvcxq2MMUknVOvcyI0DRw4UN98843atLFmCUmPkt3//d//VWJiop5++mnl5uZKkpKSkjR+/Hg98MADlgQGIPi1b5Xks7HTfTg24G+Nz+xU7ysymM5KxSR1qLf58BtWVGYbSGX3qquu0kMPPaStW7eqc+fOCg8Pr/b473//e7fG8yjZtdlsGj9+vMaPH6+ioiJJ4sY0AG5rGt9YHdulaNtPu73aKvhENsPQuW3PUtP4xpaMBwSixqnn6devXvbLvPCPUNpB7e6775YkTZ06tcZjhmHI4XBv50Cvm+Xi4uJIdAF4bGCvrpYlulLVSgwDene1bDwgEMWnX6ywmHpcR9qwqdFZXRTdIq3+5kTIcjqdJz3cTXQlNyq73bp1q/N+2Bs2bHA7EACh6ey0ZPXudrbWbNru9U5qhmGo13ntdXbrZIuiAwKTYQ9Tiwv+qNzP50k+WL6vBtOplj1u9P08OLkQamM40dGjRxUVFeXVGHVOdq+99lqvJgKAk7l+YE/9lPOr8g8UelzltRmGWjaL1/UDe1ocHRCYWlxwg/K/flWOshL5NIsxbIpsmqImHS7z3Rw4vRBKdh0OhzIzM/Xcc8/p119/1fbt29WmTRv99a9/VevWrXXXXXe5NV6dk91Jkya5HSwA1EV0ZIQeGH61nlr0nn49UOh2hdcwDCU0b6Jxw65SdGSEj6IEAkt4ozOUevUEZS2d6PO50oY8Kps9/PQXAhZ4/PHHtWjRIk2fPt3VvytJnTt31tNPP+12sutVz+769ev18ssv65VXXtHGjRu9GQpAiItrHKMJd/9Bv8uoutvbZjt925TtWGvV7zI66JER1yqusX/WHgX85YyOA9Skw+WSD9arPi7x4jvUKPlcn42Pujl+g5q3R0Pw4osvau7cubrllluqbWDWpUsX/fDDD26P59FqDPn5+brpppv02WefqUmTJjJNU4WFhbr00kv12muvqUWLFp4MCyDERUWEa+iVF+uiLun66KvN2rgtS07TlM1myDi2ZZQpU06nKZthqFuHNPXr2Vltzkrwc+SAfxiGobQ/TNH2F/epZM/3lvfvNu08SMmXjrR0THjIih3QGsgOanv27FG7du1qnHc6naqoqHB7PI+S3fvuu09FRUXasmWLOnSoqsJs3bpVw4cP1/33369XX33Vk2EBQJLU5qwE/b/rE3T4yFFl7c7Xrtx9KjpcKsOQ4hrFKDWpudLOaqnGMd7dtAAEA1t4lNJvm6WfXntAxVnrLBjRkGSqWbdr1OrqP8vwYdUYbgihnt2OHTtq9erVatWqVbXzr7/+urp16+b2eB4lu//+97/10UcfuRJdSTr33HP17LPPasCAAZ4MCQA1NI6JUuf2qercPtXfoQABzR4Ro/RbZ+nXr17R3k9my5QpOd1fokmGXbaIKKVe+Yiadr6izqswAVaaNGmSbrvtNu3Zs0dOp1NvvvmmfvzxR7344ot677333B7Po7drTqezxm4WkhQeHi6nD7b+BAAAp2bY7ErsPUzn/s9rimtzkSSj7r28hiHZ7GraeaA63btUzboMItENMKHUszt48GAtWbJEy5cvl2EY+tvf/qZt27bp3XffVf/+/d0ez6PK7mWXXaY//elPevXVV5WcXLWe5Z49ezR27FhdfvnlngwJAAAsENW8tdJv+YfKCvZo//q3dOjHz3T0QHat/byGLUxRLduqacf+atbtGoU3qseNKuCeEGpjkKSBAwdq4MCBlozlUbI7a9YsXXPNNWrdurVSUlJkGIZ27dqlLl266OWX63/7QgAAUF3kGWfqzH736sx+98pZcVRHft2hiuL9Mh0VsoVFKKJJkqJbtJVh9ygVAHymTZs2WrdunZo1a1bt/KFDh3T++efr559/dms8j/6Ep6SkaMOGDVq5cqV++OEHmaapjh07UtUFACAA2cKj1Piszv4OA96wog2hgVR2f/nll1q3BS4rK9OePXvcHs+tZPc///mPDh48qEGDBkmS+vfvr71792rSpEk6cuSIrr32Wj3zzDOKjIx0OxAAAACcRAi0MSxbtsz16w8//FDx8fGurx0Ohz7++GO1bt3a7XHdSnYnT56svn37upLdzZs36+6779bw4cPVoUMHPfnkk0pOTtbkyZPdDgQAAACh69prr5VUtX708OHDqz0WHh6u1q1b66mnnnJ7XLeS3U2bNunRRx91ff3aa6/pwgsv1PPPPy+pqr1h0qRJJLsAAABWCoHK7vEVvdLS0rRu3To1b97cknHdSnYLCgqUkPDfnYpWrVqlK664wvX1BRdcoJycHEsCAwAAQBUrlg5rKEuPZWVlWTqeW8luQkKCsrKylJKSovLycm3YsEFTpkxxPV5cXFzr+rsAAABAXX388cf6+OOPlZ+fX2MPh/nz57s1llvJ7hVXXKFHHnlETzzxhN5++23FxMTokksucT3+3XffqW3btm4FAAAAABw3ZcoUTZ06VRkZGUpKSvJ6gxO3kt3HHntMQ4YMUZ8+fdS4cWMtWrRIERERrsfnz5/PdsEAAABWC4Ge3eOee+45LVy4ULfddpsl47mV7LZo0UKrV69WYWGhGjduLLvdXu3x119/XY0bN7YkMAAAAFQJpZ7d8vJy9erVy7Lx6rhpdnXx8fE1El1Jatq0abVKLwAAAOCOESNGaPHixZaNxx6BAAAADUEDqcx66+jRo5o7d64++ugjdenSpcbiBzNmzHBrPJJdAACAQBdCPbvfffedzjvvPEnS999/7/V4JLsAAAAIGJ9++qml45HsAgAABLhQuEFtyJAhp73GMAwtXbrUrXFJdgEAAAJdCLQxxMfH+2TcgE92W7durV27dtU4P3r0aD377LMyTVNTpkzR3LlzVVBQoB49eujZZ59Vx44d/RAtAAAAPLFgwQKfjOvR0mP1ad26dcrNzXUdK1eulCRdf/31kqTp06drxowZmjVrltatW6fExET1799fxcXF/gwbAADAMsfbGLw9QlHAJ7stWrRQYmKi63jvvffUtm1b9enTR6ZpaubMmZo4caKGDBmiTp06adGiRTpy5Iil67MBAAD4lWnR4abZs2crLS1NUVFR6t69u1avXl2n53355ZcKCwtzrargTwGf7J6ovLxcL7/8su68804ZhqGsrCzl5eVV26I4MjJSffr00Zo1a046TllZmYqKiqodAAAA+K8lS5ZozJgxmjhxojZu3KhLLrlEgwYNUnZ29imfV1hYqGHDhunyyy+vp0hPrUElu2+//bYOHTqk22+/XZKUl5cnSUpISKh2XUJCguux2kybNk3x8fGuIyUlxWcxAwAAeM0Pld0ZM2borrvu0ogRI9ShQwfNnDlTKSkpmjNnzimfN3LkSA0dOlQ9e/Z0b0IfaVDJ7rx58zRo0CAlJydXO28YRrWvTdOsce5EEyZMUGFhoevIycnxSbwAAABWsLJn97efbpeVldWYr7y8XOvXr6/26bkkDRgw4JSfni9YsEA//fSTJk2aZOnr90aDSXZ37dqljz76SCNGjHCdS0xMlKQaVdz8/Pwa1d4TRUZGKi4urtoBAAAQsCys7KakpFT7hHvatGk1ptu/f78cDodbn57v2LFDjzzyiF555RWFhQXOgl+BE8lpLFiwQC1bttRVV13lOpeWlqbExEStXLlS3bp1k1T1TmTVqlV64okn/BUqAABAwMrJyalW6IuMjDzptXX99NzhcGjo0KGaMmWK2rdvb12wFmgQya7T6dSCBQs0fPjwau8UDMPQmDFjlJmZqfT0dKWnpyszM1MxMTEaOnSoHyMGAACwkIWbStTlU+3mzZvLbrfX+dPz4uJiffPNN9q4caPuvfdeSVX5m2maCgsL04oVK3TZZZd5+QI80yCS3Y8++kjZ2dm68847azw2fvx4lZaWavTo0a5NJVasWKHY2Fg/RAoAAGC9+t4uOCIiQt27d9fKlSv1hz/8wXV+5cqVuuaaa2pcHxcXp82bN1c7N3v2bH3yySd64403lJaW5nHc3moQye6AAQNkmrV/hwzD0OTJkzV58uT6DQoAACCIjRs3TrfddpsyMjLUs2dPzZ07V9nZ2Ro1apSkqhv+9+zZoxdffFE2m02dOnWq9vyWLVsqKiqqxvn61iCSXQAAgJBmYRtDXd144406cOCApk6dqtzcXHXq1EnLly9Xq1atJEm5ubmnXXM3EBjmyUqmIaSoqEjx8fEqLCxkZQYAAODi7xzh+Pwd7s2UPTLKq7EcZUe1bdafQy7faTBLjwEAAADuoo0BAAAg0PmhjSFYkOwCAAAEOpJdj9HGAAAAgKBFZRcAACDAGccOb8cIRSS7AAAAgY42Bo+R7AIAAAS4+t5BLZjQswsAAICgRWUXAAAg0NHG4DGSXQAAgIYgRJNVb9HGAAAAgKBFZRcAACDAcYOa50h2AQAAAh09ux6jjQEAAABBi8ouAABAgKONwXMkuwAAAIGONgaP0cYAAACAoEVlFwAAIMDRxuA5kl0AAI6pLC9VYe5PKty7U8X7suWoKJNh2BQRE6e4xDZqktxOjVukyDD4YBT1jDYGj5HsAgBC3qG9O/XLuve1d/MqOR0VkmHIMGwyzarswDAMmU6HJCkqrrlaX3i1Urv1U0RMvD/DRigh2fUYyS4A1JNDhw7pp59+Um5urn799VdVVFTIbrerefPmSkpKUuvWrZWYmOjvMENK2eECfff+bP36w9cybDaZTmfVA6Yp03S4rjNPSBKOFu3XDx8v0vZPX9E5/W5XWo+rqfQCAYxkFwB8bNeuXfryyy+1Y8cOSZLNZpPzeFIlKTc3V5s3b5ZpmkpKSlLPnj3VqVMnGYbhr5BDQt4PX2nT2/+Qo7xUkv6b6NaFacrpqNDWD59X7pbVOv/6hxUd19xHkQL07HqDt6IA4CNlZWV67733tHDhQu3cudN13vmbpMo0TdfH5Xl5eXrzzTf10ksvqbCwsF7jDSXZG1fqmyWZqiw7ItN0I8mtxaE92/XlCw+qpCDPouiAWpgWHSGIZBcAfKCoqEhz587Vhg0bJMmVzJ7O8et27dql2bNna/fu3T6LMVTt3fqlvlv2z2Nfef/T3zSdKisp0FeL/qyykkNejwfAWiS7AGCxkpISLVy4UIcOHapzkvtbTqdTFRUVevHFF5Wbm2txhKGrtGi/vn1npuXjmk6nyooO6Lv3nvX4ew6cimGalhyhiGQXACxkmqbeeecdHTp0qEa7gidjVVZWasmSJSovL7cowtBlmqa+W/aMnJW++b00Tad+/eFr7d2y2ifjI8TRxuAxkl0AsNDmzZu1Y8cOy6p7pmmqqKhIH3/8sSXjhbIDWd9p308b3LsRzQNbP3xBTqfj9BcCqBckuwBgEafT6ZOk1DRNrVu3TkVFRZaPHUqy1r0nw+b7H3tlhwuUv32dz+dBaDm+GoO3Rygi2QUAi+zcudOnCenxm93gvqOHC/Trj//xeVVXkgzDpl/Wve/zeRBiaGPwGMkuAFjk+++/99nauKZp6ttvv/XJ2KHgYPbW6jtD+JBpOnUwe6trxzUA/kWyCwAWycnJ8emd+IcOHdLRo0d9Nn4wK9y7U4bNXm/zOSvLdfjA3nqbD8GPNgbPkewCgAXKy8t16NAhn8+Tl8fGBZ4oyv+l3iutxb/+Uq/zIcjRxuAxtgsGAAvUV8W1tLS0XuYJNpVHS+p/zvIj9T4nghfbBXuOyi4AWMBXvbr+mif4+OH3zeBHLBAIqOwCgAWio6NlGIbPd89q1KiRT8cPVhExsZJh1NtNapIUHtW43uZCCLCiDYHKLgDAU2FhYWrWrJlP5zAMQ4mJiT6dI1jFJ7at96p4fGJavc6H4MfNaZ4h2QUAi6Smpsrmw00LmjdvrvDwcJ+NH8zik9vWyxq7x4VFRCu6SUK9zQfg5Eh2AcAiXbt2ldOHCdX555/vs7GDXdPUc2Wz188bBcNmU4t23emvhrVM05ojBJHsAoBFUlJS1KJFC58kOXa7XV27drV83FARHtVYyZ371Mtau6bTqdYXXuXzeRBaWGfXcyS7AGARwzB0xRVX+OQmtb59+yo6OtrycUNJ2oVX+X6tXcOmRs3PUtPUjr6dB0CdkewCgIXatGmj7t2t+wjbZrMpKSlJvXr1smS8UBaf1E4p3fr7dkkw06kuV42mhQHWY1MJj5HsAoDFBg4cqJSUFK8THpvNpkaNGunGG2/06Y1voeTcAXcpslG8bxJew1DrC69Ws9adrR8bIc9wWnOEIv71BACLhYeH65ZbblFamudLTxmGofj4eN15552Kj4+3MLrQFh7VSBk3/Fk2m71q3V2LGIZNTZLaqUO/4ZaNCcAaJLsA4AMRERG69dZbNXDgQNnt9jpXeY9XcC+44AKNGjVKTZo08WGUoemMlHN04S2TqlZnsKLCa9gUl9RWF946VfbwKO/HA2pDG4PH2EENAHzEMAxddNFFOuecc7Ru3TqtX79eZWVlMgzDtdva8f+bpim73a5OnTrpwgsvVHJysr/DD2rN07qq953TtWHpkyo5sFceZQHHdmRLOe9ydbziboVFcAMhfMeK1RRCdTUGkl0A8LEmTZqof//+uvTSS7V3717t3btX+/btU2VlpWw2m5o2baqkpCSdddZZioqiMlhf4pPa6nejntGOVa/qpzVvyTQddVqH1DBsMk2nomKbqcvg+9SyHesfox5YsU5uiK6zS7ILAPUkLCxMqampSk1N9XcoOMYeFq5zLh+mNj2vVc7Gj/TLN8tVeujXqgcNm6v9pGr3NVMyDDVr3VmtewxWQnpGvazbC8A7JLsAgJAXEROntr2HqG3vISorOaTCvTtVvC9bjvIyGTabImLiFJfUVnEJrWUPi/B3uAhBtDF4jmQXAIATRDZqopbpGWqZnuHvUID/suIGsxBNdlmNAQAAAEGLyi4AAECAo43BcyS7AAAAgY7VGDxGGwMAAACCFpVdAACAAEcbg+dIdgEAAAIdqzF4jDYGAAAABC0quwAAAAGONgbPBXxld8+ePbr11lvVrFkzxcTE6LzzztP69etdj5umqcmTJys5OVnR0dHq27evtmzZ4seIAQAALOY0rTlCUEAnuwUFBerdu7fCw8P1wQcfaOvWrXrqqafUpEkT1zXTp0/XjBkzNGvWLK1bt06JiYnq37+/iouL/Rc4AACAlUyLjhAU0G0MTzzxhFJSUrRgwQLXudatW7t+bZqmZs6cqYkTJ2rIkCGSpEWLFikhIUGLFy/WyJEj6ztkAAAABJCAruwuW7ZMGRkZuv7669WyZUt169ZNzz//vOvxrKws5eXlacCAAa5zkZGR6tOnj9asWXPSccvKylRUVFTtAAAACFSG/tu36/Hh7xfhJwGd7P7888+aM2eO0tPT9eGHH2rUqFG6//779eKLL0qS8vLyJEkJCQnVnpeQkOB6rDbTpk1TfHy860hJSfHdiwAAAPDW8R3UvD1CUEAnu06nU+eff74yMzPVrVs3jRw5UnfffbfmzJlT7TrDqP5exTTNGudONGHCBBUWFrqOnJwcn8QPAAAA/wroZDcpKUnnnntutXMdOnRQdna2JCkxMVGSalRx8/Pza1R7TxQZGam4uLhqBwAAQKDyuoXBw6XLZs+erbS0NEVFRal79+5avXr1Sa9988031b9/f7Vo0UJxcXHq2bOnPvzwQy9etTUCOtnt3bu3fvzxx2rntm/frlatWkmS0tLSlJiYqJUrV7oeLy8v16pVq9SrV696jRUAAMBn/LAaw5IlSzRmzBhNnDhRGzdu1CWXXKJBgwa5io6/9fnnn6t///5avny51q9fr0svvVSDBw/Wxo0b3X+9Fgro1RjGjh2rXr16KTMzUzfccIPWrl2ruXPnau7cuZKq2hfGjBmjzMxMpaenKz09XZmZmYqJidHQoUP9HD0AAEDDNWPGDN11110aMWKEJGnmzJn68MMPNWfOHE2bNq3G9TNnzqz2dWZmpt555x29++676tatW32EXKuATnYvuOACvfXWW5owYYKmTp2qtLQ0zZw5U7fccovrmvHjx6u0tFSjR49WQUGBevTooRUrVig2NtaPkQMAAFjHME0ZXt5gdvz5v12FKjIyUpGRkdXOlZeXa/369XrkkUeqnR8wYMApV7w6kdPpVHFxsZo2bepF1N4L6GRXkq6++mpdffXVJ33cMAxNnjxZkydPrr+gAAAA6pPz2OHtGFKNVagmTZpUI4/av3+/HA6H2yteneipp55SSUmJbrjhBo9DtkLAJ7sAAACwTk5OTrWb839b1T2RuyteHffqq69q8uTJeuedd9SyZUvPg7UAyS4AAECAs7KNoS4rUTVv3lx2u93tFa+kqhvb7rrrLr3++uvq16+fVzFbIaBXYwAAAIDqfTWGiIgIde/evdqKV5K0cuXKU6549eqrr+r222/X4sWLddVVV9V9Qh+isgsAABDorNgBzc3njxs3TrfddpsyMjLUs2dPzZ07V9nZ2Ro1apSkqk269uzZ49rZ9tVXX9WwYcP0j3/8QxdddJGrKhwdHa34+HjvYvcCyS4AAABquPHGG3XgwAFNnTpVubm56tSpk5YvX+7a7yA3N7famrv/+te/VFlZqXvuuUf33HOP6/zw4cO1cOHC+g7fxTDNEN0o+QRFRUWKj49XYWEhu6kBAAAXf+cIx+fv0+uvCguL8mqsysqjWrXm0ZDLd6jsAgAABDo/tDEEC25QAwAAQNCisgsAABDgDGfV4e0YoYhkFwAAINDRxuAx2hgAAAAQtKjsAgAABDo3N4U46RghiGQXAAAgwFm5XXCooY0BAAAAQYvKLgAAQKDjBjWPkewCAAAEOlOSt0uHhWauS7ILAAAQ6OjZ9Rw9uwAAAAhaVHYBAAACnSkLenYtiaTBIdkFAAAIdNyg5jHaGAAAABC0qOwCAAAEOqckw4IxQhDJLgAAQIBjNQbP0cYAAACAoEVlFwAAINBxg5rHSHYBAAACHcmux2hjAAAAQNCisgsAABDoqOx6jGQXAAAg0LH0mMdIdgEAAAIcS495jp5dAAAABC0quwAAAIGOnl2PkewCAAAEOqcpGV4mq87QTHZpYwAAAEDQorILAAAQ6Ghj8BjJLgAAQMCzINlVaCa7tDEAAAAgaFHZBQAACHS0MXiMZBcAACDQOU153YbAagwAAABAcKGyCwAAEOhMZ9Xh7RghiGQXAAAg0NGz6zGSXQCAR5xOh/Kyf9K+vb9of262SooKZDqdCo+MVtOEM9UiuZWSWrVXTOM4f4cKNHz07HqMZBcA4JajRw5r6zertGXdZyo9XCTDMCQZMk/4iHRv1g9yOh0yDJtad+imzj0uU2JqO/8FDSBkkewCAOrENE39vOUbrX7/FVWUHZV57CPRqv9Xrxg5nY5jjzn1yw8blbV1vdp37ameV9ygyKiY+g4daPhoY/AYyS4A4LScDoc+e2ehdm5e6/ZzTWdVxXfHd18rZ+cWDbr1fjVPTLE6RCC4mbIg2bUkkgaHpccAAKfkdDi04v+e087v13k1jmmaOnrksJYteFL7c7Mtig4ATo1kFwBwSl+teF3Z27+z5CNQ03TKUVGh91+aqdKSYguiA0LE8TYGb48QRLILADipvb/8qC1rP7V0TNN0qvxoqb5YvtjScYGg5nRac4Qgkl0AQK2cToc+e3vhsdUWrGWaTmVt3aDs7ZstHxsATkSyCwCoVfb2zTpceNC16oLVDMPQt1+t8MnYQNChjcFjJLsAgFp9v/ZTGYbvfkyYpqncX7br0P48n80BBA2SXY+R7AIAaqisKFfuLz9W2yjCJwxD2TtoZQDgO6yzCwCo4UBejs/aF05kyGAZMqAu2C7YYyS7AIAaDvy6p17mMU2n9u39pV7mAhoy03R6/UmLzz+pCVAkuwCAGsqPHpFhs7l2P/PtXKU+nwNo8EzT+8osPbsAABzjg+XGTj4VP4oA+A6VXQBADVExjeulqitJkTGN6mUeoEEzLejZDdHKLskuAKCG5okp9TKPYbOpZXJavcwFNGhOp2R4+QY0RHt2A/qzo8mTJ8swjGpHYmKi63HTNDV58mQlJycrOjpaffv21ZYtW/wYMQAEhzNaJstms/t8HtPpVPPkVJ/PAyB0BXSyK0kdO3ZUbm6u69i8+b/rMU6fPl0zZszQrFmztG7dOiUmJqp///4qLi72Y8QA0PDZ7WFqdXZXGTbf/pgwDEOtz+7q0zmAoMCmEh4L+GQ3LCxMiYmJrqNFixaSqqq6M2fO1MSJEzVkyBB16tRJixYt0pEjR7R48WI/Rw0ADV/HC/v6tG/XMGxqfc55ahR3hs/mAIKF6XRacoSigE92d+zYoeTkZKWlpemmm27Szz//LEnKyspSXl6eBgwY4Lo2MjJSffr00Zo1a045ZllZmYqKiqodAIDqklq1V/OkVJ+tlmCaTnXpNeD0FwKAFwI62e3Ro4defPFFffjhh3r++eeVl5enXr166cCBA8rLq9pLPSEhodpzEhISXI+dzLRp0xQfH+86UlLq50YMAGhIDMNQ32vvkHywCplhGOrU43IlnNXG+sGBYEQbg8cCOtkdNGiQrrvuOnXu3Fn9+vXT+++/L0latGiR6xrjN2tBmqZZ49xvTZgwQYWFha4jJyfH+uABIAg0bZmsHpcPsXRMw2ZTXNOWuvDyay0dFwhqTtOaIwQFdLL7W40aNVLnzp21Y8cO16oMv63i5ufn16j2/lZkZKTi4uKqHQCA2nXu2U+dL+pnyViGzaaYxvG6athYhYVHWDImAJxKg0p2y8rKtG3bNiUlJSktLU2JiYlauXKl6/Hy8nKtWrVKvXr18mOUABBcDMPQRQP+qAsuu+bYMpCe/+holpCia0c8osbclAa4xzSr1sn16gjNym5Abyrx4IMPavDgwUpNTVV+fr4ee+wxFRUVafjw4TIMQ2PGjFFmZqbS09OVnp6uzMxMxcTEaOjQof4OHQCCimEY6nbJlTqrbUd9+tZ8HdqfJ8MwZNbhh+fxBDnjsmvUpWe/elm/Fwg2ptOUaXiXrNbl72swCuhkd/fu3br55pu1f/9+tWjRQhdddJG+/vprtWrVSpI0fvx4lZaWavTo0SooKFCPHj20YsUKxcbG+jlyAAhOLZJb6Y+j/qZffvxW36/9RHm7dkiqak8wZMiUKUOGnE6HpKpth8+9oK86nH8xS4wB3jCdkthBzROGGapp/gmKiooUHx+vwsJC+ncBwA1HDhdq/95s7cvdpdLDRXI6HQqPiFLTlslqntxKZ7RIopKLBs3fOcLx+S+1D1GYEe7VWJVmhT51vOnWa5k9e7aefPJJ5ebmqmPHjpo5c6YuueSSk16/atUqjRs3Tlu2bFFycrLGjx+vUaNGeRW3twK6sgsACGwxjeOV2r6zUtt39ncoQFDzRxvDkiVLNGbMGM2ePVu9e/fWv/71Lw0aNEhbt25VamrNbb6zsrJ05ZVX6u6779bLL7+sL7/8UqNHj1aLFi103XXXeRW7N6jsyv/v2gAAQGDyd45wfP6+usaSyu5neqfOr6VHjx46//zzNWfOHNe5Dh066Nprr9W0adNqXP/www9r2bJl2rZtm+vcqFGj9O233+qrr77yKnZvUNnVf9/psJMaAAA40fHcwN+1wUpVSF6GUKkKSTXzncjISEVGRlY7V15ervXr1+uRRx6pdn7AgAEn3an2q6++qrazrSQNHDhQ8+bNU0VFhcLDvUvWPUWyK6m4uFiS2EkNAADUqri4WPHx8fU+b0REhBITE/VF3nJLxmvcuHGNfGfSpEmaPHlytXP79++Xw+Fwa6favLy8Wq+vrKzU/v37lZSU5P0L8ADJrqTk5GTl5OQoNjb2tLuvoeodYUpKinJycmj7CFB8jxoGvk+Bj+9R4PP198g0TRUXFys5OdnysesiKipKWVlZKi8vt2S82naa/W1V90Tu7lRb2/W1na9PJLuSbDabzjrrLH+H0eCw+1zg43vUMPB9Cnx8jwKfL79H/qjonigqKkpRUVH1Omfz5s1lt9vd2qk2MTGx1uvDwsLUrFkzn8V6Og1qBzUAAAD4XkREhLp3715tp1pJWrly5Ul3qu3Zs2eN61esWKGMjAy/9etKJLsAAACoxbhx4/TCCy9o/vz52rZtm8aOHavs7GzXurkTJkzQsGHDXNePGjVKu3bt0rhx47Rt2zbNnz9f8+bN04MPPuivlyCJNgZ4IDIyUpMmTTpljw/8i+9Rw8D3KfDxPQp8fI9858Ybb9SBAwc0depU5ebmqlOnTlq+fLlrJ9vc3FxlZ2e7rk9LS9Py5cs1duxYPfvss0pOTtY///lPv66xK7HOLgAAAIIYbQwAAAAIWiS7AAAACFokuwAAAAhaJLsAAAAIWiS7qLNp06bpggsuUGxsrFq2bKlrr71WP/74o7/DwilMmzZNhmFozJgx/g4FJ9izZ49uvfVWNWvWTDExMTrvvPO0fv16f4eFYyorK/WXv/xFaWlpio6OVps2bTR16lQ5nU5/hxbSPv/8cw0ePFjJyckyDENvv/12tcdN09TkyZOVnJys6Oho9e3bV1u2bPFPsAgoJLuos1WrVumee+7R119/rZUrV6qyslIDBgxQSUmJv0NDLdatW6e5c+eqS5cu/g4FJygoKFDv3r0VHh6uDz74QFu3btVTTz2lJk2a+Ds0HPPEE0/oueee06xZs7Rt2zZNnz5dTz75pJ555hl/hxbSSkpK1LVrV82aNavWx6dPn64ZM2Zo1qxZWrdunRITE9W/f38VFxfXc6QINCw9Bo/t27dPLVu21KpVq/S73/3O3+HgBIcPH9b555+v2bNn67HHHtN5552nmTNn+jssSHrkkUf05ZdfavXq1f4OBSdx9dVXKyEhQfPmzXOdu+666xQTE6OXXnrJj5HhOMMw9NZbb+naa6+VVFXVTU5O1pgxY/Twww9LksrKypSQkKAnnnhCI0eO9GO08Dcqu/BYYWGhJKlp06Z+jgS/dc899+iqq65Sv379/B0KfmPZsmXKyMjQ9ddfr5YtW6pbt256/vnn/R0WTnDxxRfr448/1vbt2yVJ3377rb744gtdeeWVfo4MJ5OVlaW8vDwNGDDAdS4yMlJ9+vTRmjVr/BgZAgE7qMEjpmlq3Lhxuvjii9WpUyd/h4MTvPbaa9qwYYPWrVvn71BQi59//llz5szRuHHj9Oc//1lr167V/fffr8jIyGrbbsJ/Hn74YRUWFuqcc86R3W6Xw+HQ448/rptvvtnfoeEk8vLyJEkJCQnVzickJGjXrl3+CAkBhGQXHrn33nv13Xff6YsvvvB3KDhBTk6O/vSnP2nFihWKiorydziohdPpVEZGhjIzMyVJ3bp105YtWzRnzhyS3QCxZMkSvfzyy1q8eLE6duyoTZs2acyYMUpOTtbw4cP9HR5OwTCMal+bplnjHEIPyS7cdt9992nZsmX6/PPPddZZZ/k7HJxg/fr1ys/PV/fu3V3nHA6HPv/8c82aNUtlZWWy2+1+jBBJSUk699xzq53r0KGDli5d6qeI8FsPPfSQHnnkEd10002SpM6dO2vXrl2aNm0ayW6ASkxMlFRV4U1KSnKdz8/Pr1HtReihZxd1Zpqm7r33Xr355pv65JNPlJaW5u+Q8BuXX365Nm/erE2bNrmOjIwM3XLLLdq0aROJbgDo3bt3jSX7tm/frlatWvkpIvzWkSNHZLNV//Fot9tZeiyApaWlKTExUStXrnSdKy8v16pVq9SrVy8/RoZAQGUXdXbPPfdo8eLFeueddxQbG+vqkYqPj1d0dLSfo4MkxcbG1uihbtSokZo1a0ZvdYAYO3asevXqpczMTN1www1au3at5s6dq7lz5/o7NBwzePBgPf7440pNTVXHjh21ceNGzZgxQ3feeae/Qwtphw8f1s6dO11fZ2VladOmTWratKlSU1M1ZswYZWZmKj09Xenp6crMzFRMTIyGDh3qx6gRCFh6DHV2sr6nBQsW6Pbbb6/fYFBnffv2ZemxAPPee+9pwoQJ2rFjh9LS0jRu3Djdfffd/g4LxxQXF+uvf/2r3nrrLeXn5ys5OVk333yz/va3vykiIsLf4YWszz77TJdeemmN88OHD9fChQtlmqamTJmif/3rXyooKFCPHj307LPP8kYfJLsAAAAIXvTsAgAAIGiR7AIAACBokewCAAAgaJHsAgAAIGiR7AIAACBokewCAAAgaJHsAgAAIGiR7AIIeZ999pkMw9ChQ4ckSQsXLlSTJk38GhMAwBokuwCCwu233y7DMGQYhsLDw9WmTRs9+OCDKikpcXusG2+8Udu3b/dBlACA+hbm7wAAwCpXXHGFFixYoIqKCq1evVojRoxQSUmJ5syZ49Y40dHRio6O9lGUAID6RGUXQNCIjIxUYmKiUlJSNHToUN1yyy16++23VVZWpvvvv18tW7ZUVFSULr74Yq1bt+6k49TWxrBs2TJlZGQoKipKzZs315AhQ1yPlZeXa/z48TrzzDPVqFEj9ejRQ5999pmPXiUAwB0kuwCCVnR0tCoqKjR+/HgtXbpUixYt0oYNG9SuXTsNHDhQBw8erNM477//voYMGaKrrrpKGzdu1Mcff6yMjAzX43fccYe+/PJLvfbaa/ruu+90/fXX64orrtCOHTt89dIAAHVEGwOAoLR27VotXrxYl156qebMmaOFCxdq0KBBkqTnn39eK1eu1Lx58/TQQw+ddqzHH39cN910k6ZMmeI617VrV0nSTz/9pFdffVW7d+9WcnKyJOnBBx/Uv//9by1YsECZmZk+eHUAgLoi2QUQNN577z01btxYlZWVqqio0DXXXKP77rtPb7zxhnr37u26Ljw8XBdeeKG2bdtWp3E3bdqku+++u9bHNmzYINM01b59+2rny8rK1KxZM89fDADAEiS7AILG8SpueHi4kpOTFR4erm+//VaSZBhGtWtN06xx7mROdbOa0+mU3W7X+vXrZbfbqz3WuHFjN18BAMBq9OwCCBqNGjVSu3bt1KpVK4WHh0uS2rVrp4iICH3xxReu6yoqKvTNN9+oQ4cOdRq3S5cu+vjjj2t9rFu3bnI4HMrPz1e7du2qHYmJid6/KACAV6jsAghqjRo10v/8z//ooYceUtOmTZWamqrp06fryJEjuuuuu+o0xqRJk3T55Zerbdu2uummm1RZWakPPvhA48ePV/v27XXLLbdo2LBheuqpp9StWzft379fn3zyiTp37qwrr7zSx68QAHAqJLsAgt7f//53OZ1O3XbbbSouLlZGRoY+/PBDnXHGGXV6ft++ffX666/r0Ucf1d///nfFxcXpd7/7nevxBQsW6LHHHtMDDzygPXv2qFmzZurZsyeJLgAEAMM0TdPfQQAAAAC+QM8uAAAAghbJLgAAAIIWyS4AAACCFskuAAAAghbJLgAAAIIWyS4AAACCFskuAAAAghbJLgAAAIIWyS4AAACCFskuAAAAghbJLgAAAIIWyS4AAACC1v8HA0rG5jApL8MAAAAASUVORK5CYII=",
      "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()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "3fc850f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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