Tugas SES 6.ipynb

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    "#Latihan 1\n",
    "\n",
    "1.Jelaskan perbedaan antara median dan nilai tengah?\n",
    "\n",
    "2.Apakah nilai rata-rata (mean) dan modus selalu sama untuk dataset yang tidak diurutkan dan yang diurutkan? Mengapa?\n",
    "\n",
    "3.Jika rentang dihitung dengan mengurangkan nilai data terakhir dengan nilai data pertama, apakah dataset harus diurutkan terlebih dahulu atau tidak? Mengapa?\n",
    "\n",
    "===\n",
    "\n",
    "1.Median adalah ukuran tendensi sentral yang mewakili nilai tengah dari dataset ketika diurutkan dari yang terkecil hingga yang terbesar. Jika dataset memiliki jumlah observasi ganjil, median adalah nilai tengah. Jika dataset memiliki jumlah observasi genap, median adalah rata-rata dari dua nilai tengah.\n",
    "\n",
    "Nilai tengah adalah nilai yang berada di posisi tengah dalam dataset. Ini tidak selalu harus menjadi median. Sebagai contoh, pada dataset [4, 5, 6, 7, 8], median adalah 6, sementara nilai tengah juga 6 karena berada di posisi tengah. Namun, pada dataset [7, 8, 9, 10, 11, 12], median adalah (9 + 10) / 2 = 9,5, tetapi nilai tengah adalah 10.\n",
    "\n",
    "2.Nilai rata-rata (mean) dan modus (nilai yang paling sering muncul) mungkin atau mungkin tidak sama untuk dataset yang tidak diurutkan dan yang diurutkan.\n",
    "\n",
    "Untuk mean, pengurutan dataset tidak mengubah jumlah nilai, sehingga mean tetap sama apakah dataset diurutkan atau tidak.\n",
    "\n",
    "Untuk modus, pengurutan dataset dapat mengubah distribusi frekuensi nilai, yang dapat mempengaruhi nilai yang muncul paling sering dan dengan demikian mengubah modus. Namun, jika hanya ada satu modus (satu nilai yang muncul paling sering), itu akan tetap sama tidak peduli diurutkan atau tidak.\n",
    "\n",
    "3.Tidak masalah apakah dataset diurutkan atau tidak saat menghitung rentang. Rentang hanyalah selisih antara nilai maksimum dan minimum dalam dataset.\n",
    "\n",
    "Mengurutkan dataset tidak mengubah nilai maksimum dan minimum, sehingga apakah dataset diurutkan atau tidak, rentang tetap sama.\n",
    "\n",
    "Oleh karena itu, mengurutkan dataset tidak diperlukan dan tidak mempengaruhi perhitungan rentang."
   ]
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     "text": [
      "[47, 28, 12, 73, 64, 29, 40, 47, 49, 57, 51, 25, 16, 54, 66, 76, 63, 51, 24, 41, 51, 11, 12, 58, 57, 76, 36, 11, 17, 52, 80, 66, 56, 61, 47, 17, 48, 18, 24, 13, 18, 56, 28, 33, 49, 13, 24, 44, 50, 37, 11, 33, 57, 57, 28, 14, 48, 34, 33, 64, 57, 48, 59, 30, 72, 22, 25, 67, 52, 71, 10, 21, 10, 44, 19, 60, 39, 52, 16, 79, 41, 15, 22, 50, 78, 50, 40, 38, 61, 13, 22, 30, 20, 45, 10, 51, 37, 23, 35, 69]\n"
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Latihan 2\n",
    "\n",
    "import random as rnd\n",
    "x = [\n",
    "    rnd.randint(10, 80)\n",
    "    for i in range(0, 100)\n",
    "]\n",
    "print(x)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.hist(x, bins=25, color='yellow', edgecolor='black')\n",
    "plt.xlabel('Value')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Histogram')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "65ff2003",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#lATIHAN 3\n",
    "\n",
    "import numpy as np\n",
    "import statistics as stat\n",
    "\n",
    "N = 1000; a = 20; b = 60; x = []\n",
    "for i in range(N):\n",
    "    xi = rnd.randint(a, b)\n",
    "    x.append(xi)\n",
    "    \n",
    "plt.hist(x, bins=10)\n",
    "plt.xticks(np.arange(20, 64, 4))\n",
    "plt.xlim([20, 60])\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a9b58d4",
   "metadata": {},
   "source": [
    "n = b - a + 1 = 60 - 20 + 1 = 41\n",
    "\n",
    "Average Value = N * 1/n * the width of the bins = 1000 * 1/41 * 4 = 97,5609"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1f041dbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#lATIHAN 4\n",
    "\n",
    "N = 6000; a=1; b=6; dice = []\n",
    "for i in range(N):\n",
    "    xi = rnd.randint(a, b)\n",
    "    dice.append(xi)\n",
    "    \n",
    "plt.hist(dice, bins=6)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83d4613c",
   "metadata": {},
   "source": [
    "VALUE = 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8a03f36c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yGzduxPTp0zU5JN9r3nboPJ36luRAmZmZOHr0KPbu3WvxmIeHh9l9EWnz9UTE7HnNX+NGba6Xk5OD7Oxs5X5VVRULGVKo+VRoXV0d6urqlPscRSRn69CFvdYOyY8YMUIZSueQPLnSrFmzsG3bNhQVFaFHjx7KdKPRCAAWoyU//PCDWZv6+npUVlaatamoqFD+YBiNRnz//fcW73v+/HmLUZ4mer0eQUFBZje6MX4qUF2nQjmKSK7W7iLGliH55sPtHJInZxMRZGZmIj8/H7t370Z0dLTZ49HR0TAajdi1a5cyrb6+Hvv27VPux8fHw9vb26zNuXPncOzYMQwdOhQAkJCQAJPJhE8//VRp88knn8BkMiltiNpLbadCc3JyYDKZlFtZWVn7O0fUDu3+dJKtQ/ItDaW31IZD8mRPM2fOxMaNG/Hee+8hMDBQ2SkbDAb4+vrCw8MDWVlZWLhwIfr06YM+ffpg4cKF8PX1VY58DQYD0tPTMXv2bHTr1g0hISGYM2cOYmNjlSH6fv364YEHHsDUqVPxl7/8BQAwbdo0pKSk8JNJ1GFqOxWq1+uh1+vbfB8iR2nXSIytQ/LNh9s5JE/OtmrVKphMJiQmJiIiIkK5bdmyRWkzd+5cZGVlISMjA4MGDcLZs2dRUFBg9jrLli1Damoq0tLScPfdd8PPzw/vv/8+PD1/vmhqw4YNiI2NRVJSEpKSkhAXF4c333zTaX0l96TGU6HUeanl1K5NRUx7h+SLi4uVoXQOyZMriMgNb5MnT1baeHh4IDc3F+fOnUNtbS2Ki4tx++23m72Oj48PVqxYgQsXLuDKlSt4//33LUb8QkJCsH79euXarPXr16Nr165O6CW5I54KJWqZTaeT2jsk7+fnh8cee0xpyyF5IiLr8FQoUctsKmJWrVoFAEhMTDSbnpeXpxzRzp07FzU1NcjIyEBlZSWGDBmCnTt3IjAwUGm/bNkyeHl5IS0tDTU1NRg5ciTWrVtnMST/9NNPK1fTjxkzBitXrmxPH4mINKu9+92CggIkJCQo7bnfJXdkUxFjzYViTUPyubm5LbZpGpJfsWJFi22ahuSJiDqz9u53m3/NBPe75I74A5BERESkSSxiiIiISJNYxBAREZEmsYghIiIiTWIRQ0Sap5Yv3iLq7Jy9LbKIISIiIk1iEUNERESaxCKGiIiINIlFDBEREWkSi5g28IJBIiIidWIRQ0RERJrEIoaIiIg0iUVMB/BUExERkeuwiCEiIiJNYhFDREREmsQihoiIiDSJRQwRaQKvQSNSBzVtiyxiiIiISJNYxBAREZEmsYghIiIiTWIRQ0RERJrEIoaIiIg0iUUMERER2Y0zP73EIoaIiIg0iUUMEbkFNX13BRE5B4sYIiIi0iQWMURERKRJLGKIiIhIk1jEEBERkSaxiOkgXkxIRETkGixiWsEChYiISL1YxBCR6vGAgohuhEUMERERaRKLGCIiIrKK2kZFWcQQERGRJrGIISIiIk1iEUNERESaxCKGiIiINIlFDBEREdmVsy4AZhFjB2q7Wpuos+K2SNS5sIhpAXeGRERE6sYihoiIiDSJRQwRERFpEosYIlI1ntolUgc1bossYoiIiMjunFH0sIghIiIiTWIRQ0RuRY1D3kTkGCxi7IQ7TiL743ZFRK1RfRHzyiuvIDo6Gj4+PoiPj8fHH3/s8PfkjpPswRXZJbIHZpeaU+vfRVUXMVu2bEFWVhbmz5+Pw4cP45577sGDDz6IM2fOuHrWiFrF7LqWWne4WsDskj05elv0cuird9DSpUuRnp6OKVOmAACWL1+ODz74AKtWrcKiRYvM2tbV1aGurk65bzKZAABVVVU2vWfMgg9uOL3RsxaXL//3/3VXcK3xmkWbm595G8f+kGzT+5G6NeVHRGx6niuy6246si0CXH7MLtmLs7dFm7IrKlVXVyeenp6Sn59vNv3pp5+W4cOHW7RfsGCBAOCNN4fcysrKmF3eNHljdnnT6s2a7Kp2JOaHH35AY2MjwsPDzaaHh4ejvLzcon1OTg6ys7OV+9euXcPFixfRrVs3eHh42GWeqqqqEBUVhbKyMgQFBdnlNV2J/WmbiKC6uhqRkZFWP4fZdTz2p23MrjqxP22zKbtWl+hOdvbsWQEg+/fvN5v+pz/9Sfr27euSeTKZTAJATCaTS95//vz5Mnr0aImMjBQAMmnSpBbbfv311/LII4+IwWAQf39/GTVqlJSUlJi1aerP2rVrZcCAAaLX6yUiIkJ+85vfSHV1tV3mOS8vTwDIqVOn7PJ6rbl+/dh7WdmC2bX0z3/+UzIyMiQmJkYCAgIkLCxMRo4cKf/4xz9u2L6tdXJ9fzZt2qT5/DZfP67KL7Nr6cyZM5KamirR0dHi5+cnQUFBMnDgQFmxYoVcvXrVoj2z69zsqvbC3u7du8PT09Oi+q+oqLA4Sugsli1bhgsXLmDMmDHQ6XQttjt//jzuuecefPnll/jrX/+Kt956C7W1tUhMTMTJkyct2qenp+POO+/E3//+dyxYsADr1q3D2LFj7TLPo0ePxoEDBxAREWGX17OWo5aVNZhdS5s2bcKnn36KJ598Eu+99x7WrFkDvV6PkSNH4o033jBra8s6eeuttzBhwgTm1075ZXYtXb58GUFBQfj973+Pbdu2YfPmzRg2bBhmzZqFGTNmmLVldl2Q3Y5UYI42ePBgeeqpp8ym9evXT+bNm+eS+XH1EUFjY6Pyf39//xYr3N/+9rfi7e0tp0+fVqaZTCbp3r27pKWlKdMuXrwoAOS+++4ze/6GDRsEgOzYscO+HXCw69ePvZeVrZhdc99//73FtIaGBomLi5Nbb73VbLo166SpP0ajUZKSksyer8X8Nl8/rswvs2udtLQ08fLyktraWmUas+v87Kq6iNm8ebN4e3vL2rVr5V//+pdkZWWJv7+/Wadb03TR2eeffy6PPvqoBAUFSXBwsDzzzDNy9epVOXHihCQnJ0tAQID07NlTXnjhBbPnNx+Oq62tlQULFsgHH3wgAKSoqMjOPbZea+Ho3bu3JCcnW0yfNm2a+Pr6KkOgu3fvFgDyxhtvmLWrr6+XgIAAmTp1aqvz0NjYKP/7v/8rt912m/j4+IjBYJDY2FhZvny50saZp5Oa1s/1OxUR+ywrW3U0uyL2ze/1y6aoqMjl+W3yxBNPiE6nM5tmzTqpra2VJ554QgDIpk2bzNppMb8tZVfE+flldq0zc+ZM0el0ZsuY2TXnjOyq9sJeABg/fjwuXLiAP/7xjzh37hxiYmKwY8cO9OzZ06bXSUtLw+OPP47p06dj165dWLJkCa5evYoPP/wQGRkZmDNnDjZu3Ijf/e536N27d4vDeXq9Hrm5udizZ49V7ysiaGxstKqtl5d9VkVNTQ2+/vprPPLIIxaPxcXFoaamBv/5z39w22234csvvwQAxMfHm7Xz9vbGL37xCxw7dqzV91qyZAlyc3PxP//zPxg+fDiuXr2KEydO4NKlSzbPtz2WVdP6sZYty8pW9souYJ/82rpsAMfnt6GhAR9//DH69++vTLNlnQwZMgR5eXmIi4sza6fF/LZn/Tgqv8xu669ZXV2NnTt3Yt26dZg9e7byfGbXevbMrqqLGADIyMhARkZGh15j2rRpyhX0o0aNws6dO7Fy5Urk5+crCzExMRF/+9vfsGHDBrudkywuLsa9995rVdtTp06hV69eHX7PyspKiAhCQkIsHmuaduHCBbN/W2p7+vTpVt9r3759iI2NNQtwcnL7vidH7cuqPeyRXcB985ubm4uvvvoK7777rjKN+bWeI/PL7Fp64YUXkJOTAwDw8PDAs88+iz/96U/K48yu9eyZXdUXMfaQkpJidr9fv374/PPP8eCDDyrTvLy80Lt3b3zzzTd2e9/4+Hh89tlnVrW15WOQ1mjt443NH2upbVsfkRw8eDC2b9+OjIwMPPzww0hISGj3R+y0sqxcwR3zu2bNGjz//POYPXs2Hn74YYvHmV/rqTm/7pTdyZMnY9SoUbh48SJ2796NF198ESaTCStWrDBrx+xazx7Z7RRFTPNqT6fTwc/PDz4+PhbT7flNkwEBARg4cKBVbe11Oik4OBgeHh43rGIvXrwI4Ofl0a1bNwA/VbzNP3lw8eLFG1bJ18vJyYG/vz/Wr1+P1atXw9PTE8OHD8cLL7yAQYMG2TTfal9WruRu+c3Ly8P06dMxbdo0vPjii2aPMb/W00J+3Sm7RqMRRqMRAJCUlITg4GDMmzcPTz75JO644w5m1wb2zK5qP2KtBk0b2vVfqw389IVQ1iguLoa3t7dVt7aGD63l6+uL3r17o7S01OKx0tJS+Pr64pZbbgEAxMbGKtOv19DQgBMnTiAmJqbV9/Ly8kJ2djYOHTqEixcvYtOmTSgrK0NycjKuXLli03yrfVlpkRrzm5eXhylTpmDSpElYvXq1xdEW82s9d86vGrPb3ODBgwFAubaQ2bWePbPbKUZi2qvp3N/Ro0fRt29fZfq2bduser6rhukeeeQRLF++HGVlZYiKigIAVFdXIz8/H2PGjFGq6SFDhiAiIgLr1q3D+PHjlee/8847+PHHH206P921a1c8+uijOHv2LLKysnD69GncfvvtVj9f7ctKi9SW33Xr1mHKlCl4/PHHsWbNmhaHi5lf67lrftWW3RspKioCAPTu3VuZxuxaz27ZteozTBrV9DG/8+fPm02fNGmS+Pv7i4hIcXGxpKSkSEREhACQqKgopV1DQ4P07dtXDAaDdO3aVby9vSUiIkJ69Ohh9jG/2tpayczMlG7duomfn5889NBDNv1eibX27Nkjb7/9trz99tvi4+MjiYmJyv2KigoREVm4cKEMGDBAPDw8xNPTUwYPHiyvvvqqDB8+XAIDA+X48eNy7do1WbBggURERIi3t7cAkHHjxklRUZG8+uqr0rVrVxk5cmSbfUpJSZF58+bJO++8I8XFxfLGG29Ir169pGfPnlJfXy8iHf+Y3yuvvCKxsbESGBgogYGBctddd5l9h8L1ffHx8ZERI0bIsWPHLJbVTTfdJAEBAaLT6SQpKUnpS0VFhUREREhsbKwUFBTIjh07zJaVK7WV3+bZ7d+/v/Tv319pd/XqVenWrZt4enqKt7e3xMbGyrhx4yQ6OlrJr7Oy+9Zbb0mXLl3kl7/8pezbt08OHDhgdqutrZWFCxfKoEGDxN/fX7p06SJBQUGycuVKs3Xyr3/9y2x9/+IXvxAAMm3aNLP8RkVFtdknR+e3vdkV+Xlb37hxo3h6eoqXl5fodDqJj4+XI0eOKK+h1vy6U3afe+45mT59umzYsEH27Nkj7777rsyYMUM8PT1l3LhxIiLMrguz2+mLmB07dsj8+fNl69atFkWMiMicOXPE09NT/Pz8JCQkRPr06SPBwcFmRcyMGTPkpptukl27dsmhQ4fk3nvvlQEDBkhDQ4Nd+zNixIgWfyiraV6Sk5MlLy9PduzYIffee694eXmJh4eHJCYmKl/nvHjxYgkMDJStW7dKaWmp3HXXXUrQjEajPP3005Kent5mn1566SUZOnSodO/eXXQ6ndx8882Snp5u9n0SHS1itm3bJtu3b5eTJ0/KyZMn5dlnnxVvb29lg2nel/Hjx0tERIQMGzasxWU1cOBAs7589dVXkpqaKkFBQeLn5ycjR47s0M8O2Etb+W2e3eZ/CBYvXiz+/v4yYMAACQgIEL1eL35+fvL2228rmXFWdidNmtTqD72dOnVKye6xY8fk/fffl/DwcPHw8DBbJzda3127dpWYmBglvzExMRIREdFmnxyd3/Zmt6qqqtVt/dZbbzXrixrz607Z3bZtm4waNUrCw8PFy8tLAgICZPDgwfLnP/9Z+S4TZtd12XXrIsZWAKSgoEC5f+3aNTEajbJ48WJlWm1trRgMBlm9erWIiFy6dEm8vb1l8+bNSpuzZ89Kly5dpLCw0Gnz3pKKigoBIMXFxSLiHn0KDg6WNWvWuEVf7IXZ1UafmF1LzK42+qTW7PLC3lacOnUK5eXlSEpKUqbp9XqMGDEC+/fvBwCUlJTg6tWrZm0iIyMRExOjtHElk8kE4OcrvbXcp8bGRmzevBmXL19GQkKCpvviaO6wbJhddfbF0dxh2TC7zusLi5hWNP0IWms/S19eXg6dTofg4OAW27iKiCA7OxvDhg1TrnbXYp9KS0sREBAAvV6PGTNmoKCgALfffrsm++IsWl82zK76+uIsWl82zK5z+6LNS9edrPmnKESkzS/isaaNo2VmZuLo0aPYu3evxWNa6lPfvn1x5MgRXLp0CVu3bsWkSZNQXFysPK6lvjibVpcNs9syNawfZ9DqsmF2W+aIvnAkphVNX2zU2s/SG41G1NfXo7KyssU2rjBr1ixs27YNRUVF6NGjhzJdi33S6XTo3bs3Bg0ahEWLFmHAgAF4+eWXNdkXZ9HysmF21dkXZ9HysmF2nd8XFjGtiI6OhtFoxK5du5Rp9fX1KC4uxtChQwH89Bl7b29vszbnzp3DsWPHlDbOJCLIzMxEfn4+du/ejejoaLPHtdin5kQEdXV1btEXR9HismF2tdUXR9HismF2XdgXu14mrEHV1dVy+PBhOXz4sACQpUuXyuHDh+Wbb74RkZ8+SmYwGCQ/P19KS0tlwoQJykfJmsyYMUN69OghH374oRw6dEjuu+8+h3zUzxpPPfWUGAwG2bNnj5w7d065XblyRWmjpT7l5OTIRx99JKdOnZKjR4/Ks88+K126dJGdO3dqri/2xuyqu0/MbsuYXXX3SUvZddtrYq5du4bvvvsOgYGBrZ6D+/jjj81+pKzpF1cnTJiA1atXY8aMGbh06ZLy76BBg5Cfnw8RUX7r4w9/+AOuXbuGcePGoaamBiNGjMDGjRtx+fJlx3byBlatWgXgp1+Gvd4rr7yCX/3qVwCgqT6VlZXhV7/6FcrLyxEUFISYmBhs3boVQ4YMQVVVlcP7IiKorq5GZGQkunRxzsAls5toNp3ZZXbVvJ4BZteV2fUQEbFLr1Xm22+/Vb7KmKijysrKzM5xOxKzS/bE7JJWWZNdtx2JCQwMBPDTQmjvT5Rfr7HxMvbv/+l3I4YO/Q6env4dfk1Sv6qqKkRFRSl5cgZ7Z9fdcFu0DrNLjuaobdGW7LptEdM0lBkUFGSnIsYT/v9dP0FBQdxxdjLO/IijvbPrbrgt2obZJUdx9LZoTXb56SQiIiLSJBYxREREpEksYoiIiEiTWMQQERFRh/R7rtAl78sihoiIiDSJRYyVrq8yXVVxEhER0c9YxBAREZEmsYghIiIiTWIRQ0RERDZTw6UVLGKISLPUsBMlItdhEUNEmsLChUides3b7vT3ZBFDREREmsQihoiIiDSJRYwVXDFERkRERK1jEUNEmsaDDKLOi0VMO3HHSURE5FosYoiIiEiTWMQQERGRXTj7LAWLGCIiIrKJWi6pYBFDRJrR0o5TLTtUInIuFjFERESkSSxiiIiISJNYxLShtWFqDmETERG5DosYIiIi0iQWMURERKRJLGKIiIhIk1jEEBERkSaxiCEiIiJNYhFDREREmsQihog0gV9pQKQNztxWWcQQERGR1dR0QMEiphVqWlFERERkjkUMERERaRKLGCIiItIkFjEdxFNORERErsEihoiIiDSJRQwRERFpEosYIiIi0iQWMUTkFnh9GlHnwyKGiIiIrKK2gwUWMUSkemrbcRKROrCIoU5h0aJFuPPOOxEYGIiwsDCkpqbi5MmTZm1EBLm5uYiMjISvry8SExNx/PhxszZ1dXWYNWsWunfvDn9/f4wZMwbffvutWZvKykpMnDgRBoMBBoMBEydOxKVLlxzdRSKiTodFDHUKxcXFmDlzJg4ePIhdu3ahoaEBSUlJuHz5stJmyZIlWLp0KVauXInPPvsMRqMRqampZq+TlZWFgoICbN68GXv37sWPP/6IlJQUNDY2Km0ee+wxHDlyBIWFhSgsLMSRI0cwceJEZ3WViKjTsKmIae/R7BdffGHWhkez5GyFhYWYPHky+vfvjwEDBiAvLw9nzpxBSUkJgJ9yu3z5csyfPx9jx45FTEwMXn/9ddTU1CivYTKZsHbtWrz00ksYNWoU7rjjDqxfvx6lpaX48MMPAQDHjx9HYWEh1qxZg4SEBCQkJOC1117D3/72N4tthYiIOsamIqa9R7P3338/qqurlTY8miVXM5lMAICQkBAAwKlTp1BeXo6kpCSljV6vx913363cLykpwdWrV83aREZGIiYmBvv37wcAHDhwAAaDAUOGDFHa3HXXXTAYDEqb5urq6lBVVWV2I2rCU6FELbOpiGnv0eyVK1ewceNGADyaJdcTEWRnZ2PYsGGIiYkBAJSXlwMAwsPDzdqGhoYq/y8vL4dOp0NwcLBZm/DwcOX55eXlCAsLs3jPsLAwpU1zixYtUv5oGAwGREVFtb9znZw7XgDMU6FELevQNTHWHs2OGDFCOQp1x6NZd9xxurPMzEwcPXoUmzZtsnjMw8PD7L6ItPl6ImL2vOavcaM218vJyYHJZFJuZWVlbb4ndR48FUpa5Ky/i+0uYmw5mm1+pMqjWXKVWbNmYdu2bSgqKkKPHj2U6UajEQAs8vXDDz+Ytamvr0dlZaVZm4qKCiXzRqMR33//vcX7nj9/3mK7aKLX6xEUFGR2I2oJT4US/azdRYytR7MtHYW21IZHs2RPIoLMzEzk5+dj9+7diI6ONns8OjoaRqMRu3btUqbV19dj3759yv34+Hh4e3ubtTl37hyOHTuGoUOHAgASEhJgMpnw6aefKm0++eQTmEwmpQ1Re/FUKJE5r/Y8qelo9qOPPmrxaDYiIkKZ3vxItelo9voNqqKiQtnJt/doVq/Xt6c71AnMnDkTGzduxHvvvYfAwEBlp2wwGODr6wsPDw9kZWVh4cKF6NOnD/r06YOFCxfC19dXOfI1GAxIT0/H7Nmz0a1bN4SEhGDOnDmIjY3FqFGjAAD9+vXDAw88gKlTp+Ivf/kLAGDatGlISUlB3759XdN5chtNB4979+61eMxVp0Kzs7OV+1VVVSxk3JgaL52waSSmvUezxcXFSoHCo1lyhVWrVsFkMiExMRERERHKbcuWLUqbuXPnIisrCxkZGRg0aBDOnj2LgoICs9dZtmwZUlNTkZaWhrvvvht+fn54//334enpqbTZsGEDYmNjkZSUhKSkJMTFxeHNN990Wl/JPfFUKJElm0Zi2ns06+fnh8cee0xpq4WjWTVWnNR+1hyVenh4IDc3F7m5ucq05uf4fXx8sGLFCqxYsaLF1wkJCcH69evbPa9E1xMRzJo1CwUFBdizZ0+rB4933HEHgNZPhaalpQH4+eBxyZIlAMwPHgcPHgyAB4+kfjYVMatWrQIAJCYmmk3Py8vD5MmTAfx0NFtTU4OMjAxUVlZiyJAh2LlzJwIDA5X2y5Ytg5eXF9LS0lBTU4ORI0di3bp1FkezTz/9tHIh2pgxY7By5cr29JGISLN4KpSoZTYVMe09mm2OR7NERNZp78FjQUEBEhISlPY8eCR31K4Le4mInKWzn9rlqVDSql7ztuP04tEOfQ/+AKSddPYdLRERkbOxiCEit8IDCqLOg0UMERERaRKLGCIiItIkFjE3wOFoIiIi9WMRQ0RERJrEIoaIiIg0iUUMEakWT+0SqYNat0UWMUREROQQji5+WMQQERGRJrGIISIiIk1iEUNERESaxCKGiIiINIlFDBEREWkSixgiIiLSJBYxREREpEksYprpyGfa1fplQERERO2l5r9tLGKISJV4QEFEbWERQ0RERJrEIoaIiIg0iUUMERERaRKLGCIiInIYR16jxiKGiNwSL+4lcn8sYuyMO04iIiLnYBFzHRYgROrAbZGIrMEihoiIiDSJRQwRERFpEosYIiIicihHnSJmEUNEREQ3pPbr01jEEBERkSaxiPkvtVebRGQ7btdE7o1FjANwx0lEROR4LGKIiIhIk1jEEBERkcM54iwFixgiUhWejiUia7GIcRDuiImISMu08HeMRQwRERFpEosYInJrWjiaJKL2YRED7uSIiIi0iEUMEakGDyiI1EEr2yKLGCIiItIkFjEOpJVKlsjdcVskUgd7b4ssYohIFVhoEJGtOn0Rwx0nERHRz7T0d7HTFzFERETkPPYskjp1EeOMalNLFS2RO+O2SNQ2rW0nnbqIISJ10NqOk4g6xl7bvOqLmFdeeQXR0dHw8fFBfHw8Pv74Y7u8rjN3mtxBd06Oyi61H7dF6zC7pBWqLmK2bNmCrKwszJ8/H4cPH8Y999yDBx98EGfOnHH1rBG1itm1HgsLdWF2Oy8tbouqLmKWLl2K9PR0TJkyBf369cPy5csRFRWFVatWtfs1e83b7pIVpcVwUPs5IrvuyFXbIrfHljG7nZNWt0UvO82L3dXX16OkpATz5s0zm56UlIT9+/dbtK+rq0NdXZ1y32QyAQCqqqoAADELPujQ/DR61uLy5f/+v+4KrjVes/k1bn7mbeX/x/6Q3KH5Iedoyo+IWP0ce2fXHXVke7THtgj8vD2667bI7FJb1PB3EbDcFm3KrqjU2bNnBYDs27fPbPrzzz8vt912m0X7BQsWCADeeHPIraysjNnlTZM3Zpc3rd6sya5qR2KaeHh4mN0XEYtpAJCTk4Ps7Gzl/rVr13Dx4kV069bthu3bo6qqClFRUSgrK0NQUJBdXtOV2J+2iQiqq6sRGRlp83OZXcdhf9rG7KoT+9M2m7JrdYnuZHV1deLp6Sn5+flm059++mkZPny4S+bJZDIJADGZTC55//nz58vo0aMlMjJSAMikSZNabPv111/LI488IgaDQfz9/WXUqFFSUlJi1qapP2vXrpUBAwaIXq+XiIgI+c1vfiPV1dV2mee8vDwBIKdOnbLL67WmqT979uyRjIwMiYmJkYCAAAkLC5ORI0fKP/7xjxs+z5plZQtm19I///lPu66T6/uzadMmzef3+v7Ye1nZgtm1dObMGUlNTZXo6Gjx8/OToKAgGThwoKxYsUKuXr1q0Z7ZdW52VXthr06nQ3x8PHbt2mU2fdeuXRg6dKiL5sq1li1bhgsXLmDMmDHQ6XQttjt//jzuuecefPnll/jrX/+Kt956C7W1tUhMTMTJkyct2qenp+POO+/E3//+dyxYsADr1q3D2LFj7TLPo0ePxoEDBxAREWGX17PGO++8g08//RRPPvkk3nvvPaxZswZ6vR4jR47EG2+8YdbW1mVlDWbX0qZNmxyyTt566y1MmDDBrfLrqGVlDWbX0uXLlxEUFITf//732LZtGzZv3oxhw4Zh1qxZmDFjhllbZtcF2bVHJeYomzdvFm9vb1m7dq3861//kqysLPH395fTp0+7ZH5cfUTQ2Nio/N/f37/FkZjf/va34u3tbbacTCaTdO/eXdLS0pRpFy9eFABy3333mT1/w4YNAkB27Nhh3w44WNP6+eqrrywea2hokLi4OLn11lvNplu7rGzF7Jr7/vvvLaZ1ZJ009cdoNEpSUpLZ87WY3+vXj72Xla2YXeukpaWJl5eX1NbWKtOYXednV9VFjIjI//t//0969uwpOp1OfvnLX0pxcbHVz2266Ozzzz+XRx99VIKCgiQ4OFieeeYZuXr1qpw4cUKSk5MlICBAevbsKS+88ILZ85sPx9XW1sqCBQvkgw8+EABSVFRkx57aprUipnfv3pKcnGwxfdq0aeLr66sMge7evVsAyBtvvGHWrr6+XgICAmTq1KmtzkNjY6P87//+r9x2223i4+MjBoNBYmNjZfny5UobZ55Oalo/1+9UrvfEE0+ITqczm2btsmqPjmRXxL75vX7ZFBUVuTy/Tdq7Tmpra+WJJ54QALJp0yazdlrMb1vZFXFufpndts2cOVN0Op3ZMmZ2b8yR2VXt6aQmGRkZOH36NOrq6lBSUoLhw4fb/BppaWkYMGAAtm7diqlTp2LZsmV45plnkJqaitGjR6OgoAD33Xcffve73yE/P7/F19Hr9cjNzW31VM71RAQNDQ1W3eylpqYGX3/9NeLi4iwei4uLQ01NDf7zn/8AAL788ksAQHx8vFk7b29v/OIXv8CxY8dafa8lS5YgNzcXEyZMwPbt27Flyxakp6fj0qVLNs+3PZZV0/rR6/UWjzU0NODjjz9G//79lWm2LKv2sEd2Afvkt7Vl0xJH57cj60Sv12PIkCHK9OtpMb9trR9n55fZbfk1KysrsWXLFqxbtw6zZ8+Gl9dPn49hdl2TXdV/Oskepk2bplxBP2rUKOzcuRMrV65Efn4+HnnkEQBAYmIi/va3v2HDhg12OydZXFyMe++916q2p06dQq9evTr8npWVlRARhISEWDzWNO3ChQtm/7bU9vTp062+1759+xAbG4vc3FxlWnJy+75zw9HLKjc3F1999RXeffddZZoty8qV3DW/HV0nzK/68+tO2X3hhReQk5MD4KdPbz377LP405/+pDzO7N6Yo7PbKYqYlJQUs/v9+vXD559/jgcffFCZ5uXlhd69e+Obb76x2/vGx8fjs88+s6ptez4G2ZrWPt7Y/LGW2rb1EcnBgwdj+/btyMjIwMMPP4yEhIR2f8TOkctqzZo1eP755zF79mw8/PDDFo/bsqxcwR3za891wvyqN7/ulN3Jkydj1KhRuHjxInbv3o0XX3wRJpMJK1asMGvH7P7MGdntFEVM82pPp9PBz88PPj4+FtPt+U2TAQEBGDhwoFVtm4YkOyo4OBgeHh43rGIvXrwI4Ofl0a1bNwA/Vbzh4eEWbW9UJV8vJycH/v7+WL9+PVavXg1PT08MHz4cL7zwAgYNGmTTfDtqWeXl5WH69OmYNm0aXnzxRbPHbFlWruRu+bXXOmF+1Z9fd8qu0WiE0WgE8NM3GAcHB2PevHl48skncccddzC7zTgru6q/JsaVmja0679WGwB++OEHq55fXFwMb29vq25tDR9ay9fXF71790ZpaanFY6WlpfD19cUtt9wCAIiNjVWmX6+hoQEnTpxATExMq+/l5eWF7OxsHDp0CBcvXsSmTZtQVlaG5ORkXLlyxab5dsSyysvLw5QpUzBp0iSsXr3aorK3ZVlpkRrza891wvy6b37VmN3mBg8eDODnawuZ3Z85NbtWXf6rUU1XyJ8/f95s+qRJk8Tf319ERIqLiyUlJUUiIiIEgERFRSntDhw4IADk0UcflYiICPHx8ZERI0bIQw89ZHaFfG1trWRmZkq3bt3Ez89PHnroISkrK5Oqqir57LPPrLrV1dXZ1LeWPp20cOFCMRqNAkBCQkLk4YcflhMnTkhVVZWEhobK+PHj5dq1a7JgwQKlXXBwsBw7dkx5jU2bNgkAeeihhyz61Jbly5cLAPniiy9ExPor5FtaVr/73e/k1ltvFT8/P/Hz85OYmBh57733lOc19eX69XPs2DHJy8uTLl26yK9//Wu5cuXKDdePiMjcuXNFp9PJmTNnzOalaVm5Ulv5bZ7d/v37S//+/ZV2+/fvFwDStWtXs2UzceJEJb8tZVek5XXS3vxev06u/7qAJgsXLpRBgwaJt7e3AJCkpCQ5ceKEMi9N66Rpfev1etHpdDJs2DCz13njjTcEgAQGBtqUXRH75vdG2V2+fLmyrFrK7vXL6le/+pXMnDmzxe1Qrfl1t+zeyO9//3sBIP/85z+ZXRdmt9MXMTt27JD58+fL1q1bLYqYhoYGCQ0NFQ8PD8nKypJVq1bJLbfcIp6enmZFzIwZM+Smm26SXbt2yaFDh+Tee++VAQMGSENDg137s2fPHnn77bfl7bffFh8fH0lMTFTuV1RUiIhIcnKyvPzyyxIaGip9+vSR+Ph4CQ0NlbvvvlsCAwPl+PHjsnjxYgkMDJStW7fKwoULBYD4+fnJ9u3b5dVXX5WuXbtKjx492uxTSkqKzJs3T9555x0pLi6WN954Q3r16iU9e/aU+vp6Een4x/y2bdsm27dvl5MnT8rJkyfl2WefFW9vb2WDub4vpaWlMn78eOnatat06dJFfvnLX8q+ffvkkUcekdDQUHn55Zdl3bp1MmLECKUvFRUVEhERIbGxsVJQUCA7duyQ4cOHK8vKldrKb/PsNv9DsHDhQunSpYt0795dXnjhBRk+fLj4+flJr169lPw6K7tvvfWW2To5cOCA2a22tlaSk5MlLy9PPvroIwkNDZXAwEDp3r275OfnK+skOzvbbH0PGTJEAMgTTzwhRUVF8uqrr4pOpxO9Xt9mnxyd3/ZkNyIiQl5//XVlWaWmpppl95e//KXExsYqfVFrft0pu88995xMnz5dNmzYIHv27JF3331XZsyYIZ6enjJu3DgREWbXhdnt9EXM9ZoXMdeuXZPu3btLnz59JCgoSEJDQyUjI0P8/PyUDenSpUvi7e0tmzdvVp539uxZ6dKlixQWFtq1PyNGjGjxh7Kaf2/CV199JampqRIYGCgAJD4+XkpKSuTatWtiNBpl8eLFStum4Hl5eYnRaJTp06eLl5dXm3166aWXZOjQodK9e3fR6XRy8803S3p6utmXFzniuwqCg4NlzZo1N+xLbW2tcjTU0u3gwYNmfWlaVkFBQeLn5ycjR47s0M8O2Ist+W3+h6Bp2cyZM0eSkpKU/Op0Opk5c6YAkPfff99p2Z00aVKr66R5Pr766it58MEHBYD4+PjIyJEj5Z///OcN17evr6/cdNNNotPpJDw8XLp06SLr1q1rs0+uyG9b2TUYDHLXXXe1uqw8PDzM+qLG/LpTdrdt2yajRo2S8PBw8fLykoCAABk8eLD8+c9/vuF3mTC7zs2uWxcxtgIgBQUFyv2vv/5aAMihQ4fM2o0ZM0Z+/etfi4jIP/7xDwEgFy9eNGsTFxcnzz33nMPnuS3//ve/BYCUlpaKiLb71NDQIJs2bRKdTidffPGFpvtib8yuuvvE7LaM2VV3n9SeXV7Y24ry8nIAsLh6PDw8XHmsvLwcOp0OwcHBLbZxFRFBdnY2hg0bplwopsU+lZaWIiAgAHq9HjNmzEBBQQFuv/12TfbFWbS+bJhd9fXFWbS+bJhd5/alU3zEuqOs/Vl6W9s4WmZmJo4ePYq9e/daPKalPvXt2xdHjhzBpUuXsHXrVkyaNAnFxcXK41rqi7Npddkwuy1Tw/pxBq0uG2a3ZY7oC0diWtH0nQDNK8eKigqlCjUajaivr0dlZWWLbVxh1qxZ2LZtG4qKitCjRw9luhb7pNPp0Lt3bwwaNAiLFi3CgAED8PLLL2uyL86i5WXD7KqzL86i5WXD7Dq/LyxiWhEdHQ2j0Wj2s/T19fUoLi5WfpY+Pj4e3t7eZm3OnTuHY8eOueSn60UEmZmZyM/Px+7duxEdHW32uBb71JyIoK6uzi364ihaXDbMrrb64ihaXDbMrgv7YtcrbDSourpaDh8+LIcPHxYAsnTpUjl8+LB88803IvLTR8kMBoPk5+dLaWmpTJgwQSIiIqSqqkp5jRkzZkiPHj3kww8/lEOHDsl9993nkI/6WeOpp54Sg8Ege/bskXPnzim3K1euKG201KecnBz56KOP5NSpU3L06FF59tlnpUuXLrJz584O9aXpO3J+85vfKO1a++6DJq19N0WTixcvyuOPPy5BQUESFBQkjz/+uFRWVtp92TC76u6To7LrqvVjT8yuuvukpex2+iKm6afdm9+avkju+i+G0+v1Mnz4cOWK8yY1NTWSmZkpISEh4uvrKykpKWZf4ONMN+oLAMnLy1PaaKlPTz75pPTs2VN0Op2EhobKyJEjlQ1JpH19GTZsmERFRUlcXJxZEdPSdx803zDb+m6KBx54QGJiYmT//v2yf/9+iYmJkZSUFLsvG2ZX3X1yRHZduX7sidlVd5+0lF0PERH7ju2ow7Vr1/Ddd98hMDDQ5Rd6kXr8+OOPGD58OF566SX83//7fxEbG4vFixdDRNC3b1889dRTeOaZZwD89JXnvXv3Rk5ODubOnYvq6mqEhobizTffxPjx4wEA3333HaKiorBjxw4kJyfj+PHjuP3223Hw4EEMGTIEAHDw4EEkJCTgxIkT6Nu3b5vzyOySPYgIqqurERkZiS5dnHPlALNL9mBTdu1eFqlEWVlZq1+6wxtvttzKysqs+u6DtWvXisFgsMijwWCQv/71rzfMam1trZhMJuX2r3/9y+X95c19btZ+bb09cL/Lmz1v1mTXbT9iHRgYCAAoKytr90+UX6+x8TL27//pZ8iHDv0Onp7+HX5Ncq533nkHL730EoqKiuDj44PRo0crIzGffPIJkpKScOLECURERCjPeeqpp7Bx40YEBgZa9d0H5eXlCAsLs3jvsLCwFr8fYdGiRfjDH/5gMd1e2XU33BatU1VVhaioKGVf6Az23u+SujlqW7Qlu25bxDQNZQYFBdmpiPGE/3/XT1BQEHecGlNWVoacnBzs3LlTKTI8PT2h0+kQFBQE//+u3OZ5afrp+daGxqXZdx/cqG3zNtfLyclBdna2cr9pA7ZXdt0Nt0XbOPO0jr33u6Rujt4WrckuP2JNnUJJSQkqKioQHx8PLy8veHl5obi4GH/+85/h5eWlfHdB89GSH374Qfm/Nd99YDQa8f3331u8//nz51v8fgS9Xq/s9LnzJyKyHosY6hRGjhyJ0tJSHDlyRLkNGjQIv/rVr3DkyBHccsstN/zug3379in3rfnug4SEBJhMJnz66adKm08++QQmk0kV3/VARORO3PZ0kiP1e64QXz7//7l6NsgGgYGByu+YNPH390e3bt2U6VlZWVi4cCH69OmDPn36YOHChfD19YXJZAIAGAwGpKenY/bs2ejWrRtCQkIwZ84cxMbGYtSoUQCAfv364YEHHsDUqVPxl7/8BQAwbdo0pKSkWPXJJCIirej3XCFevd+188Aihui/5s6di5qaGmRkZKCyshJDhgxBQUEBEhISlDbLli2Dl5cX0tLSUFNTg5EjR2LdunXw9PRU2mzYsAFPP/00kpKSAABjxozBypUrnd4fIiJ3xyKGOq09e/aY3ffw8EBubi5yc3OVaVVVVWZtfHx8sGLFCqxYsaLF1w0JCcH69evtOatERKrXa952nF482qnvyWtiiEhT+j1XeMP/E1HnwyKGiDSt17ztrp4Fok6v6YDC2dsjixgi0gwWLER0PRYxVuKwNRERkbqwiGknHhESqQe3RyLn6TVvu2q2ORYxREREpEksYtqglmqTiIhIC5z5d5NFjBVaWyEscoico61tjdsiUefDIqYDuNMkcg5ua0R0IyxiiIiIyK6cdeDBIqYVPPojIiJSLxYxROQ2eOBB5Fhq28ZYxBAREZEmsYhpgS3VptoqUyJ3Yuv2xe2RSB2csS2yiCEiIiJNYhFzAzySIyIiMqfGv40sYuxEjSuXiIjInbGIISIiolap9UCdRQwRERFpEosYIiIi0iQWMUSkWu0dwlbr0DcR2ReLGCJSJRYiRNQWFjFERESkSSximuHRH5F74LZM5P5YxNgRd5pERORuOvK3zdF/F1nEEBERkSaxiCEiIiJNYhFDREREmsQixs54XQwREdHPHPl3kUXMdViAELkXbtNE7o1FDBGpDosPInVQ+7bIIsYB1L7SiYiI3AGLGCIiInIoRx3cs4ghIiIiC1o4q8Ai5r+0sLKIiIjoZyxiiMit8QCFyH2xiCEiIiJNYhFDRKRiixYtwp133onAwECEhYUhNTUVJ0+eNGsjIsjNzUVkZCR8fX2RmJiI48ePm7Wpq6vDrFmz0L17d/j7+2PMmDH49ttvzdpUVlZi4sSJMBgMMBgMmDhxIi5duuToLhK1G4sYB+EQNhHZQ3FxMWbOnImDBw9i165daGhoQFJSEi5fvqy0WbJkCZYuXYqVK1fis88+g9FoRGpqqtnrZGVloaCgAJs3b8bevXvx448/IiUlBY2NjUqbxx57DEeOHEFhYSEKCwtx5MgRTJw40VldJRXRyt8wL1fPABHR9bSy83SWwsJCs/t5eXkICwtDSUkJhg8fDhHB8uXLMX/+fIwdOxYA8PrrryM8PFx5jslkwtq1a/Hmm29i1KhRAID169cjKioKH374IZKTk3H8+HEUFhbi4MGDGDJkCADgtddeQ0JCAk6ePIm+ffs6qcdE1rNpJKa9w5pffPGFWRsOaxKRM7lTYWQymQAAISEhAIBTp06hvLwcSUlJShu9Xo+7775buV9SUoKrV6+atYmMjERMTAz2798PADhw4AAMBoNSwADAXXfdBYPBoLRprq6uDlVVVWY3opY4Yju0qYhp77Dm/fffj+rqaqWN2oY13WkHR0TuS0SQnZ2NYcOGISYmBgBQXl4OAGYjLwAQGhqq/L+8vBw6nQ7BwcFmbcLDw5Xnl5eXIywszOI9w8LClDbNLVq0SDnQNBgMiIqKan/niNrBptNJHRnW3LhxI6ZPn85hTSJqEQ8oWpeZmYmjR49i7969Fo95eHiY3ReRNl9PRMye1/w1btTmejk5OcjOzlbuV1VVsZAhp+rQhb3WDmuOGDFCGY7ksCa5Aj/hQVo3a9YsbNu2DUVFRejRo4cy3Wg0AoDFaMkPP/xg1qa+vh6VlZVmbSoqKpQRHKPRiO+//97ifc+fP28xytNEr9cjKCjI7EbkTO0uYmwZ1mw+ZKmmYU1HHvnxqFI9+AkP0ioRQWZmJvLz87F7925ER0ebPR4dHQ2j0Yhdu3Yp0+rr67Fv3z7lfnx8PLy9vc3anDt3DseOHcPQoUMBAAkJCTCZTPj000+VNp988glMJpPShqij7P13sd2fTrJ1WLOl4ciW2nBYk+xJzZ/wqKurQ11dnXK/s44isui/sZkzZ2Ljxo147733EBgYqBzIGQwG+Pr6wsPDA1lZWVi4cCH69OmDPn36YOHChfD19VVGyw0GA9LT0zF79mx069YNISEhmDNnDmJjY5Us9+vXDw888ACmTp2Kv/zlLwCAadOmISUlhafwSbXaNRJj67Bm8yFLDmuSq6npEx68OJJas2rVKphMJiQmJiIiIkK5bdmyRWkzd+5cZGVlISMjA4MGDcLZs2dRUFBg9jrLli1Damoq0tLScPfdd8PPzw/vv/8+PD09lTYbNmxAbGwskpKSkJSUhLi4OLz55ptO6yuRrWwqYto7rFlcXKwMR3JYk1xNbZ/wyMnJgclkUm5lZWXt7xy5HRG54W3y5MlKGw8PD+Tm5uLcuXOora1FcXExbr/9drPX8fHxwYoVK3DhwgVcuXIF77//vkXBHBISgvXr1yvXFa5fvx5du3Z1Qi9JTRw9KmrP17fpdFJ7hzX9/Pzw2GOPKW3VMqzpjOHrXvO24/Ti0Q5/H7Ke2j7hodfrodfr23wf6hhui0Tux6YiZtWqVQCAxMREs+l5eXnKUcHcuXNRU1ODjIwMVFZWYsiQIdi5cycCAwOV9suWLYOXlxfS0tJQU1ODkSNHYt26dRbDmk8//bQydD9mzBisXLmyPX0kUjSdCv3oo49aPBUaERGhTG/pEx7Xj8ZUVFQoI4TtORVKRETtY1MRY81RadOwZm5ubottmoY1V6xY0WKbpmFNInsQEcyaNQsFBQXYs2dPq6dC77jjDgCtf8IjLS0NwM+nQpcsWQLA/FTo4MGDAfBUKBGRo/C3k6hT4Cc8iIjcD4sY6hTaeyq0oKAACQkJSnueCiUiUg8WMdQptPdUaPPvbOGpUMfgd8QQdS72utC+Qz87QEREROQqLGKcgEeZROrAbZHIvbCIISIiIk1iEUNEREQAnDtaaY/36rRFDIeVidSB2yIRtVenLWKcjTtqIiJSMy3+nWIRQ0RERJrEIoaIiIg0iUUMERERaRKLGCJyGS2egyci++noPqBTFjHccRJ1Xtz+idxHpyxiiIiI6GdaLe5ZxBBRp6PVHTYRmWMRQ0RERJrEIsaJePRHRERqo+W/TZ2uiNHyyiJyJ9wWiaijOl0RQ0RERO6BRYyT8eiTiNsBEf2sI/sDFjFE1CmxkCLSvk5VxHCnRURE9DOt/13sVEUMERERuQ8WMS6g9cqXqCOYfyJqrr37hU5TxHDHSUTNcb9AnZk75L/TFDFERESkXu0pqljEEBERkSaxiCEiIupk3OFUEsAixmXcJUBEWsdtkUi7OkURw50UkTpwWyQie+oURQwRERGpn60HOm5fxMQs+MDVs9AiHpVSZ8K8E5G9uX0RQ0TUFhZYROphy+ADixgicjgtFAlamEcie3CnrLOIcTF3ChMREZEzsYghIiLqJNztwJlFjAq4W6iIrsd8E5GjsIghIvovFlzkztwx3yxiVMIdw0XEXBORI7GIISK6DgsvIu1gEaMi3HmSO2GeidSh17ztbrs9soghImrGXXf4RO6GRYzKcOdJ7sAdcuwOfSBy9xyziFEhdw8dkVZwWyRSN9UXMa+88gqio6Ph4+OD+Ph4fPzxx66eJafgzlP7OmN23fnce2fSGbPrjjrDtqjqImbLli3IysrC/PnzcfjwYdxzzz148MEHcebMGVfPmlPwD4J2dcbsumtWO9t22Bmz6246U2ZVXcQsXboU6enpmDJlCvr164fly5cjKioKq1atcvWsOVVnCaM76UzZ7Sw7zM7QR6BzZdcddZacNvFy9Qy0pL6+HiUlJZg3b57Z9KSkJOzfv9+ifV1dHerq6pT7JpMJAHCt7opd5qfRsxaXL//3/3VXcK3xml1e11o3P/O28v9jf0h26nt3ZlVVVQAAEbH6OfbKbtN7q03Mgg9c+v6u3Bav3w4BdW+LzG7n4apt0lHbYtPfbauyKyp19uxZASD79u0zm/7888/LbbfdZtF+wYIFAoA33hxyKysrY3Z50+SN2eVNqzdrsqvakZgmHh4eZvdFxGIaAOTk5CA7O1u5f+3aNVy8eBHdunW7Yfv2qKqqQlRUFMrKyhAUFGSX13Ql9qdtIoLq6mpERkba/Fxm13HYn7Yxu+rE/rTNluyqtojp3r07PD09UV5ebja9oqIC4eHhFu31ej30er3ZtK5duzpk3oKCgtwifE3Yn9YZDAab2jO7zsP+tI7ZVS/2p3XWZle1F/bqdDrEx8dj165dZtN37dqFoUOHumiuiNrG7JJWMbukNaodiQGA7OxsTJw4EYMGDUJCQgJeffVVnDlzBjNmzHD1rBG1itklrWJ2SUtUXcSMHz8eFy5cwB//+EecO3cOMTEx2LFjB3r27OmS+dHr9ViwYIHF8KlWsT+Ow+w6FvvjOMyuY7E/9uUhYsPn74iIiIhUQrXXxBARERG1hkUMERERaRKLGCIiItIkFjFERESkSSxiiIiISJM6fRHz0Ucf4aGHHkJkZCQ8PDzw7rvvmj0uIsjNzUVkZCR8fX2RmJiIL774wqxNXV0dZs2ahe7du8Pf3x9jxozBt99+68Re/GzRokW48847ERgYiLCwMKSmpuLkyZNmbbTUp1WrViEuLk75NsiEhAT8/e9/Vx7XUl/sjdlVd5+Y3ZYxu+ruk6aya/Uvg7mpHTt2yPz582Xr1q0CQAoKCsweX7x4sQQGBsrWrVultLRUxo8fLxEREVJVVaW0mTFjhtx0002ya9cuOXTokNx7770yYMAAaWhocHJvRJKTkyUvL0+OHTsmR44ckdGjR8vNN98sP/74oyb7tG3bNtm+fbucPHlSTp48Kc8++6x4e3vLsWPHNNcXe2N21d0nZrdlzK66+6Sl7Hb6IuZ6zTema9euidFolMWLFyvTamtrxWAwyOrVq0VE5NKlS+Lt7S2bN29W2pw9e1a6dOkihYWFTpv3llRUVAgAKS4uFhH36FNwcLCsWbPGLfpiL8yuNvrE7FpidrXRJ7Vmt9OfTmrNqVOnUF5ejqSkJGWaXq/HiBEjsH//fgBASUkJrl69atYmMjISMTExShtXMplMAICQkBAA2u5TY2MjNm/ejMuXLyMhIUHTfXE0d1g2zK46++Jo7rBsmF3n9YVFTCuafsm1+a+3hoeHK4+Vl5dDp9MhODi4xTauIiLIzs7GsGHDEBMTA0CbfSotLUVAQAD0ej1mzJiBgoIC3H777Zrsi7Nofdkwu+rri7Nofdkwu87ti6p/O0ktPDw8zO6LiMW05qxp42iZmZk4evQo9u7da/GYlvrUt29fHDlyBJcuXcLWrVsxadIkFBcXK49rqS/OptVlw+y2TA3rxxm0umyY3ZY5oi8ciWmF0WgEAIvKsaKiQqlCjUYj6uvrUVlZ2WIbV5g1axa2bduGoqIi9OjRQ5muxT7pdDr07t0bgwYNwqJFizBgwAC8/PLLmuyLs2h52TC76uyLs2h52TC7zu8Li5hWREdHw2g0YteuXcq0+vp6FBcXY+jQoQCA+Ph4eHt7m7U5d+4cjh07prRxJhFBZmYm8vPzsXv3bkRHR5s9rsU+NSciqKurc4u+OIoWlw2zq62+OIoWlw2z68K+2PUyYQ2qrq6Ww4cPy+HDhwWALF26VA4fPizffPONiPz0UTKDwSD5+flSWloqEyZMuOFHyXr06CEffvihHDp0SO677z6XfdTvqaeeEoPBIHv27JFz584ptytXrihttNSnnJwc+eijj+TUqVNy9OhRefbZZ6VLly6yc+dOzfXF3phddfeJ2W0Zs6vuPmkpu52+iCkqKhIAFrdJkyaJyE8fjVuwYIEYjUbR6/UyfPhwKS0tNXuNmpoayczMlJCQEPH19ZWUlBQ5c+aMC3ojN+wLAMnLy1PaaKlPTz75pPTs2VN0Op2EhobKyJEjlQ1JRFt9sTdmV919YnZbxuyqu09ayq6HiIh9x3aIiIiIHI/XxBAREZEmsYghIiIiTWIRQ0RERJrEIoaIiIg0iUUMERERaRKLGCIiItIkFjFERESkSSxiiIiISJNYxBAREZEmsYghIiIiTWIRQ0RERJr0/wNuAbv6rZ/I4gAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mu = 100\n",
    "si = 5\n",
    "N = 100000\n",
    "values = np.random.normal(mu, si, N)\n",
    "\n",
    "plt.subplot(3,3,1)\n",
    "plt.hist(values, 100)\n",
    "plt.axvline(values.mean(), color='y')\n",
    "plt.xlim([60,340])\n",
    "plt.title(\"mu = 100 si = 5\")\n",
    "\n",
    "mu = 200\n",
    "si = 5\n",
    "N = 100000\n",
    "values = np.random.normal(mu, si, N)\n",
    "\n",
    "plt.subplot(3,3,2)\n",
    "plt.hist(values, 100)\n",
    "plt.axvline(values.mean(), color='y')\n",
    "plt.xlim([60,340])\n",
    "plt.title(\"mu = 200 si = 5\")\n",
    "\n",
    "mu = 300\n",
    "si = 5\n",
    "N = 100000\n",
    "values = np.random.normal(mu, si, N)\n",
    "\n",
    "plt.subplot(3,3,3)\n",
    "plt.hist(values, 100)\n",
    "plt.axvline(values.mean(), color='y')\n",
    "plt.xlim([60,340])\n",
    "plt.title(\"mu = 300 si = 5\")\n",
    "\n",
    "mu = 100\n",
    "si = 10\n",
    "N = 100000\n",
    "values = np.random.normal(mu, si, N)\n",
    "\n",
    "plt.subplot(3,3,4)\n",
    "plt.hist(values, 100)\n",
    "plt.axvline(values.mean(), color='y')\n",
    "plt.xlim([60,340])\n",
    "plt.title(\"mu = 100 si = 10\")\n",
    "\n",
    "mu = 200\n",
    "si = 10\n",
    "N = 100000\n",
    "values = np.random.normal(mu, si, N)\n",
    "\n",
    "plt.subplot(3,3,5)\n",
    "plt.hist(values, 100)\n",
    "plt.axvline(values.mean(), color='y')\n",
    "plt.xlim([60,340])\n",
    "plt.title(\"mu = 200 si = 10\")\n",
    "\n",
    "mu = 300\n",
    "si = 10\n",
    "N = 100000\n",
    "values = np.random.normal(mu, si, N)\n",
    "\n",
    "plt.subplot(3,3,6)\n",
    "plt.hist(values, 100)\n",
    "plt.axvline(values.mean(), color='y')\n",
    "plt.xlim([60,340])\n",
    "plt.title(\"mu = 300 si = 10\")\n",
    "\n",
    "mu = 100\n",
    "si = 20\n",
    "N = 100000\n",
    "values = np.random.normal(mu, si, N)\n",
    "\n",
    "plt.subplot(3,3,7)\n",
    "plt.hist(values, 100)\n",
    "plt.axvline(values.mean(), color='y')\n",
    "plt.xlim([60,340])\n",
    "plt.title(\"mu = 100 si = 20\")\n",
    "\n",
    "mu = 200\n",
    "si = 20\n",
    "N = 100000\n",
    "values = np.random.normal(mu, si, N)\n",
    "\n",
    "plt.subplot(3,3,8)\n",
    "plt.hist(values, 100)\n",
    "plt.axvline(values.mean(), color='y')\n",
    "plt.xlim([60,340])\n",
    "plt.title(\"mu = 200 si = 20\")\n",
    "\n",
    "mu = 300\n",
    "si = 20\n",
    "N = 100000\n",
    "values = np.random.normal(mu, si, N)\n",
    "\n",
    "plt.subplot(3,3,9)\n",
    "plt.hist(values, 100)\n",
    "plt.axvline(values.mean(), color='y')\n",
    "plt.xlim([60,340])\n",
    "plt.title(\"mu = 300 si = 20\")\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}