NerdyVandheka/Issues 6 Nerdy.ipynb

## Issues 6 Nerdy.ipynb
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   "source": [
    "#Exercise 1\n",
    "\n",
    "1. Dalam statistik, median mewakili nilai tengah dalam dataset yang telah diurutkan. \n",
    "Ini membagi data menjadi dua bagian yang sama, dengan setengah nilai lebih kecil dari median dan setengah lainnya lebih besar.\n",
    "Sedangkan, middle value mungkin hanya mengacu pada nilai aritmatika pusat dalam dataset yang tidak di urutkan. \n",
    "Namun, ini tidak selalu sama dengan median, terutama ketika dataset memiliki jumlah elemen genap. \n",
    "Kita dapat menemukan nilai tengah dengan mengindeks data yang diurutkan pada posisi tengah. \n",
    "Namun, ini hanya berfungsi jika ada jumlah elemen ganjil.\n",
    "2. Tidak, nilai mean dan mode values tidak selalu sama untuk dataset yang tidak diurutkan dan diurutkan. \n",
    "Hal ini disebabkan karena mean mewakili rata-rata dari semua nilai dalam dataset.\n",
    "Ini dihitung dengan menjumlahkan semua nilai dan membaginya dengan jumlah total nilai. \n",
    "Pada mean, Mengurutkan data tidak memengaruhi jumlah nilai, sehingga mean tetap sama apa pun urutan datanya. \n",
    "Sedangkan mode values sendiri mewakili nilai paling sering dalam data set, pada mode values mengurutkan data dapat mengubah\n",
    "frekuensi setiap nilai. Misalnya, jika nilai paling sering awalnya muncul beberapa kali di posisi berbeda, pengurutan mungkin\n",
    "mengelompokkannya bersama, membuatnya tampak lebih sering.\n",
    "3. Jika kita menghitung range hanya sebagai selisih antara titik data terakhir dan titik data pertama dalam dataset, \n",
    "maka pengurutan tidak lah diperlukan karena urutan data tidak memengaruhi elemen pertama atau terakhir. \n",
    "Apapun pengurutannya, elemen pertama akan tetap menjadi yang pertama dan elemen terakhir akan tetap menjadi yang terakhir.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "7756491b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{10, 11, 12, 13, 15, 16, 17, 18, 19, 20, 21, 23, 24, 25, 26, 27, 28, 29, 31, 33, 35, 36, 37, 38, 39, 40, 41, 44, 45, 47, 48, 49, 50, 51, 53, 54, 55, 56, 57, 58, 59, 60, 61, 63, 65, 66, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80}\n"
     ]
    }
   ],
   "source": [
    "#Exercise 2\n",
    "import random as rnd\n",
    "x = {\n",
    "    rnd.randint(10,80)\n",
    "    for i in range(0,100)\n",
    "}\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "4226c699",
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Exercise 2\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.hist(x, bins=10 , range=[0,100])\n",
    "plt.yticks(range(0, 11,1))\n",
    "plt.xticks(range(0, 100,10))\n",
    "plt.grid()\n",
    "plt.show()\n",
    "\n",
    "#Nilai bins yang harus digunakan ialah 10 karena pada nilai bins ini grafik terlihat jelas dan \n",
    "#tidak ada kekosongan pada tengah tengah histogram.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "f2cade74",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Exercise 3\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "N = 1000; a = 20; b = 60; x = []\n",
    "for i in range(N):\n",
    "    xi = rnd.randint(a,b)\n",
    "    x.append(xi)\n",
    "plt.hist(x, bins=10)\n",
    "plt.xticks(range(20, 64,4))\n",
    "plt.xlim([20,60])\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "4983cceb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Exercise 3\n",
    "1. N mewakili jumlah titik data yang akan dihasilkan untuk dataset. \n",
    "Kode ini akan membuat dataset dengan 1000 titik data.\n",
    "2. Rata-rata nilai yang sekitar 100 bisa ditemukan dari rumus 1/n karena :\n",
    "n = b-a+1 = 60-20+1=41\n",
    "Average value = N * 1/n * the width of the bins = 1000 * 1/41 * 4 = 97,5609\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "6f9560ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Exercise 4\n",
    "N = 6000; a = 1; b = 6; x = []\n",
    "for i in range(N):\n",
    "    xi = rnd.randint(a, b)\n",
    "    x.append(xi)\n",
    "plt.hist(x, bins=6)\n",
    "plt.xticks(range(1, 7,1))\n",
    "plt.xlim([1, 6])\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "ab4a580f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Exercise 5\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "mu = 100\n",
    "si = 5\n",
    "N = 100000\n",
    "values1 = np.random.normal(mu, si, N)\n",
    "\n",
    "plt.hist(values1, 100)\n",
    "plt.axvline(values1.mean(), color='r')\n",
    "plt.xlim([20,350])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "dbb1f13e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Exercise 5\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "mu = 200\n",
    "si = 10\n",
    "N = 100000\n",
    "values2 = np.random.normal(mu, si, N)\n",
    "\n",
    "plt.hist(values2, 100)\n",
    "plt.axvline(values2.mean(), color='r')\n",
    "plt.xlim([20,350])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "dcf325f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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v3gyp8fl8SktLc2qOHz8uy7Kc0CNJs2bNkmVZTg0AAEBf9fslpbZta926dXriiSeUlpYmSfL7/ZIkj8cTUuvxePTVV185NVFRURo7dmy3mtvf9/v9SkxM7PY7ExMTnZo7BYNBBYNB53NLS0s/jwwAAIxW/Q4+a9as0RdffKGqqqpu21wuV8hn27a7rbvTnTU91d9rP8XFxdqyZUtvmg4AMNhQneLC8NSvU12FhYX6+OOPdeTIEU2YMMFZ7/V6JanbrExTU5MzC+T1etXR0aHm5uZ71ly5cqXb77169Wq32aTbNm3apEAg4CwNDQ39OTQAADCK9Sn42LatNWvW6MMPP9Snn36q1NTUkO2pqanyer2qqKhw1nV0dKiyslJZWVmSpPT0dEVGRobUNDY26uzZs05NZmamAoGATp486dScOHFCgUDAqbmT2+1WXFxcyAIAANBVn051rV69Wu+//77++Mc/KjY21pnZsSxL0dHRcrlcKioq0tatWzV58mRNnjxZW7du1SOPPKL8/HyndsWKFVq/fr3GjRun+Ph4bdiwQdOnT9f8+fMlSVOnTtWiRYu0cuVK7d69W5K0atUq5ebm9uqOLgAA8J3bp/ZG2t1dg6VPwWfXrl2SpDlz5oSsf+edd7R8+XJJ0iuvvKL29na9/PLLam5uVkZGhg4dOqTY2FinfufOnYqIiNCSJUvU3t6uefPmac+ePRozZoxTs3//fq1du9a5+ysvL08lJSX9OUYAAABJD/gcn+GM5/gAAHoy1Bc3h/s5PreNlBmfYf0cHwAAgJGE4AMAAIxB8AEAAMYg+AAAAGMQfAAAgDEIPgAAwBgEHwAAYAyCDwAAMAbBBwAAGIPgAwAAjEHwAQAAxiD4AAAAYxB8AACAMSLC3QAAAAbbUL+RHcMXwQcAAAN0DX8Xtz0dxpaEF6e6AACAMQg+AADAGAQfAABgDIIPAAAwBsEHAAAYg+ADAACMQfABAADGIPgAAABjEHwAAIAxCD4AAMAYBB8AAGAMgg8AADAGwQcAABiD4AMAAIxB8AEAAMaICHcDAAAYLJM2Hgh3EzDMMOMDAACMQfABAADGIPgAAABjEHwAAIAxCD4AAMAYBB8AAGAMgg8AADAGwQcAAMNM2njA2GccEXwAAIAxCD4AAMAYBB8AAGAMgg8AADBGn4PPZ599pmeeeUY+n08ul0sfffRRyPbly5fL5XKFLLNmzQqpCQaDKiwsVEJCgmJiYpSXl6dLly6F1DQ3N6ugoECWZcmyLBUUFOj69et9PkAAAIDb+hx8bty4oRkzZqikpOSuNYsWLVJjY6OzHDx4MGR7UVGRysrKVFpaqqqqKrW1tSk3N1ednZ1OTX5+vmpra1VeXq7y8nLV1taqoKCgr80FAABwRPT1Czk5OcrJyblnjdvtltfr7XFbIBDQ22+/rb1792r+/PmSpH379ik5OVmHDx/WwoULVVdXp/LyclVXVysjI0OS9NZbbykzM1Pnz5/XlClT+tpsAACAwbnG5+jRo0pMTNSjjz6qlStXqqmpydlWU1OjmzdvKjs721nn8/mUlpamY8eOSZKOHz8uy7Kc0CNJs2bNkmVZTg0AAD25/YwaU59Tg3vr84zP/eTk5OiFF15QSkqK6uvr9dprr2nu3LmqqamR2+2W3+9XVFSUxo4dG/I9j8cjv98vSfL7/UpMTOy278TERKfmTsFgUMFg0Pnc0tIygEcFAABGgwEPPkuXLnV+TktL08yZM5WSkqIDBw5o8eLFd/2ebdtyuVzO564/362mq+LiYm3ZsuUBWg4AAEa7Qb+dPSkpSSkpKbpw4YIkyev1qqOjQ83NzSF1TU1N8ng8Ts2VK1e67evq1atOzZ02bdqkQCDgLA0NDQN8JAAAYKQb9OBz7do1NTQ0KCkpSZKUnp6uyMhIVVRUODWNjY06e/assrKyJEmZmZkKBAI6efKkU3PixAkFAgGn5k5ut1txcXEhCwAAQFd9PtXV1tamv/71r87n+vp61dbWKj4+XvHx8dq8ebOef/55JSUl6eLFi3r11VeVkJCgH//4x5Iky7K0YsUKrV+/XuPGjVN8fLw2bNig6dOnO3d5TZ06VYsWLdLKlSu1e/duSdKqVauUm5vLHV0AAKDf+hx8Tp8+raeeesr5vG7dOknSsmXLtGvXLp05c0bvvfeerl+/rqSkJD311FP64IMPFBsb63xn586dioiI0JIlS9Te3q558+Zpz549GjNmjFOzf/9+rV271rn7Ky8v757PDgIAALgfl23bdrgbMRhaWlpkWZYCgQCnvQDAIMPtNvbojm9Ut/MnkqSpv/yD2qMeDnOLvndx29PhbkI3g/33m3d1AQAAYxB8AAAwlIkPeiT4AAAAYxB8AACAMQg+AADAGAQfAABgDIIPAAAwBsEHAAAYg+ADAACMQfABAADGIPgAAABjEHwAAIAx+vx2dgAAhouur1sYji/cxPDDjA8AADAGMz4AgFHBtJdtDiSTZs6Y8QEAAMYg+AAAAGMQfAAAgDEIPgAAwBgEHwAAYAyCDwAAMAbBBwAAGIPgAwAAjEHwAQAAxiD4AAAAYxB8AACAMQg+AADAGAQfAABgDIIPAAAwBsEHAAA4Jm08oEkbD4S7GYOG4AMAAIxB8AEAAMYg+AAAAGMQfAAAgDEIPgAAwBgEHwAAYAyCDwAAMAbBBwAAGCMi3A0AAKCvRvMD9jC4mPEBAADGIPgAAABjEHwAAIAxCD4AAMAYfQ4+n332mZ555hn5fD65XC599NFHIdtt29bmzZvl8/kUHR2tOXPm6Ny5cyE1wWBQhYWFSkhIUExMjPLy8nTp0qWQmubmZhUUFMiyLFmWpYKCAl2/fr3PBwgAAHBbn4PPjRs3NGPGDJWUlPS4ffv27dqxY4dKSkp06tQpeb1eLViwQK2trU5NUVGRysrKVFpaqqqqKrW1tSk3N1ednZ1OTX5+vmpra1VeXq7y8nLV1taqoKCgH4cIAADwnT7fzp6Tk6OcnJwet9m2rTfeeEO//vWvtXjxYknSu+++K4/Ho/fff18vvviiAoGA3n77be3du1fz58+XJO3bt0/Jyck6fPiwFi5cqLq6OpWXl6u6uloZGRmSpLfeekuZmZk6f/68pkyZ0t/jBQAABhvQa3zq6+vl9/uVnZ3trHO73Zo9e7aOHTsmSaqpqdHNmzdDanw+n9LS0pya48ePy7IsJ/RI0qxZs2RZllNzp2AwqJaWlpAFAACgqwF9gKHf75ckeTyekPUej0dfffWVUxMVFaWxY8d2q7n9fb/fr8TExG77T0xMdGruVFxcrC1btjzwMQAAgNCHRF7c9nQYWzKwBuWuLpfLFfLZtu1u6+50Z01P9ffaz6ZNmxQIBJyloaGhHy0HAACj2YAGH6/XK0ndZmWampqcWSCv16uOjg41Nzffs+bKlSvd9n/16tVus0m3ud1uxcXFhSwAAABdDWjwSU1NldfrVUVFhbOuo6NDlZWVysrKkiSlp6crMjIypKaxsVFnz551ajIzMxUIBHTy5Emn5sSJEwoEAk4NAABAX/X5Gp+2tjb99a9/dT7X19ertrZW8fHxmjhxooqKirR161ZNnjxZkydP1tatW/XII48oPz9fkmRZllasWKH169dr3Lhxio+P14YNGzR9+nTnLq+pU6dq0aJFWrlypXbv3i1JWrVqlXJzc7mjCwAA9Fufg8/p06f11FNPOZ/XrVsnSVq2bJn27NmjV155Re3t7Xr55ZfV3NysjIwMHTp0SLGxsc53du7cqYiICC1ZskTt7e2aN2+e9uzZozFjxjg1+/fv19q1a527v/Ly8u767CAAgBl4KzselMu2bTvcjRgMLS0tsixLgUCA630AYJQYicEnuuMb1e38iSRp6i//oPaoh8Pcor4byru6BvvvN+/qAgAAxiD4AAAAYxB8AACAMQg+AADAGAQfAABgDIIPAAAwBsEHAAAYg+ADAACMQfABAAD3NGnjgRH58Mie9PmVFQAADKXR8gcXwwMzPgAAwBgEHwAAYAyCDwAAMAbBBwAAGIPgAwAAjEHwAQAAxuB2dgDAsMRt7BgMBB8AANArXcPoxW1Ph7El/cepLgAAYAyCDwAAMAbBBwAAGIPgAwAAjEHwAQAAxiD4AAAAYxB8AACAMQg+AADAGAQfAABgDIIPAAAwBsEHAAAYg+ADAACMQfABAADGIPgAAABjEHwAAECfTdp4QJM2Hgh3M/osItwNAADgtpH4hxQjCzM+AADAGAQfAABgDIIPAAAwBsEHAAAYg4ubAQBhx0XNI9ft/3YXtz0d5pb0DjM+AADAGAQfAABgDIIPAAAwBsEHAAAYY8CDz+bNm+VyuUIWr9frbLdtW5s3b5bP51N0dLTmzJmjc+fOhewjGAyqsLBQCQkJiomJUV5eni5dujTQTQUAAIYZlBmfH/3oR2psbHSWM2fOONu2b9+uHTt2qKSkRKdOnZLX69WCBQvU2trq1BQVFamsrEylpaWqqqpSW1ubcnNz1dnZORjNBQAAhhiU29kjIiJCZnlus21bb7zxhn79619r8eLFkqR3331XHo9H77//vl588UUFAgG9/fbb2rt3r+bPny9J2rdvn5KTk3X48GEtXLhwMJoMAAAMMCgzPhcuXJDP51Nqaqp++tOf6ssvv5Qk1dfXy+/3Kzs726l1u92aPXu2jh07JkmqqanRzZs3Q2p8Pp/S0tKcmp4Eg0G1tLSELAAAYGjcflv7cH8m04AHn4yMDL333nv65JNP9NZbb8nv9ysrK0vXrl2T3++XJHk8npDveDweZ5vf71dUVJTGjh1715qeFBcXy7IsZ0lOTh7gIwMAACPdgAefnJwcPf/885o+fbrmz5+vAwe+S37vvvuuU+NyuUK+Y9t2t3V3ul/Npk2bFAgEnKWhoeEBjgIAAIxGg347e0xMjKZPn64LFy441/3cOXPT1NTkzAJ5vV51dHSoubn5rjU9cbvdiouLC1kAAAC6GvTgEwwGVVdXp6SkJKWmpsrr9aqiosLZ3tHRocrKSmVlZUmS0tPTFRkZGVLT2Nios2fPOjUAgNFhJFwTgtFlwO/q2rBhg5555hlNnDhRTU1N+s1vfqOWlhYtW7ZMLpdLRUVF2rp1qyZPnqzJkydr69ateuSRR5Sfny9JsixLK1as0Pr16zVu3DjFx8drw4YNzqkzAACA/hrw4HPp0iX97Gc/09///neNHz9es2bNUnV1tVJSUiRJr7zyitrb2/Xyyy+rublZGRkZOnTokGJjY5197Ny5UxEREVqyZIna29s1b9487dmzR2PGjBno5gIAAIO4bNu2w92IwdDS0iLLshQIBLjeBwCGKRNOc0V3fKO6nT+RJE395R/UHvVwmFs0+C5ue7rf3x3sv9+8qwsAABiD4AMAAAbUcL5oneADAACMQfABAADGIPgAAABjDMrb2QEAuJvheu0HzMCMDwAAMAbBBwAAGINTXQAAYFB0Pa35IA81HEjM+AAAAGMw4wMAGBJc1IzhgBkfAABgDIIPAAAwBsEHAAAMuuHy/i6CDwAAMAbBBwAAGIO7ugAAg2Y4nNoAumLGBwAAGIPgAwAAjEHwAQAAxiD4AACAIRPu29oJPgAAwBjc1QUAGHDczYXhihkfAABgDGZ8AAADhpke9FbXsXJx29ND9nuZ8QEAAMYg+AAAAGNwqgsA8EA4vYWRhBkfAAAQVkP5bB+CDwAAMAbBBwAAGINrfAAA/cK1PRiJmPEBAADGYMYHAAAMC5M2HtCt4NeD+jsIPgCAXuP0FkY6TnUBAABjEHwAAIAxONUFALgvTnFhtGDGBwAAGIMZHwBACGZ3MJox4wMAAIxB8AEAAMYg+AAAAGMM++Dz5ptvKjU1VQ8//LDS09P13//93+FuEgCMGpM2HnAWwATD+uLmDz74QEVFRXrzzTf1z//8z9q9e7dycnL05z//WRMnTgx38wBgRLlfuCH8wATDesZnx44dWrFihf7lX/5FU6dO1RtvvKHk5GTt2rUr3E0DgBGDGR3ge8N2xqejo0M1NTXauHFjyPrs7GwdO3asW30wGFQwGHQ+BwIBSVJLS8vgNhQAhoG01z8JdxNwF50d3+j2X6LO4Ne6Zd8Ka3uGu9svKbVte1D2P2yDz9///nd1dnbK4/GErPd4PPL7/d3qi4uLtWXLlm7rk5OTB62NAAD0hnX7hzf/XzibMaJcu3ZNlmXdv7CPhm3wuc3lcoV8tm272zpJ2rRpk9atW+d8vnXrlv7v//5P48aN67F+OGppaVFycrIaGhoUFxcX7uYMa/RV39BfvUdf9R591Xv0Ve8FAgFNnDhR8fHxg7L/YRt8EhISNGbMmG6zO01NTd1mgSTJ7XbL7XaHrPuHf/iHwWzioImLi+MfRi/RV31Df/UefdV79FXv0Ve999BDg3MZ8rC9uDkqKkrp6emqqKgIWV9RUaGsrKwwtQoAAIxkw3bGR5LWrVungoICzZw5U5mZmfqP//gP/e1vf9NLL70U7qYBAIARaFgHn6VLl+ratWv613/9VzU2NiotLU0HDx5USkpKuJs2KNxut15//fVup+zQHX3VN/RX79FXvUdf9R591XuD3Vcue7DuFwMAABhmhu01PgAAAAON4AMAAIxB8AEAAMYg+AAAAGMQfMJg8+bNcrlcIYvX63W227atzZs3y+fzKTo6WnPmzNG5c+fC2OKh89lnn+mZZ56Rz+eTy+XSRx99FLK9N30TDAZVWFiohIQExcTEKC8vT5cuXRrCoxga9+ur5cuXdxtns2bNCqkxoa+Ki4v1+OOPKzY2VomJiXruued0/vz5kBrG1fd601+Mre/s2rVLjz32mPNQwszMTP3pT39ytjOuvne/vhrKMUXwCZMf/ehHamxsdJYzZ84427Zv364dO3aopKREp06dktfr1YIFC9Ta2hrGFg+NGzduaMaMGSopKelxe2/6pqioSGVlZSotLVVVVZXa2tqUm5urzs7OoTqMIXG/vpKkRYsWhYyzgwcPhmw3oa8qKyu1evVqVVdXq6KiQt9++62ys7N148YNp4Zx9b3e9JfE2JKkCRMmaNu2bTp9+rROnz6tuXPn6tlnn3XCDePqe/frK2kIx5SNIff666/bM2bM6HHbrVu3bK/Xa2/bts1Z980339iWZdn//u//PkQtHB4k2WVlZc7n3vTN9evX7cjISLu0tNSp+d///V/7oYcessvLy4es7UPtzr6ybdtetmyZ/eyzz971O6b2VVNTky3JrqystG2bcXU/d/aXbTO27mXs2LH2f/7nfzKueuF2X9n20I4pZnzC5MKFC/L5fEpNTdVPf/pTffnll5Kk+vp6+f1+ZWdnO7Vut1uzZ8/WsWPHwtXcYaE3fVNTU6ObN2+G1Ph8PqWlpRnZf0ePHlViYqIeffRRrVy5Uk1NTc42U/sqEAhIkvMCRMbVvd3ZX7cxtkJ1dnaqtLRUN27cUGZmJuPqHu7sq9uGakwN6yc3j1YZGRl677339Oijj+rKlSv6zW9+o6ysLJ07d855KeudL2L1eDz66quvwtHcYaM3feP3+xUVFaWxY8d2q7nzhbejXU5Ojl544QWlpKSovr5er732mubOnauamhq53W4j+8q2ba1bt05PPPGE0tLSJDGu7qWn/pIYW12dOXNGmZmZ+uabb/SDH/xAZWVlmjZtmvPHmHH1vbv1lTS0Y4rgEwY5OTnOz9OnT1dmZqb+6Z/+Se+++65zMZfL5Qr5jm3b3daZqj99Y2L/LV261Pk5LS1NM2fOVEpKig4cOKDFixff9Xujua/WrFmjL774QlVVVd22Ma66u1t/Mba+N2XKFNXW1ur69ev6r//6Ly1btkyVlZXOdsbV9+7WV9OmTRvSMcWprmEgJiZG06dP14ULF5y7u+5MsE1NTd3+z8E0vekbr9erjo4ONTc337XGVElJSUpJSdGFCxckmddXhYWF+vjjj3XkyBFNmDDBWc+46tnd+qsnJo+tqKgo/fCHP9TMmTNVXFysGTNm6He/+x3jqgd366ueDOaYIvgMA8FgUHV1dUpKSlJqaqq8Xq8qKiqc7R0dHaqsrFRWVlYYWxl+vemb9PR0RUZGhtQ0Njbq7NmzxvfftWvX1NDQoKSkJEnm9JVt21qzZo0+/PBDffrpp0pNTQ3ZzrgKdb/+6ompY6sntm0rGAwyrnrhdl/1ZFDHVJ8uhcaAWL9+vX306FH7yy+/tKurq+3c3Fw7NjbWvnjxom3btr1t2zbbsiz7ww8/tM+cOWP/7Gc/s5OSkuyWlpYwt3zwtba22p9//rn9+eef25LsHTt22J9//rn91Vdf2bbdu7556aWX7AkTJtiHDx+2/+d//seeO3euPWPGDPvbb78N12ENinv1VWtrq71+/Xr72LFjdn19vX3kyBE7MzPT/sd//Efj+uoXv/iFbVmWffToUbuxsdFZvv76a6eGcfW9+/UXY+t7mzZtsj/77DO7vr7e/uKLL+xXX33Vfuihh+xDhw7Zts246upefTXUY4rgEwZLly61k5KS7MjISNvn89mLFy+2z50752y/deuW/frrr9ter9d2u932k08+aZ85cyaMLR46R44csSV1W5YtW2bbdu/6pr293V6zZo0dHx9vR0dH27m5ufbf/va3MBzN4LpXX3399dd2dna2PX78eDsyMtKeOHGivWzZsm79YEJf9dRHkux33nnHqWFcfe9+/cXY+t7Pf/5zOyUlxY6KirLHjx9vz5s3zwk9ts246upefTXUY8pl27bdtzkiAACAkYlrfAAAgDEIPgAAwBgEHwAAYAyCDwAAMAbBBwAAGIPgAwAAjEHwAQAAxiD4AAAAYxB8AACAMQg+AADAGAQfAABgDIIPAAAwxv8HaYL2hDhPm+sAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Exercise 5\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "mu = 300\n",
    "si = 20\n",
    "N = 100000\n",
    "values1 = np.random.normal(mu, si, N)\n",
    "\n",
    "plt.hist(values1, 100)\n",
    "plt.axvline(values1.mean(), color='r')\n",
    "plt.xlim([20,350])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "57f9ce25",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}