--- /dev/null
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "id": "8c938713-e577-4cd6-a1bc-7d15af208464",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%matplotlib inline\n",
+ "\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import scipy.stats as stats\n",
+ "from sklearn import linear_model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5159cf05-14f2-4e79-a9e1-a2d2e110b509",
+ "metadata": {},
+ "source": [
+ "# distribution"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "ae05f3e9-db9b-45dd-8a6f-ba986985dc65",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "uniform_data = stats.uniform.rvs(size=100000, # Generate 100000 numbers\n",
+ " loc = 0, # From 0 \n",
+ " scale=10) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "afec21f8-cb61-46ad-9378-a85e9b10e8ed",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 900x900 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "pd.DataFrame(uniform_data).plot(kind=\"density\", # Plot the distribution\n",
+ " figsize=(9,9),\n",
+ " xlim=(-1,11));"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "9b6f8b52-8ec6-4061-9a89-2de6cf7d4470",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "np.float64(0.25)"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "stats.uniform.cdf(x=2.5, # Cutoff value (quantile) to check\n",
+ " loc=0, # Distribution start\n",
+ " scale=10) # Distribution end"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "5edca5f0-9a97-4a3c-8fb6-41ccfff7f926",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "np.float64(4.0)"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "stats.uniform.ppf(q=0.4, # Probability cutoff\n",
+ " loc=0, # Distribution start\n",
+ " scale=10) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "b8cd7bde-fa7e-41ee-99ca-14f3629f24f9",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Density at x value -1\n",
+ "0.0\n",
+ "Density at x value 2\n",
+ "0.1\n",
+ "Density at x value 5\n",
+ "0.1\n",
+ "Density at x value 8\n",
+ "0.1\n",
+ "Density at x value 11\n",
+ "0.0\n"
+ ]
+ }
+ ],
+ "source": [
+ "for x in range(-1,12,3):\n",
+ " print(\"Density at x value \" + str(x))\n",
+ " print( stats.uniform.pdf(x, loc=0, scale=10) ) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "6d3b5b2c-9056-412c-a4c5-0774c2d721cc",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "2"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import random\n",
+ "\n",
+ "random.randint(0,10) # Get a random integer in the specified range\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "5924cee2-0f50-4075-b0f1-9e06eed372da",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "6"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "random.choice([2,4,6,9]) # Get a random element from a sequence"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "29887512-eba2-4f8e-af9e-9d9a5147ed80",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.11117785727508667"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "random.random() # Get a real number between 0 and 1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "dde82da3-19bd-4d2c-b6e5-e567e1fcf1c9",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "2.5767573846317813"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "random.uniform(0,10) # Get a real in the specified range"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "6ad19ad6-4afc-4bed-8726-d879d4ecce90",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[4.7457067868854805, 6.574725026572553, 6.664104711248381, 1.4260035292536777]\n",
+ "[4.7457067868854805, 6.574725026572553, 6.664104711248381, 1.4260035292536777]\n"
+ ]
+ }
+ ],
+ "source": [
+ "random.seed(12) # Set the seed to an arbitrary value\n",
+ "\n",
+ "print([random.uniform(0,10) for x in range(4)])\n",
+ "\n",
+ "random.seed(12) # Set the seed to the same value\n",
+ "\n",
+ "print([random.uniform(0,10) for x in range(4)])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "b5691d0a-a95c-40b2-9641-df507640df31",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0.15865525393145707 0.15865525393145707 0.6826894921370859\n"
+ ]
+ }
+ ],
+ "source": [
+ "prob_under_minus1 = stats.norm.cdf(x= -1, \n",
+ " loc = 0, \n",
+ " scale= 1) \n",
+ "\n",
+ "prob_over_1 = 1 - stats.norm.cdf(x= 1, \n",
+ " loc = 0, \n",
+ " scale= 1) \n",
+ "\n",
+ "between_prob = 1-(prob_under_minus1+prob_over_1)\n",
+ "\n",
+ "print(prob_under_minus1, prob_over_1, between_prob)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "c74523f7-f0cd-47b2-aaab-b01c8d128987",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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AUi/Dstx/M+1QKKS8vDwFg0Hl5uaaLgcAEvbf/23feArJkZUlffWr9r8A4EaDzblx33wKAJAa3d1SY6PpKrwlGuVDEgB/INQDgEPU19sDZZFczFcPwA8I9QDgEITP1OB1BeAHhHoAcAjCZ2q0tEjHmEUZADyBUA8ADtDZKYVCpqvwLj4wAfA6Qj0AOAA3nEotXl8AXkeoBwAHoCU5tXh9AXgdoR4AHIDQmVodHVJbm+kqACB1CPUAYFgwKHV1ma7C++iCA8DLCPUAYBhhMz14nQF4GaEeAAwjbKZHXR039wLgXYR6ADDIsgj16RIO212dAMCLCPUAYFBLi9TdbboK/+ADFACvItQDgEGEzPTi9QbgVYR6ADCIkJle9fX0qwfgTYR6ADAkFpMaGkxX4S/d3XaXJwDwGkI9ABjS3Cz19Jiuwn+4OgLAiwj1AGAI4dIMXncAXkSoBwBD6utNV+BPDQ121ycA8BJCPQAYQH96c3p67K5PAOAlhHoAMKCpSYpGTVfhX1wlAeA1hHoAMIBQaRavPwCvIdQDgAEM1jSLfvUAvIZQDwBpFotJjY2mq/A3+tUD8BpCPQCkGf3pnYEuOAC8hFAPAGlGmHQGfg8AvIRQDwBpRph0hoYGybJMVwEAyUGoB4A0oj+9c/T0SC0tpqsAgOQg1ANAGrW02GESzsBVEwBeQagHgDQiRDoLvw8AXkGoB4A0IkQ6C/3qAXgFoR4A0sSy7BAJ54hEpGDQdBUAMHSEegBIk9ZWqbvbdBU4EldPAHgBoR4A0oRWemfi9wLACwj1AJAmtAg7E78XAF5AqAeANKFF2Jm6uqS2NtNVAMDQEOoBIA3a2uzwCGfiAxcAtyPUA0Aa0MXD2Qj1ANyOUA8AaUBodDY+dAFwO0I9AKQBod7Z2tulAwdMVwEAiSPUA0CKHThgh0Y4Gx+8ALgZoR4AUoyw6A78ngC4GaEeAFKMsOgO/J4AuBmhHgBSjLDoDq2tUne36SoAIDGEegBIoe5uOyzC+SxLamoyXQUAJIZQDwAp1NRkh0W4A1NbAnArQj0ApBBdb9ylsdF0BQCQGEI9AKQQod5dGhulWMx0FQAQP0I9AKRILEbLr9tEo4yBAOBOhHoASJHWVjskwl24ugLAjQj1AJAihEN34vcGwI0I9QCQIoRDd+L3BsCNCPUAkCL0p3enri6pvd10FQAQH0I9AKRAR4d04IDpKpAoPpABcBtCPQCkAF043I3fHwC3IdQDQArQ0utu/P4AuA2hHgBSgJZed2ttlbq7TVcBAINHqAeAJOvuloJB01VgqJqaTFcAAINHqAeAJGtqkizLdBUYKq62AHATQj0AJBn9sb2B3yMANyHUA0CSEQa9gSsuANyEUA8ASWRZhHqvYGwEADch1ANAEoVCzJriJXxAA+AWhHoASCIGV3oLv08AbkGoB4AkYhpEb+H3CcAtCPUAkES07HpLW5vU1WW6CgA4PkI9ACRJOGyHQHgL/eoBuAGhHgCShPDnTXTBAeAGCYX61atXa8qUKcrJyVFxcbE2bNgw4La///3vNX/+fI0ZM0YnnniiioqK9MQTT/TZxrIsrVixQhMnTtSIESNUUlKibdu2JVIaABhD+PMmPqwBcIO4Q/3atWtVXl6ulStXatOmTZo7d65KS0tVX1/f7/bjxo3Tj3/8Y1VXV+vdd99VWVmZysrK9Oc//7l3m7vvvlv33Xef1qxZo/Xr1+vEE09UaWmpuujICMBFCH/e1NQkxWKmqwCAY8uwrPjul1dcXKyzzjpLDzzwgCQpFoupsLBQP/jBD3TzzTcPah+f+9zndMkll+j222+XZVmaNGmS/vmf/1k33HCDJCkYDCo/P1+PPvqorrjiiuPuLxQKKS8vT8FgULm5ufE8HQBICsuSnnlG6ukxXQlSobRUGjfOdBUA/GiwOTeulvpIJKKNGzeqpKTk0A4yM1VSUqLq6urjPt6yLFVVVWnr1q36whe+IEnauXOnAoFAn33m5eWpuLh4wH2Gw2GFQqE+CwCY1NpKoPcyrsIAcLq4Qn1jY6Oi0ajy8/P7rM/Pz1cgEBjwccFgUKNGjdLw4cN1ySWX6P7779cXv/hFSep9XDz7rKioUF5eXu9SWFgYz9MAgKQj9Hkbv18ATpeW2W9Gjx6tzZs3680339TPfvYzlZeXa926dQnvb/ny5QoGg73Lnj17klcsACSA0OdtDIIG4HTD4tl4/PjxysrKUl1dXZ/1dXV1KigoGPBxmZmZmjFjhiSpqKhIH374oSoqKnTBBRf0Pq6urk4TJ07ss8+ioqJ+95edna3s7Ox4SgeAlCL0eVt7u30Tqpwc05UAQP/iaqkfPny45s2bp6qqqt51sVhMVVVVWrhw4aD3E4vFFA6HJUlTp05VQUFBn32GQiGtX78+rn0CgCncdMof+OAGwMniaqmXpPLyci1btkzz58/XggULtGrVKnV0dKisrEyStHTpUk2ePFkVFRWS7P7v8+fP1/Tp0xUOh/XCCy/oiSee0IMPPihJysjI0PXXX6877rhDM2fO1NSpU3Xrrbdq0qRJuvzyy5P3TAEgRQh7/tDYKE2ebLoKAOhf3KF+yZIlamho0IoVKxQIBFRUVKTKysrega41NTXKzDx0AaCjo0Pf+973tHfvXo0YMUKzZs3Sr3/9ay1ZsqR3mxtvvFEdHR369re/rdbWVp177rmqrKxUDtc5AbgA/en9gd8zACeLe556J2KeegAmvfKKdIwJwOARw4ZJX/ualJFhuhIAfpKSeeoBAH1ZFt1v/KKnRwoGTVcBAP0j1APAEIRCUne36SqQLnTBAeBUhHoAGAJCnr/w+wbgVIR6ABgCut74C79vAE5FqAeAIaDl1l9CISkSMV0FAByNUA8ACeruZuCkH9FaD8CJCPUAkKDmZtMVwARCPQAnItQDQILoeuNP/N4BOBGhHgASRIutP/F7B+BEhHoASBAttv4UidgDZgHASQj1AJCA9nYpHDZdBUyhtR6A0xDqASABhDp/4/cPwGkI9QCQALre+Bu/fwBOQ6gHgATQUutvra1SNGq6CgA4hFAPAHGKxaSWFtNVwCTL4j4FAJyFUA8AcWppsYM9/I2rNQCchFAPAHEizEHiOADgLIR6AIgTYQ4SxwEAZyHUA0CcCHOQpI4OqavLdBUAYCPUA0AcwmGprc10FXAKPuABcApCPQDEgRlPcDhCPQCnINQDQBy46RAOx/EAwCkI9QAQB1pmcbjmZnvOegAwjVAPAHGg+w0O193NGAsAzkCoB4BBam+3B8oCh6MLDgAnINQDwCDR9Qb94eoNACcg1APAIBHq0R+OCwBOQKgHgEEivKE/LS1SNGq6CgB+R6gHgEGIxehmgf5Zlh3sAcAkQj0ADEJrqx3sgf5wFQeAaYR6ABgEQhuOheMDgGmEegAYBEIbjoXjA4BphHoAGARCG46FexgAMI1QDwDH0d0thUKmq4DTMZAagEmEegA4DsIaBoOrOQBMItQDwHE0NpquAG7AcQLAJEI9ABwHLfUYDI4TACYR6gHgOGiBxWCEw/aAWQAwgVAPAMfQ2Sl1dZmuAm5Bv3oAphDqAeAYCGmIB8cLAFMI9QBwDPSTRjw4XgCYQqgHgGOg5RXxaG6WLMt0FQD8iFAPAAOwLFpeEZ9oVGptNV0FAD8i1APAANra7LvJAvHggyAAEwj1ADAAut4gERw3AEwg1APAAAhnSATHDQATCPUAMADCGRIRDEo9PaarAOA3hHoA6EcsxoBHJMaypJYW01UA8BtCPQD0o6XFDvZAIrjKAyDdCPUA0A9mMMFQcPwASDdCPQD0g5ZWDAXHD4B0I9QDQD8IZRiK9nYpHDZdBQA/IdQDwBG6u6VQyHQVcDu64ABIJ0I9AByBMIZk4GoPgHQi1APAEQhjSAaOIwDpRKgHgCPQUo9k4DgCkE6EegA4Ai2sSIauLqmz03QVAPyCUA8AhyGIIZkaG01XAMAvCPUAcBha6ZFMdMEBkC6EegA4DKEeycTxBCBdCPUAcBhaVpFMLS2SZZmuAoAfEOoB4DC0rCKZuJEZgHQh1APA37S3S5GI6SrgNVz9AZAOhHoA+Bta6ZEKHFcA0oFQDwB/Q/hCKnBcAUgHQj0A/A3dJJAKra1SLGa6CgBeR6gHANkzlBDqkQqxmB3sASCVCPUAICkYlKJR01XAq+iCAyDVEgr1q1ev1pQpU5STk6Pi4mJt2LBhwG0feughnXfeeRo7dqzGjh2rkpKSo7a/6qqrlJGR0WdZvHhxIqUBQEIIXUglrgIBSLW4Q/3atWtVXl6ulStXatOmTZo7d65KS0tVX1/f7/br1q3TlVdeqVdeeUXV1dUqLCzUokWLtG/fvj7bLV68WPv37+9dfvOb3yT2jAAgAYQupBIfGgGkWoZlxXevu+LiYp111ll64IEHJEmxWEyFhYX6wQ9+oJtvvvm4j49Goxo7dqweeOABLV26VJLdUt/a2qrnnnsu/mcgKRQKKS8vT8FgULm5uQntA4C/VVbad/8EUiEjQ/ra16Rhw0xXAsBtBptz42qpj0Qi2rhxo0pKSg7tIDNTJSUlqq6uHtQ+Ojs71d3drXHjxvVZv27dOk2YMEGnnXaarrnmGjUdo1kjHA4rFAr1WQAgUdEoAxmRWgzEBpBqcYX6xsZGRaNR5efn91mfn5+vQCAwqH3cdNNNmjRpUp8PBosXL9bjjz+uqqoq3XXXXXr11Vd18cUXKzrAqLWKigrl5eX1LoWFhfE8DQDoo6XFDl1AKhHqAaRSWi8E3nnnnXr66ae1bt065eTk9K6/4oorev9/xhlnaM6cOZo+fbrWrVuniy666Kj9LF++XOXl5b1fh0Ihgj2AhBG2kA70qweQSnG11I8fP15ZWVmqq6vrs76urk4FBQXHfOw999yjO++8Uy+++KLmzJlzzG2nTZum8ePHa/v27f1+Pzs7W7m5uX0WAEgUYQvpwIdHAKkUV6gfPny45s2bp6qqqt51sVhMVVVVWrhw4YCPu/vuu3X77bersrJS8+fPP+7P2bt3r5qamjRx4sR4ygOAhBC2kA7t7VI4bLoKAF4V95SW5eXleuihh/TYY4/pww8/1DXXXKOOjg6VlZVJkpYuXarly5f3bn/XXXfp1ltv1cMPP6wpU6YoEAgoEAiovb1dktTe3q4f/vCHeuONN7Rr1y5VVVXpsssu04wZM1RaWpqkpwkA/evulhhrj3ThAySAVIm7T/2SJUvU0NCgFStWKBAIqKioSJWVlb2DZ2tqapSZeeizwoMPPqhIJKKvfe1rffazcuVK3XbbbcrKytK7776rxx57TK2trZo0aZIWLVqk22+/XdnZ2UN8egBwbIQspFNTk8RFaACpEPc89U7EPPUAEvXBB9I775iuAn4xebL0hS+YrgKAm6RknnoA8Bpa6pFODMoGkCqEegC+RshCOnV1SZ2dpqsA4EWEegC+RcCCCcyAAyAVCPUAfItWegCAVxDqAfgW/ekBAF5BqAfgWy0tpiuAL1mWvQBAEhHqAfhWT4/pCuBLliW1tZmuAoDHEOoBAEg3+n4BSDJCPQAA6cYobQBJRqgH4E/RqOkK4Ge01ANIMkI9AH9qbTVdAfyspUWKxUxXAcBDCPUA/MeyaCmFWdGoFAyargKAhxDqAfhPKCR1d5uuAn5Hv3oASUSoB+A/tNLDCTgOASQRoR6A/9BCCifgOASQRIR6AP5DmIITBIPMwgQgaQj1APwlFmPmGzgDA7YBJBGhHoC/tLYylSCcg1APIEkI9QD8ha43cBKORwBJQqgH4C+0jMJJOB4BJAmhHoC/0DIKJ2lrkyIR01UA8ABCPQD/6OmxbzwFOAmt9QCSgFAPwD+am+0ZRwAn4eoRgCQg1APwD1pE4UQclwCSgFAPwD9oEYUTcVwCSAJCPQD/IDzBiQ4csBcAGAJCPQB/CIeljg7TVQD94wMngCEi1APwB/otw8k4PgEMEaEegD/QEgon4/gEMESEegD+QGiCk9FSD2CICPUA/IHQBCeLROy7ywJAggj1ALyvo0Pq6jJdBXBsXE0CMASEegDeRys93IDjFMAQEOoBeB8toHADjlMAQ0CoB+B9hCW4QUuLFIuZrgKASxHqAXibZdlhCXC6aFQKBk1XAcClCPUAvC0Ukrq7TVcBDA5XlQAkiFAPwNsYfAg34XgFkCBCPQBvo+UTbsLxCiBBhHoA3kZIgpsEg1JPj+kqALgQoR6Ad8ViUmur6SqAwWNgN4AEEeoBeFdrK1MEwn3oVw8gAYR6AN5F1xu4EcctgAQQ6gF4F+EIbsRxCyABhHoA3kU4ghu1t0vhsOkqALgMoR6AN3V32zeeAtyIfvUA4kSoB+BNhCK4GVeZAMSJUA/AmwhFcDOOXwBxItQD8CZCEdyM4xdAnAj1ALyJUAQ3C4eljg7TVQBwEUI9AO85cMBeADfjgymAOBDqAXgPYQhewHEMIA6EegDew8w38AKOYwBxINQD8B5aOOEFTU2SZZmuAoBLEOoBeItlEerhDdGoFAyargKASxDqAXhLW5t9N1nAC/iACmCQCPUAvIV+yPASjmcAg0SoB+AttGzCSzieAQwSoR6AtxCC4CWtrXbfegA4DkI9AO+IxaSWFtNVAMljWXTBATAohHoA3tHSYgd7wEu4+gRgEAj1ALyD8AMv4rgGMAiEegDeQfiBF3FcAxgEQj0A76DvMbyoo0MKh01XAcDhCPUAvCESkUIh01UAqUFrPYDjINQD8AZa6eFlhHoAx0GoB+ANhB54Gcc3gOMg1APwBkIPvIzjG8BxJBTqV69erSlTpignJ0fFxcXasGHDgNs+9NBDOu+88zR27FiNHTtWJSUlR21vWZZWrFihiRMnasSIESopKdG2bdsSKQ2AXxF64GWRiNTWZroKAA4Wd6hfu3atysvLtXLlSm3atElz585VaWmp6uvr+91+3bp1uvLKK/XKK6+ourpahYWFWrRokfbt29e7zd1336377rtPa9as0fr163XiiSeqtLRUXV1diT8zAP7R0SHx9wJexwdXAMcQd6i/9957dfXVV6usrEyzZ8/WmjVrNHLkSD388MP9bv/kk0/qe9/7noqKijRr1iz927/9m2KxmKqqqiTZrfSrVq3SLbfcossuu0xz5szR448/rtraWj333HNDenIAfIKwAz/gOAdwDHGF+kgkoo0bN6qkpOTQDjIzVVJSourq6kHto7OzU93d3Ro3bpwkaefOnQoEAn32mZeXp+Li4gH3GQ6HFQqF+iwAfIywAz/gOAdwDHGF+sbGRkWjUeXn5/dZn5+fr0AgMKh93HTTTZo0aVJviD/4uHj2WVFRoby8vN6lsLAwnqcBwGsIO/CDlhYpFjNdBQCHSuvsN3feeaeefvppPfvss8rJyUl4P8uXL1cwGOxd9uzZk8QqAbhKLMYc9fCHWMwO9gDQj7hC/fjx45WVlaW6uro+6+vq6lRQUHDMx95zzz2688479eKLL2rOnDm96w8+Lp59ZmdnKzc3t88CwKeCQSkaNV0FkB5clQIwgLhC/fDhwzVv3rzeQa6Sege9Lly4cMDH3X333br99ttVWVmp+fPn9/ne1KlTVVBQ0GefoVBI69evP+Y+AUCS1NhougIgfTjeAQxgWLwPKC8v17JlyzR//nwtWLBAq1atUkdHh8rKyiRJS5cu1eTJk1VRUSFJuuuuu7RixQo99dRTmjJlSm8/+VGjRmnUqFHKyMjQ9ddfrzvuuEMzZ87U1KlTdeutt2rSpEm6/PLLk/dMAXgTXW/gJxzvAAYQd6hfsmSJGhoatGLFCgUCARUVFamysrJ3oGtNTY0yMw9dAHjwwQcViUT0ta99rc9+Vq5cqdtuu02SdOONN6qjo0Pf/va31draqnPPPVeVlZVD6ncPwCdouYSftLVJ4bCUnW26EgAOk2FZlmW6iKEKhULKy8tTMBikfz3gJ5GI9B//kdhj587Vy4HZOmI4D5ByixfFNPbFtYnv4IILpIkTk1YPAGcbbM5N6+w3AJBUdEWAH3F1CkA/CPUA3IuZQOBHHPcA+kGoB+BehBv4Ecc9gH4Q6gG4F90Q4EeRiD1gFgAOQ6gH4E7t7fYsIIAf0VoP4AiEegDuRKiBn3GVCsARCPUA3IlQDz/j+AdwBEI9AHci1MDPWlqkaNR0FQAchFAPwH1iMeaoh79ZFucAgD4I9QDcp6XFDvaAn3G1CsBhCPUA3IcwA3AeAOiDUA/AfZj5A+A8ANAHoR6A+9BCCUidndKBA6arAOAQhHoA7tLVZd94CgCt9QB6EeoBuAut9MAhnA8A/oZQD8BdaJkEDuF8APA3hHoA7kLLJHBIczPTuwKQRKgH4CaWRagHDheNSsGg6SoAOAChHoB7BINST4/pKgBnoQsOABHqAbgJ4QU4GucFABHqAbgJXW+Ao3FeABChHoCb0CIJHK2tTQqHTVcBwDBCPQB3iESkUMh0FYAz0VoP+B6hHoA7EFqAgXEVC/A9Qj0AdyC0AAPj/AB8j1APwB0ILcDAmprs+zgA8C1CPQDn46ZTwLH19HATKsDnCPUAnC8Ukrq7TVcBOBtXswBfI9QDcD7CCnB8nCeArxHqATgfYQU4Ps4TwNcI9QCcj7ACHB83oQJ8jVAPwNm46RQweAwoB3yLUA/A2QgpwOBxVQvwLUI9AGcjpACDx/kC+BahHoCzEVKAweMmVIBvEeoBOJdlEeqBePT0SK2tpqsAYAChHoBzBYN2SAEweHwQBnyJUA/AuQgnQPw4bwBfItQDcK6GBtMVAO5DqAd8iVAPwLmYzhKIX3u71NVlugoAaUaoB+BMXV32HTIBxI/WesB3CPUAnIlQAiSOrmuA7xDqATgToR5IHOcP4DuEegDORCgBEtfcLMVipqsAkEaEegDOE4sxSBYYiljMDvYAfINQD8B5aGUEho6rXYCvEOoBOA9hBBg6BssCvkKoB+A8hHpg6DiPAF8h1ANwHloYgaHjXg+ArxDqATgLd8MEkofWesA3CPUAnIUQAiQP5xPgG4R6AM5C1xsgeTifAN8g1ANwFkIIkDzBoBSJmK4CQBoQ6gE4RyRihxAAyUMXHMAXCPUAnIPwASQf5xXgC4R6AM5B1xsg+TivAF8g1ANwDloUgeRrapJiMdNVAEgxQj0AZ4jF7PABILmiUam52XQVAFKMUA/AGZqb7fABIPnoggN4HqEegDPQ9QZIHc4vwPMI9QCcgZZEIHU4vwDPI9QDcAZCB5A64bAUCpmuAkAKEeoBmBcK2aEDQOrwwRnwNEI9APMIG0DqcZ4BnkaoB2AeYQNIPc4zwNMI9QDMI2wAqdfeLh04YLoKAClCqAdg1oEDdtgAkHp8gAY8i1APwCxCBpA+nG+AZyUU6levXq0pU6YoJydHxcXF2rBhw4DbbtmyRV/96lc1ZcoUZWRkaNWqVUdtc9tttykjI6PPMmvWrERKA+A2hAwgfTjfAM+KO9SvXbtW5eXlWrlypTZt2qS5c+eqtLRU9fX1/W7f2dmpadOm6c4771RBQcGA+/3sZz+r/fv39y6vvfZavKUBcKMB/nYASIGWFqm723QVAFIg7lB/77336uqrr1ZZWZlmz56tNWvWaOTIkXr44Yf73f6ss87Sz3/+c11xxRXKzs4ecL/Dhg1TQUFB7zJ+/Ph4SwPgNpGI1NpqugrAXxobTVcAIAXiCvWRSEQbN25USUnJoR1kZqqkpETV1dVDKmTbtm2aNGmSpk2bpm984xuqqakZcNtwOKxQKNRnAeBChAsg/bg6BnhSXKG+sbFR0WhU+fn5fdbn5+crEAgkXERxcbEeffRRVVZW6sEHH9TOnTt13nnnqa2trd/tKyoqlJeX17sUFhYm/LMBGET/XiD9OO8AT3LE7DcXX3yxvv71r2vOnDkqLS3VCy+8oNbWVv32t7/td/vly5crGAz2Lnv27ElzxQCSghZDIP2amqRo1HQVAJJsWDwbjx8/XllZWaqrq+uzvq6u7piDYOM1ZswYffrTn9b27dv7/X52dvYx++cDcIFoVGpuNl0F4D+xmB3sJ0wwXQmAJIqrpX748OGaN2+eqqqqetfFYjFVVVVp4cKFSSuqvb1dO3bs0MSJE5O2TwAO09hohwsA6cdVMsBz4mqpl6Ty8nItW7ZM8+fP14IFC7Rq1Sp1dHSorKxMkrR06VJNnjxZFRUVkuzBtR988EHv//ft26fNmzdr1KhRmjFjhiTphhtu0Je//GWdeuqpqq2t1cqVK5WVlaUrr7wyWc8TgNPQrxcwh/MP8Jy4Q/2SJUvU0NCgFStWKBAIqKioSJWVlb2DZ2tqapSZeegCQG1trc4888zer++55x7dc889Ov/887Vu3TpJ0t69e3XllVeqqalJJ510ks4991y98cYbOumkk4b49AA4Fi2FgDkHr5RlOmJoHYAkyLAsyzJdxFCFQiHl5eUpGAwqNzfXdDkAjicWk555xuxgvblz9XJgto4YIgSk3OJFMY19ca3pMqRFi6RPfcp0FQCOY7A5l4/oANKvuZnZNwDTuFoGeAqhHkD6ESYA8+hXD3gKoR5A+hHqAfPq6yX398AF8DeEegDpZVn2ID0AZnV3S62tpqsAkCSEegDp1dJihwkA5nHVDPAMQj2A9CJEAM7B+Qh4BqEeQHoRIgDnaGigXz3gEYR6AOljWcy4AThJOCyFQqarAJAEhHoA6dPaKkUipqsAcDjuwAZ4AqEeQPrQ9QZwHs5LwBMI9QDSh/AAOA/nJeAJhHoA6WFZhAfAicJhKRg0XQWAISLUA0iPYJD+9IBT8YEbcD1CPYD0IDQAzsX5CbgeoR5AejDDBuBcnJ+A6xHqAaQe/ekBZ6NfPeB6hHoAqcf89IDz0VoPuBqhHkDq0UoPOB/nKeBqhHoAqUcLIOB8dXV2VzkArkSoB5Ba9KcH3CESsbvKAXAlQj2A1Gppkbq7TVcBYDD4AA64FqEeQGrR9QZwD85XwLUI9QBSi5AAuEd9Pf3qAZci1ANInViMy/mAm3R3S83NpqsAkABCPYDUaWqSolHTVQCIB1fXAFci1ANIHcIB4D6ct4ArEeoBpA7hAHCfhga76xwAVyHUA0iNaFRqbDRdBYB4ce4CrkSoB5AatPYB7sVVNsB1CPUAUoNQALgX5y/gOoR6AKkRCJiuAECiGhulnh7TVQCIA6EeQPJFIlJLi+kqACTKsrjHBOAyhHoAyVdXx10pAbfjahvgKoR6AMlHf1zA/TiPAVch1ANIPlr4APdrbZW6ukxXAWCQCPUAkquzU2prM10FgGSgtR5wDUI9gOSilR7wDs5nwDUI9QCSixAAeAfnM+AahHoAyWNZhADASzo7pVDIdBUABoFQDyB5WlulcNh0FQCSiQ/qgCsQ6gEkD2/+gPdwXgOuQKgHkDy8+QPeU18vxWKmqwBwHIR6AMkRjXJbecCLurulpibTVQA4DkI9gORoaKA1D/AqrsIBjkeoB5Ac+/ebrgBAqhDqAccj1ANIDt70Ae9qapIiEdNVADgGQj2AoTtwwJ7OEoA3WZZUV2e6CgDHQKgHMHS00gPeRxc7wNEI9QCGjjd7wPs4zwFHI9QDGBrLoqUe8IPOTikUMl0FgAEQ6gEMTUuLFA6brgJAOtBaDzgWoR7A0PAmD/gH5zvgWIR6AEPDmzzgH/X19t2jATgOoR5A4iIRqbHRdBUA0iUatYM9AMch1ANIXCBgD5QF4B+1taYrANAPQj2AxNH1BvAfznvAkQj1ABLHmzvgP21tUnu76SoAHIFQDyAxra3SgQOmqwBgAh/oAcch1ANIDP1qAf/i/Acch1APIDG8qQP+VVfH1JaAwxDqAcSPqSwBf2NqS8BxCPUA4sdUlgC4Wgc4CqEeQPx4MwfA3wHAUYaZLgCAy1gWM1940IsvrtYf/vBzBYMBnXLKXC1bdr9mzFgw4PYdHa367W9/rDff/L3a25s1fvyp+uY3V+nMM78kSYrFonrmmdv017/+Wq2tAY0dO0lf+MJV+spXblFGRoYk6ZlnblN19dNqbt6jrKzhmjp1npYs+ZlmzChOy3PGELW3S6GQlJtruhIAItQDiFdzs9TVZboKJFF19Vr9+tfl+od/WKMZM4r1pz+t0p13luoXv9iqvLwJR23f0xNRRcUXlZs7Qddd94zGjZusxsbdGjlyTO82//mfd+m///tBXXPNYzr55M/qk0/e0q9+VaaRI/O0ePH/J0maOPHTuuqqBzRhwjR1dx/QCy/8UhUVi/TLX25Xbu5J6Xr6GIraWkI94BCEegDx4ZK757zwwr268MKrdcEFZZKkb31rjTZv/qNeffVhXXrpzUdtv27dw2pvb9Ztt72uYcNOkCSddNKUPtts2/a65s+/TGeeeUnv919//TfasWND7zaf//z/0+cx/+//e6/Wrft31dS8q9NPvyiZTxGpUlsrzZplugoAok89gHgR6j2lpyeinTs36vTTS3rXZWZm6vTTS7RtW3W/j9m48T81c+ZCPfLItfrud/N1442n67nn/o9isUNTHM6ceY7ef79K+/d/LEnavfsdbd36mubOvXjAOl5++V81cmSeTjllbhKfIVKqvl7q7jZdBQDRUg8gHgcO2N1v4BltbY2KxaLKy8vvsz4vL1+1tR/1+5j6+k/0wQcv6/Of/4ZuvPEF1dVt1yOPfE/RaLe++tWVkqRLL71ZBw6EdMMNs5SZmaVYLKq///uf6dxzv9FnX5s2/UH333+FIpFOjRkzUcuXv6Tc3PGpebJIPsuyZ8MqLDRdCeB7CbXUr169WlOmTFFOTo6Ki4u1YcOGAbfdsmWLvvrVr2rKlCnKyMjQqlWrhrxPAIbQSg9JlhVTbu4E/eM//qumTZunhQuX6LLLfqyqqjW927zxxm/1178+qWuvfUo/+9kmffe7j+mPf7xHf/nLY332NXv2haqo2Kzbbntdc+cu1n33/b2CQeY/d5V9+0xXAEAJhPq1a9eqvLxcK1eu1KZNmzR37lyVlpaqfoCbUHR2dmratGm68847VVBQkJR9AjCEN2/PGT16vDIzsxQM1vVZHwzWacyY/v9mjxkzUQUFn1ZmZlbvusmTP6PW1oB6eiKSpKee+qEuvfRmnXPOFTrllDN03nnf1MUX/5Oef76iz75yck5UQcEMzZx5tr797X9XZuYwrVv370l+lkip2lruWwE4QNyh/t5779XVV1+tsrIyzZ49W2vWrNHIkSP18MMP97v9WWedpZ///Oe64oorlJ2dnZR9AjAgGrUvs8NThg2zp5LcsqWqd10sFtOWLVWaOXNhv4/59Kc/r7q67YrFYr3r9u//WGPGTNSwYcMlSZFIpzIy+r7FZGZmybJiOhbLiqm7O5zo04EJ4TB3mAYcIK5QH4lEtHHjRpWU9B1QVVJSourq/gdUpWKf4XBYoVCozwIgxQIBO9jDc770pXK98spD+stfHtO+fR/q4YevUVdXh84/354N51/+Zamefnp57/Zf/OI16uho1uOPX6f9+z/W22//Uc8//3+0aNG1vdt87nNf1vPP/0xvv/1HNTTs0ptvPqsXXrhXZ531FUlSV1eHnn76R9q27Q01NOzWJ59s1K9+9Q9qadmns8/+enpfAAwdXfMA4+IaKNvY2KhoNKr8/L4DqvLz8/XRR/0PqErFPisqKvSTn/wkoZ8HIEG8aXvWwoVLFAo16JlnVqi1NaBTTy3SzTdX9g6ebWqqUWbmoTagT32qUDfd9Gf9+tf/pJtvnqOxYydr8eLrdOmlN/Vus2zZ/frd727VI498T8FgvcaOnaSLLvqO/u7vVkiyW+337/9Iq1Y9pra2Ro0a9SlNn36WVqz4H5188mfT+wJg6Pbtk+YyaxFgkitnv1m+fLnKy8t7vw6FQipk5D2QOpZFf3qPKy39vkpLv9/v9269dd1R6z796YX66U/fGHB/I0aM1tKlq7R06ap+vz98eI7+6Z9+n0ipcKJg0L7D7KhRpisBfCuuUD9+/HhlZWWprq7vgKq6uroBB8GmYp/Z2dkD9s8HkAItLfZ0lgAwkH37pNNOM10F4Ftx9akfPny45s2bp6qqvgOqqqqqtHBh/wOqTOwTQJLt3Wu6AgBOx98JwKi4u9+Ul5dr2bJlmj9/vhYsWKBVq1apo6NDZWX2gKqlS5dq8uTJqqiwpy2LRCL64IMPev+/b98+bd68WaNGjdKMGTMGtU8AhtH1BsDxNDRIkYg0fLjpSgBfijvUL1myRA0NDVqxYoUCgYCKiopUWVnZO9C1pqbvgKra2lqdeeaZvV/fc889uueee3T++edr3bp1g9onAIPa26XWVtNVAHA6y7IH1E+ZYroSwJcyLMv9d4wIhULKy8tTMBhUbm6u6XIAb9m6Vdq0yXQVyTd3rl4OzNYRw3mAlFu8KKaxL641XUZqFBZK555rugrAUwabc+O++RQAn6GfLIDB2r+f+1kAhhDqAQwsHLb7yQLAYPT0iMtfgBmEegADq621+8kCwGBxdQ8wglAPYGC8OQOI1969NAYABhDqAfSvp8fuHwsA8QiHpcZG01UAvkOoB9A/BrwBSNSePaYrAHyHUA+gf7wpA0gUfz+AtCPUAzhaLGYPkgWARHR2Ss3NpqsAfIVQD+BogYDU3W26CgBuRms9kFaEegBH480YwFDxdwRIK0I9gL5iMaayBDB0bW1SMGi6CsA3CPUA+qqvlyIR01UA8IKaGtMVAL5BqAfQF5fMASQLf0+AtCHUAzjEsngTBpA8waAUCpmuAvAFQj2AQ+rr7btBAkCy0AUHSAtCPYBDePMFkGz8XQHSglAPwGZZzHoDIPnoggOkBaEegK2+XurqMl0FAC+itR5IOUI9ABtvugBShb8vQMoR6gHYN5xi1hsAqRIMSq2tpqsAPI1QD0Cqq2PWGwCpRWs9kFKEegDS7t2mKwDgdfydAVKKUA/4XTTKrDcAUq+9XWpuNl0F4FmEesDvamul7m7TVQDwg127TFcAeBahHvA7LokDSJeaGvueGACSjlAP+Fl3t7Rvn+kqAPjFgQP2PTEAJB2hHvCzPXvs6SwBIF24OgikBKEe8DPeXAGkW02NPUAfQFIR6gG/OnDAnp8eANKpu9seoA8gqQj1gF/t3s2ANQBmMAsOkHSEesCveFMFYEptrRSJmK4C8BRCPeBHwaDU0mK6CgB+FYvZfesBJA2hHvCjnTtNVwDA7/g7BCQVoR7wG8ui6w0A8xobpbY201UAnkGoB/wmELBnvgEA02itB5KGUA/4DW+iAJxi1y5m4QKShFAP+El3t7R3r+kqAMDW0SHV15uuAvAEQj3gJ7t3cydHAM7yySemKwA8gVAP+AlvngCcZs8e+yoigCEh1AN+EQxKTU2mqwCAvqJR+yoigCEh1AN+QSs9AKfi7xMwZIR6wA9iMWa9AeBcTU321UQACSPUA36wb58UDpuuAgAGtmOH6QoAVyPUA37AmyUAp9u1y76qCCAhhHrA6zo6pP37TVcBAMcWDtsz4QBICKEe8DoGoAFwC64qAgkj1ANeZlm8SQJwj7o6qa3NdBWAKxHqAS/bt086cMB0FQAweNu3m64AcCVCPeBlvDkCcJudO+0bUgGIC6Ee8Kr2dgbIAnAfBswCCSHUA15FKz0At9q2zXQFgOsQ6gEvikaZ9QaAezU2Si0tpqsAXIVQD3jRnj3cQRaAu9FaD8SFUA940ccfm64AAIZm1y4pEjFdBeAahHrAa5qbpaYm01UAwNDQjRCIC6Ee8JqtW01XAADJsW2bfRM9AMdFqAe8pKtLqqkxXQUAJEd7u1Rba7oKwBUI9YCXbNsmxWKmqwCA5OHqIzAohHrAK2Ix5qYH4D11dVJrq+kqAMcj1ANesWuX3f0GALyG1nrguAj1gFfwpgfAq2i0AI6LUA94QSDA5WkA3hWLcTMq4DgI9YAXfPih6QoAILW2bbPnrgfQL0I94HatrXZLPQB4WTjMzaiAYyDUA25HKz0Av/joI25GBQyAUA+4WWentHu36SoAID3a26U9e0xXATgSoR5wM1qtAPjNBx+YrgBwJEI94FbhMDebAuA/LS2MIwL6kVCoX716taZMmaKcnBwVFxdrw4YNx9z+d7/7nWbNmqWcnBydccYZeuGFF/p8/6qrrlJGRkafZfHixYmUBvjH1q3MBAHAn7ZsMV0B4Dhxh/q1a9eqvLxcK1eu1KZNmzR37lyVlpaqvr6+3+1ff/11XXnllfrWt76lt99+W5dffrkuv/xyvf/++322W7x4sfbv39+7/OY3v0nsGQF+0N0tffyx6SoAwIz6eqmhwXQVgKPEHervvfdeXX311SorK9Ps2bO1Zs0ajRw5Ug8//HC/2//f//t/tXjxYv3whz/UZz7zGd1+++363Oc+pwceeKDPdtnZ2SooKOhdxo4dm9gzAvzg44/tYA8AfkVrPdBHXKE+Eolo48aNKikpObSDzEyVlJSourq638dUV1f32V6SSktLj9p+3bp1mjBhgk477TRdc801ampqGrCOcDisUCjUZwF8o7vbHiALAH62f7/U3Gy6CsAx4gr1jY2Nikajys/P77M+Pz9fgQEGrQQCgeNuv3jxYj3++OOqqqrSXXfdpVdffVUXX3yxogP0F66oqFBeXl7vUlhYGM/TANxt2zYpEjFdBQCYd0RXXsDPhpkuQJKuuOKK3v+fccYZmjNnjqZPn65169bpoosuOmr75cuXq7y8vPfrUChEsIc/9PTQSg8AB+3bZ7fWjxtnuhLAuLha6sePH6+srCzV1dX1WV9XV6eCgoJ+H1NQUBDX9pI0bdo0jR8/XtsHmK4vOztbubm5fRbAFz7+2J7KEgBgo7UekBRnqB8+fLjmzZunqqqq3nWxWExVVVVauHBhv49ZuHBhn+0l6aWXXhpwe0nau3evmpqaNHHixHjKA7yNvvQAcLR9+6RjjMMD/CLu2W/Ky8v10EMP6bHHHtOHH36oa665Rh0dHSorK5MkLV26VMuXL+/d/rrrrlNlZaV+8Ytf6KOPPtJtt92mt956S9///vclSe3t7frhD3+oN954Q7t27VJVVZUuu+wyzZgxQ6WlpUl6moAHbN1KKz0A9Ofdd01XABgXd5/6JUuWqKGhQStWrFAgEFBRUZEqKyt7B8PW1NQoM/PQZ4VzzjlHTz31lG655Rb96Ec/0syZM/Xcc8/p9NNPlyRlZWXp3Xff1WOPPabW1lZNmjRJixYt0u23367s7OwkPU3A5SIRWukBYCCBgD13/YQJpisBjMmwLMsyXcRQhUIh5eXlKRgM0r8e3vTOO9IHH5iuwlvmztXLgdk6YsgPkHKLF8U09sW1psvwnpNOko6YQhvwgsHm3Li73wBIswMH7K43AICBNTTY/esBnyLUA073/vvSAPdsAAAc5p13JPd3QAASQqgHnCwUknbsMF0FALhDMCjt3Gm6CsAIQj3gZJs30+oEAPF4912ubsKXCPWAU9XX0z8UAOJ14ACzhcGXCPWAE1mW9PbbpqsAAHf64AOpq8t0FUBaEeoBJ9q1S2puNl0FALhTTw83pILvEOoBp+npsWdwAAAkbscOqaXFdBVA2hDqAafZssXuEwoAGJqNG01XAKQNoR5wkrY2BngBQLI0NNjdGQEfINQDTrJxoxSLma4CALxj82apu9t0FUDKEeoBp9i7V9q/33QVAOAtBw5I771nugog5Qj1gBP09ND3EwBS5eOPpdZW01UAKUWoB5zgvfekzk7TVQCAN1mW9Oab3KEbnkaoB0xraZG2bjVdBQB4W2OjtH276SqAlBlmugDA1yxL2rDBMa1Hq198UT//wx8UCAY195RTdP+yZVowY0a/227Zu1crfvc7bdy5U7sbG/XLb35T1198cZ9tbnvmGf3k97/vs+60iRP10S9+0fv1jro63fDkk3pt61aFe3q0eM4c3X/VVcrPy0v+EwRwFF+d9++8I518sjRiRGp/DmAALfWASR995Jg7x66trlb5r3+tlX/3d9r0s59p7imnqPTOO1UfDPa7fWc4rGkTJujOK65QwZgxA+73syefrP3/8i+9y2srV/Z+r6OrS4sqKpSRkaGXf/xj/XXlSkV6evTln/9cMWYBAlLOd+d9d7fdDQfwIEI9YEoo5KjbmN/7wgu6+sILVXbBBZp98sla861vaWR2th5+9dV+tz9r+nT9/Bvf0BXnnKPsYQNf9BuWlaWCMWN6l/G5ub3f++vHH2tXQ4Me/c53dMYpp+iMU07RY9dco7d27tTLW7Yk/TkC6MuX5/2+fdLu3an/OUCaEeoBEyxLWr/eMXPSR3p6tHHnTpWcfnrvuszMTJWcfrqqt20b0r63BQKa9L3vadp11+kbDzygmsbG3u+Fu7uVkZGh7BNO6F2Xc8IJyszI0GuMMwBSytfn/VtvSV1d6flZQJoQ6gETPvrIHrTlEI1tbYrGYkf1Z83Py1NgCNPAFc+YoUe/8x1V3nyzHvyHf9DOhgad99Ofqu3AAUnS2TNn6sTsbN30m9+oMxxWR1eXbnjySUVjMe1n+jkgpXx93kci9ngmwEMI9UC6BYOO6naTShcXFenrZ5+tOaecotK5c/XCjTeqtaNDv33jDUnSSbm5+t111+m/Nm3SqH/4B+X94z+qtbNTn5syRZkZGYarB5AI15z3+/ZJn3ySvp8HpBiz3wDpFItJr7/umG43B40fPVpZmZmqO2JwXF0weMzBcPEac+KJ+vTEidpeV9e7btGcOdqxapUaQyENy8rSmBNPVME112jahAlJ+7kAjsZ5L2nTJmnCBGnUqPT+XCAFaKkH0umddxx5V8Phw4Zp3tSpqjpskFosFlPVli1aOHNm0n5Oe1eXdtTVaWI/gWF8bq7GnHiiXt6yRfWhkC6dNy9pPxfA0TjvZc+G88YbjplWGBgKWuqBdAkE7L70DlX+pS9p2Zo1mj9tmhZMn65Vf/qTOrq6VHb++ZKkpf/yL5o8bpwqrrhCkj3I7oO9e3v/v6+5WZt37dKonBzNKCiQJN3w5JP68uc+p1PHj1dtS4tWPvOMsjIzdeU55/T+3EfWrdNnJk/WSbm5qt62Tdc9/rj+6eKLddqkSWl+BQD/4byX1NAgbdkiHTZgGHAjQj2QDl1dUnW16SqOacnChWoIhbTimWcUaG1V0amnqvLmm3sH0dU0NSkz89DFvdqWFp35ox/1fn3PH/+oe/74R53/mc9o3a23SpL2NjXpyvvvV1N7u07KzdW5n/603vjpT3XSYdPbbd2/X8vXrlVze7umnHSSfnzZZfqnL30pTc8a8DfO+795/327Gw7d/uBiGZbl/mtOoVBIeXl5CgaDyj3sjwbgCJYlrVtnt9TDOebO1cuB2Tqsmy+QFosXxTT2xbWmy8CRRoyQLr5Yys42XQnQx2BzLn3qgVTbsoVADwBOd+CAPZGB+9s64VOEeiCV9u+X3nvPdBUAgMEIBOyuOIALEeqBVGlvt1t9AADu8f779hz2gMsQ6oFU6OmR/ud/7LsWAgDcpbpaCoVMVwHEhVAPJJtl2fMeO3A+egDAIHR30zAD1yHUA8n2/vvSnj2mqwAADEUoJP31rwychWsQ6oFk2rWLQVYA4BWBgLRxo+kqgEEh1APJUl8vrV9vugoAQDJt2+bou4EDBxHqgWQIBu3+l7GY6UoAAMn29tvS7t2mqwCOiVAPDFVnp/TKKwyoAgAve+MNbiQIRyPUA0PR1SW9/LJ9J0IAgHfFYvYV2cZG05UA/SLUA4mKROwW+rY205UAANKhp0d69VWppcV0JcBRCPVAIg4GeuaiBwB/Ofj3Pxg0XQnQB6EeiFckIq1bJzU3m64EAGBCOCxVVRHs4SiEeiAe4bDdQtPUZLoSAIBJB4M9XXHgEIR6YLAOHLD/gNNCDwCQDgV7Bs/CAQj1wGC0t0v//d9cagUA9NXdbc+Ctn+/6Urgc4R64Hiam6WXXrKDPQAAR4pG7Vlxdu40XQl8bJjpAgBH27dPev11exozAAAGYln2Dao6OqTTTzddDXyIUA8M5KOP7FuDAwAwWO+9Z9+/ZMECKSvLdDXwEUI9cKRoVHrrLemTT0xXAgBwo1277GB/3nnSiBGmq4FP0KceOFxHhz0glkAPABiKpiapslKqrzddCXyCUA8cVFtr/wFmykoAQDJ0ddkz43zwgd3nHkghut8AsZj0zjt2H3oAAJLJsuz3mPp66eyzpZwc0xXBo2iph78Fg9KLLxLoAQCptX+/9Kc/2bOqASlASz38ybLsIP/uu3ZLPQAAqdbVJf3lL9K0adKZZ0rDh5uuCB5CqIf/tLZKGzbYg5gAAEi3Tz6xW+7nz5dOPtl0NfAIQj38o6dH2rJF+vBDBiwBAMw6cED6n/+xQ/28edLIkaYrgssR6uEPNTX2jaQ6O01XAgDAIXv32q32n/2sNGsWN6xCwgj18LbGRjvMNzaargQAgP5Fo/YYrx07pLlzpVNOkTIyTFcFlyHUw5uCQftW3Xv2mK4EAIDB6eiQXn/dnshhzhxp4kTTFcFFCPXwlmDQ7je/e7fpSgAASExzs7RunXTSSdLpp0sFBaYrggsQ6uENTU32Hfv27jVdCQAAydHQIL3yijRunDR7tj2olm45GAChHu4Vi9k38di61f7DBwCAFzU3S6+9Jo0aJZ12mjR1qnTCCaargsMQ6uE+nZ32YKIdO+wpwQAA8IP2dmnjRumdd6QpU6QZM6SxY01XBYcg1MMdolG7a83OnfbUXwAA+FVPj7R9u72MHWu33J96qpSTY7oyGESoh3PFYnaAr6mxA31Pj+mKAABwlpYWe3n7bXtA7Smn2H3vhw83XRnSjFAPZ4lE7CC/b59UWyt1d5uuCAAA57Ms+/1z/35pwwYpP98O95Mnc7danyDUwyzLsgcABQL2H6LGRnsdAABIjGXZ76uBgPTWW1Jenj3nfUGBPU3mMOKfF/FbRXrFYnaIb2iQ6uvtf2mNBwAgdYJBe/noIykzU/rUp6QJE+yA/6lP0VXHIwj1SB3LkkIhu69fU5O9tLTYwR4AAKRfLGY3qB0+FXRenh3ux42zlzFjpKwsYyUiMYR6DJ1l2dNMhkL20tpqL8GgPWsNAABwroMt+Z98Yn+dkSGNHm2H+zFj7NCfm2vPk5+ZabJSHENCoX716tX6+c9/rkAgoLlz5+r+++/XggULBtz+d7/7nW699Vbt2rVLM2fO1F133aUvfelLvd+3LEsrV67UQw89pNbWVn3+85/Xgw8+qJkzZyZSHlIhGpU6Ouzw3t5+aGlrsxfCOwAA3nDwSnsoZM9Ad1BGhh3sR4+2/z24nHiiPRiXbjxGxR3q165dq/Lycq1Zs0bFxcVatWqVSktLtXXrVk2YMOGo7V9//XVdeeWVqqio0P/+3/9bTz31lC6//HJt2rRJp59+uiTp7rvv1n333afHHntMU6dO1a233qrS0lJ98MEHymHO1dSxLLs/ezgsdXXZSzhs39Dp8KWz014PAAD8y7IONeb154QTpBEj7IA/YsShJSfHXrKzDy0ZGemt3QcyLCu+qUaKi4t11lln6YEHHpAkxWIxFRYW6gc/+IFuvvnmo7ZfsmSJOjo69Ic//KF33dlnn62ioiKtWbNGlmVp0qRJ+ud//mfdcMMNkqRgMKj8/Hw9+uijuuKKK45bUygUUl5enoLBoHJzc+N5Ou5kWfac7dFo3397euyQfvDfw5dI5NC/kYgd0iMRZpqBf82dq5cDs1VXZ7oQ+M3iRTGNfXGt6TIAs4YP73854YS+y7Bhh/4dNszu63/4/33QHWiwOTeulvpIJKKNGzdq+fLlvesyMzNVUlKi6urqfh9TXV2t8vLyPutKS0v13HPPSZJ27typQCCgkpKS3u/n5eWpuLhY1dXV/Yb6cDis8GEtx6FQKJ6nkRqtrXaYtix7icUO/f/wrw//98j/H1yi0aP/PXwZahA/eOIAfjZypHJz6TmG9Bt2QoY0frzpMgDnONgw2dkZ/2MzMuxwfzDgH/n/zMyBl4yMgf89csnLc3x2iivUNzY2KhqNKj8/v8/6/Px8ffTRR/0+JhAI9Lt9IBDo/f7BdQNtc6SKigr95Cc/iaf01BszxnQFAOI0f4rpCuBPGdIXv2i6CAAe48prFsuXL1cwGOxd9uzZY7okAAAAwJi4Qv348eOVlZWluiM6odbV1amgoKDfxxQUFBxz+4P/xrPP7Oxs5ebm9lkAAAAAv4or1A8fPlzz5s1TVVVV77pYLKaqqiotXLiw38csXLiwz/aS9NJLL/VuP3XqVBUUFPTZJhQKaf369QPuEwAAAMAhcU9pWV5ermXLlmn+/PlasGCBVq1apY6ODpWVlUmSli5dqsmTJ6uiokKSdN111+n888/XL37xC11yySV6+umn9dZbb+lf//VfJUkZGRm6/vrrdccdd2jmzJm9U1pOmjRJl19+efKeKQAAAOBRcYf6JUuWqKGhQStWrFAgEFBRUZEqKyt7B7rW1NQo87Dphc455xw99dRTuuWWW/SjH/1IM2fO1HPPPdc7R70k3Xjjjero6NC3v/1ttba26txzz1VlZSVz1AMAAACDEPc89U7ku3nqAQAA4AuDzbmunP0GAAAAwCGEegAAAMDlCPUAAACAyxHqAQAAAJcj1AMAAAAuR6gHAAAAXI5QDwAAALgcoR4AAABwOUI9AAAA4HKEegAAAMDlCPUAAACAyxHqAQAAAJcj1AMAAAAuR6gHAAAAXI5QDwAAALgcoR4AAABwOUI9AAAA4HKEegAAAMDlCPUAAACAyxHqAQAAAJcj1AMAAAAuR6gHAAAAXI5QDwAAALjcMNMFJINlWZKkUChkuBIAAAAgeQ7m24N5dyCeCPVtbW2SpMLCQsOVAAAAAMnX1tamvLy8Ab+fYR0v9rtALBZTbW2tRo8erYyMjLT//FAopMLCQu3Zs0e5ublp//lux+uXOF67xPHaDQ2vX+J47RLHazc0vH6JM/naWZaltrY2TZo0SZmZA/ec90RLfWZmpk4++WTTZSg3N5eTZAh4/RLHa5c4Xruh4fVLHK9d4njthobXL3GmXrtjtdAfxEBZAAAAwOUI9QAAAIDLEeqTIDs7WytXrlR2drbpUlyJ1y9xvHaJ47UbGl6/xPHaJY7Xbmh4/RLnhtfOEwNlAQAAAD+jpR4AAABwOUI9AAAA4HKEegAAAMDlCPUAAACAyxHqAQAAAJcj1KdIOBxWUVGRMjIytHnzZtPluMall16qU045RTk5OZo4caK++c1vqra21nRZjrdr1y5961vf0tSpUzVixAhNnz5dK1euVCQSMV2aK/zsZz/TOeeco5EjR2rMmDGmy3G81atXa8qUKcrJyVFxcbE2bNhguiRX+Mtf/qIvf/nLmjRpkjIyMvTcc8+ZLsk1KioqdNZZZ2n06NGaMGGCLr/8cm3dutV0Wa7w4IMPas6cOb13Ql24cKH+9Kc/mS7Lle68805lZGTo+uuvN11Kvwj1KXLjjTdq0qRJpstwnQsvvFC//e1vtXXrVv3Hf/yHduzYoa997Wumy3K8jz76SLFYTL/61a+0ZcsW/fKXv9SaNWv0ox/9yHRprhCJRPT1r39d11xzjelSHG/t2rUqLy/XypUrtWnTJs2dO1elpaWqr683XZrjdXR0aO7cuVq9erXpUlzn1Vdf1bXXXqs33nhDL730krq7u7Vo0SJ1dHSYLs3xTj75ZN15553auHGj3nrrLf2v//W/dNlll2nLli2mS3OVN998U7/61a80Z84c06UMzELSvfDCC9asWbOsLVu2WJKst99+23RJrvX8889bGRkZViQSMV2K69x9993W1KlTTZfhKo888oiVl5dnugxHW7BggXXttdf2fh2NRq1JkyZZFRUVBqtyH0nWs88+a7oM16qvr7ckWa+++qrpUlxp7Nix1r/927+ZLsM12trarJkzZ1ovvfSSdf7551vXXXed6ZL6RUt9ktXV1enqq6/WE088oZEjR5oux9Wam5v15JNP6pxzztEJJ5xguhzXCQaDGjdunOky4CGRSEQbN25USUlJ77rMzEyVlJSourraYGXwm2AwKEn8jYtTNBrV008/rY6ODi1cuNB0Oa5x7bXX6pJLLunzt8+JCPVJZFmWrrrqKn33u9/V/PnzTZfjWjfddJNOPPFEfepTn1JNTY2ef/550yW5zvbt23X//ffrO9/5julS4CGNjY2KRqPKz8/vsz4/P1+BQMBQVfCbWCym66+/Xp///Od1+umnmy7HFd577z2NGjVK2dnZ+u53v6tnn31Ws2fPNl2WKzz99NPatGmTKioqTJdyXIT6Qbj55puVkZFxzOWjjz7S/fffr7a2Ni1fvtx0yY4y2NfvoB/+8Id6++239eKLLyorK0tLly6VZVkGn4E58b52krRv3z4tXrxYX//613X11Vcbqty8RF47AM537bXX6v3339fTTz9tuhTXOO2007R582atX79e11xzjZYtW6YPPvjAdFmOt2fPHl133XV68sknlZOTY7qc48qw/JqW4tDQ0KCmpqZjbjNt2jT9/d//vf7rv/5LGRkZveuj0aiysrL0jW98Q4899liqS3Wkwb5+w4cPP2r93r17VVhYqNdff92Xlwrjfe1qa2t1wQUX6Oyzz9ajjz6qzEz/fm5P5Lh79NFHdf3116u1tTXF1blTJBLRyJEj9cwzz+jyyy/vXb9s2TK1trZyVS0OGRkZevbZZ/u8jji+73//+3r++ef1l7/8RVOnTjVdjmuVlJRo+vTp+tWvfmW6FEd77rnn9JWvfEVZWVm966LRqDIyMpSZmalwONzne6YNM12AG5x00kk66aSTjrvdfffdpzvuuKP369raWpWWlmrt2rUqLi5OZYmONtjXrz+xWEySPUWoH8Xz2u3bt08XXnih5s2bp0ceecTXgV4a2nGH/g0fPlzz5s1TVVVVbxiNxWKqqqrS97//fbPFwdMsy9IPfvADPfvss1q3bh2BfohisZhv31fjcdFFF+m9997rs66srEyzZs3STTfd5KhALxHqk+qUU07p8/WoUaMkSdOnT9fJJ59soiRXWb9+vd58802de+65Gjt2rHbs2KFbb71V06dP92UrfTz27dunCy64QKeeeqruueceNTQ09H6voKDAYGXuUFNTo+bmZtXU1CgajfbeW2LGjBm95zFs5eXlWrZsmebPn68FCxZo1apV6ujoUFlZmenSHK+9vV3bt2/v/Xrnzp3avHmzxo0bd9T7B/q69tpr9dRTT+n555/X6NGje8dw5OXlacSIEYarc7bly5fr4osv1imnnKK2tjY99dRTWrdunf785z+bLs3xRo8efdS4jYNj/pw4noNQD8cYOXKkfv/732vlypXq6OjQxIkTtXjxYt1yyy3Kzs42XZ6jvfTSS9q+fbu2b99+1AdIetgd34oVK/p0jzvzzDMlSa+88oouuOACQ1U505IlS9TQ0KAVK1YoEAioqKhIlZWVRw2exdHeeustXXjhhb1fl5eXS7K7Lz366KOGqnKHBx98UJKOOh8feeQRXXXVVekvyEXq6+u1dOlS7d+/X3l5eZozZ47+/Oc/64tf/KLp0pBk9KkHAAAAXM7fnW4BAAAADyDUAwAAAC5HqAcAAABcjlAPAAAAuByhHgAAAHA5Qj0AAADgcoR6AAAAwOUI9QAAAIDLEeoBAAAAlyPUAwAAAC5HqAcAAABc7v8H1ZAXmGj1cIoAAAAASUVORK5CYII=",
+ "text/plain": [
+ "<Figure size 900x900 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Plot normal distribution areas*\n",
+ "\n",
+ "plt.rcParams[\"figure.figsize\"] = (9,9)\n",
+ " \n",
+ "plt.fill_between(x=np.arange(-4,-1,0.01), \n",
+ " y1= stats.norm.pdf(np.arange(-4,-1,0.01)) ,\n",
+ " facecolor='red',\n",
+ " alpha=0.35)\n",
+ "\n",
+ "plt.fill_between(x=np.arange(1,4,0.01), \n",
+ " y1= stats.norm.pdf(np.arange(1,4,0.01)) ,\n",
+ " facecolor='red',\n",
+ " alpha=0.35)\n",
+ "\n",
+ "plt.fill_between(x=np.arange(-1,1,0.01), \n",
+ " y1= stats.norm.pdf(np.arange(-1,1,0.01)) ,\n",
+ " facecolor='blue',\n",
+ " alpha=0.35)\n",
+ "\n",
+ "plt.text(x=-1.8, y=0.03, s= round(prob_under_minus1,3))\n",
+ "plt.text(x=-0.2, y=0.1, s= round(between_prob,3))\n",
+ "plt.text(x=1.4, y=0.03, s= round(prob_over_1,3));"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "83d97362-e52d-451f-88ae-20dcbb8be388",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "-1.9599639845400545\n",
+ "1.959963984540054\n"
+ ]
+ }
+ ],
+ "source": [
+ "print( stats.norm.ppf(q=0.025) ) # Find the quantile for the 2.5% cutoff\n",
+ "\n",
+ "print( stats.norm.ppf(q=0.975) ) # Find the quantile for the 97.5% cutoff"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d9f5f575-10a6-4555-93de-ce62aae89e53",
+ "metadata": {},
+ "source": [
+ "# test"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "e1d008da-bfb7-4586-80d0-887396d5d8f1",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "43.000112\n",
+ "39.26\n"
+ ]
+ }
+ ],
+ "source": [
+ "np.random.seed(6)\n",
+ "\n",
+ "population_ages1 = stats.poisson.rvs(loc=18, mu=35, size=150000)\n",
+ "population_ages2 = stats.poisson.rvs(loc=18, mu=10, size=100000)\n",
+ "population_ages = np.concatenate((population_ages1, population_ages2))\n",
+ "\n",
+ "minnesota_ages1 = stats.poisson.rvs(loc=18, mu=30, size=30)\n",
+ "minnesota_ages2 = stats.poisson.rvs(loc=18, mu=10, size=20)\n",
+ "minnesota_ages = np.concatenate((minnesota_ages1, minnesota_ages2))\n",
+ "\n",
+ "print( population_ages.mean() )\n",
+ "print( minnesota_ages.mean() )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "ce5c9504-be67-4c17-b834-3d92bb4f5127",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "TtestResult(statistic=np.float64(-2.5742714883655027), pvalue=np.float64(0.013118685425061678), df=np.int64(49))"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "stats.ttest_1samp(a = minnesota_ages, # Sample data\n",
+ " popmean = population_ages.mean()) # Pop mean"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "e2e66426-430a-49b0-91a9-a1ddd2145b98",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "np.float64(-2.0095752371292397)"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "stats.t.ppf(q=0.025, # Quantile to check\n",
+ " df=49) # Degrees of freedom\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "e2b5a4c0-6702-46f0-846a-22329fa48c6b",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "np.float64(2.0095752371292397)"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "stats.t.ppf(q=0.975, # Quantile to check\n",
+ " df=49) # Degrees of freedom"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "eff91076-2f15-46c4-a984-80bfe5de37cf",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "np.float64(0.013121066545690117)"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "stats.t.cdf(x= -2.5742, # T-test statistic\n",
+ " df= 49) * 2 # Multiply by two for two tailed test *"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "53ca8047-26f3-4bde-bc47-b59dca8a26ad",
+ "metadata": {},
+ "source": [
+ "# prediction"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "id": "bd3556a5-d27c-44c9-812c-72446639a374",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 900x900 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Load mtcars data set\n",
+ "mtcars = pd.read_csv(\"./mtcars.csv\")\n",
+ "\n",
+ "mtcars.plot(kind=\"scatter\",\n",
+ " x=\"wt\",\n",
+ " y=\"mpg\",\n",
+ " figsize=(9,9),\n",
+ " color=\"black\");"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "id": "2cb8f194-703e-4617-ab03-e813476c80af",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "37.28512616734204\n",
+ "[-5.34447157]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Initialize model\n",
+ "regression_model = linear_model.LinearRegression()\n",
+ "\n",
+ "# Train the model using the mtcars data\n",
+ "regression_model.fit(X = pd.DataFrame(mtcars[\"wt\"]), \n",
+ " y = mtcars[\"mpg\"])\n",
+ "\n",
+ "# Check trained model y-intercept\n",
+ "print(regression_model.intercept_)\n",
+ "\n",
+ "# Check trained model coefficients\n",
+ "print(regression_model.coef_)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "id": "4372ef0d-c863-4343-8c9b-87aaeabef010",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.7528327936582646"
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "regression_model.score(X = pd.DataFrame(mtcars[\"wt\"]), \n",
+ " y = mtcars[\"mpg\"])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "id": "b3d02c7d-27f3-4034-b129-4eafc1d26877",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "count 3.200000e+01\n",
+ "mean -8.215650e-15\n",
+ "std 2.996352e+00\n",
+ "min -4.543151e+00\n",
+ "25% -2.364709e+00\n",
+ "50% -1.251956e-01\n",
+ "75% 1.409561e+00\n",
+ "max 6.872711e+00\n",
+ "Name: mpg, dtype: float64"
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train_prediction = regression_model.predict(X = pd.DataFrame(mtcars[\"wt\"]))\n",
+ "\n",
+ "# Actual - prediction = residuals\n",
+ "residuals = mtcars[\"mpg\"] - train_prediction\n",
+ "\n",
+ "residuals.describe()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "56203d33-4be7-43c2-893b-2935c57936cf",
+ "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": "3.12.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
--- /dev/null
+model,mpg,cyl,disp,hp,drat,wt,qsec,vs,am,gear,carb
+Mazda RX4,21,6,160,110,3.9,2.62,16.46,0,1,4,4
+Mazda RX4 Wag,21,6,160,110,3.9,2.875,17.02,0,1,4,4
+Datsun 710,22.8,4,108,93,3.85,2.32,18.61,1,1,4,1
+Hornet 4 Drive,21.4,6,258,110,3.08,3.215,19.44,1,0,3,1
+Hornet Sportabout,18.7,8,360,175,3.15,3.44,17.02,0,0,3,2
+Valiant,18.1,6,225,105,2.76,3.46,20.22,1,0,3,1
+Duster 360,14.3,8,360,245,3.21,3.57,15.84,0,0,3,4
+Merc 240D,24.4,4,146.7,62,3.69,3.19,20,1,0,4,2
+Merc 230,22.8,4,140.8,95,3.92,3.15,22.9,1,0,4,2
+Merc 280,19.2,6,167.6,123,3.92,3.44,18.3,1,0,4,4
+Merc 280C,17.8,6,167.6,123,3.92,3.44,18.9,1,0,4,4
+Merc 450SE,16.4,8,275.8,180,3.07,4.07,17.4,0,0,3,3
+Merc 450SL,17.3,8,275.8,180,3.07,3.73,17.6,0,0,3,3
+Merc 450SLC,15.2,8,275.8,180,3.07,3.78,18,0,0,3,3
+Cadillac Fleetwood,10.4,8,472,205,2.93,5.25,17.98,0,0,3,4
+Lincoln Continental,10.4,8,460,215,3,5.424,17.82,0,0,3,4
+Chrysler Imperial,14.7,8,440,230,3.23,5.345,17.42,0,0,3,4
+Fiat 128,32.4,4,78.7,66,4.08,2.2,19.47,1,1,4,1
+Honda Civic,30.4,4,75.7,52,4.93,1.615,18.52,1,1,4,2
+Toyota Corolla,33.9,4,71.1,65,4.22,1.835,19.9,1,1,4,1
+Toyota Corona,21.5,4,120.1,97,3.7,2.465,20.01,1,0,3,1
+Dodge Challenger,15.5,8,318,150,2.76,3.52,16.87,0,0,3,2
+AMC Javelin,15.2,8,304,150,3.15,3.435,17.3,0,0,3,2
+Camaro Z28,13.3,8,350,245,3.73,3.84,15.41,0,0,3,4
+Pontiac Firebird,19.2,8,400,175,3.08,3.845,17.05,0,0,3,2
+Fiat X1-9,27.3,4,79,66,4.08,1.935,18.9,1,1,4,1
+Porsche 914-2,26,4,120.3,91,4.43,2.14,16.7,0,1,5,2
+Lotus Europa,30.4,4,95.1,113,3.77,1.513,16.9,1,1,5,2
+Ford Pantera L,15.8,8,351,264,4.22,3.17,14.5,0,1,5,4
+Ferrari Dino,19.7,6,145,175,3.62,2.77,15.5,0,1,5,6
+Maserati Bora,15,8,301,335,3.54,3.57,14.6,0,1,5,8
+Volvo 142E,21.4,4,121,109,4.11,2.78,18.6,1,1,4,2
projects / linuxyz.git / commitdiff