Linear Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "14a0a3c3-3d53-4e89-b45e-02357e7f4887",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13\n",
      "13\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Linear Regression\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "\n",
    "x = [5,7,8,7,2,17,2,9,4,11,12,9,6]\n",
    "print(len(x))\n",
    "\n",
    "y = [99,86,87,88,111,86,103,87,94,78,77,85,86]\n",
    "print(len(y))\n",
    "\n",
    "# Method 1\n",
    "plt.scatter(x, y)\n",
    "plt.show()\n",
    "\n",
    "# Method 2\n",
    "slope, intercept, r, p, std_err = stats.linregress(x, y)\n",
    "\n",
    "def myfunc(x):\n",
    "  return slope * x + intercept\n",
    "\n",
    "mymodel = list(map(myfunc, x))\n",
    "\n",
    "plt.scatter(x, y)\n",
    "plt.plot(x, mymodel)\n",
    "plt.show()"
   ]
  },
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   "id": "cee0b572-042a-4351-bd6f-47da0fe98f68",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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