Simple regression code-Hours vs scores.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ff266de0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "def estimate_coef(x, y):\n",
    "    # number of observations/points\n",
    "    n = np.size(x)\n",
    " \n",
    "    # mean of x and y vector\n",
    "    m_x = np.mean(x)\n",
    "    m_y = np.mean(y)\n",
    " \n",
    "    # calculating cross-deviation and deviation about x\n",
    "    SS_xy = np.sum(y*x) - n*m_y*m_x\n",
    "    SS_xx = np.sum(x*x) - n*m_x*m_x\n",
    " \n",
    "    # calculating regression coefficients\n",
    "    b_1 = SS_xy / SS_xx\n",
    "    b_0 = m_y - b_1*m_x\n",
    " \n",
    "    return (b_0, b_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e3125c8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_regression_line(x, y, b):\n",
    "    # plotting the actual points as scatter plot\n",
    "    plt.scatter(x, y, color = \"m\",\n",
    "               marker = \"o\", s = 30)\n",
    " \n",
    "    # predicted response vector\n",
    "    y_pred = b[0] + b[1]*x\n",
    " \n",
    "    # plotting the regression line\n",
    "    plt.plot(x, y_pred, color = \"g\")\n",
    " \n",
    "    # putting labels\n",
    "    plt.xlabel('x')\n",
    "    plt.ylabel('y')\n",
    " \n",
    "    # function to show plot\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "687783bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def main():\n",
    "    # observations / data\n",
    "    x = np.array([2.5,5.1,3.2,8.5,3.5,1.5,9.2,5.5,8.3,2.7,7.7,5.9,4.5,3.3,1.1,8.9,2.5,1.9,6.1,7.4,2.7,4.8,3.8,6.9,7.8])\n",
    "    y = np.array([21,47,27,75,30,20,88,60,81,25,85,62,41,42,17,95,30,24,67,69,30,54,35,76,86])\n",
    " \n",
    "    # estimating coefficients\n",
    "    b = estimate_coef(x, y)\n",
    "    print(\"Estimated coefficients:\\nb_0 = {}  \\\n",
    "          \\nb_1 = {}\".format(b[0], b[1]))\n",
    " \n",
    "    # plotting regression line\n",
    "    plot_regression_line(x, y, b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7e5f6b02",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated coefficients:\n",
      "b_0 = 2.4836734053732172            \n",
      "b_1 = 9.775803390787466\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "if __name__ == \"__main__\":\n",
    "    main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9efd70f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}