{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "remove_input"
    ]
   },
   "outputs": [],
   "source": [
    "path_data = '../../data/'\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('fivethirtyeight')\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "remove_input"
    ]
   },
   "outputs": [],
   "source": [
    "\n",
    "def standard_units(any_numbers):\n",
    "    \"Convert any array of numbers to standard units.\"\n",
    "    return (any_numbers - np.mean(any_numbers))/np.std(any_numbers)  \n",
    "\n",
    "def correlation(t, x, y):\n",
    "    return np.mean(standard_units(t[x])*standard_units(t[y]))\n",
    "\n",
    "def slope(table, x, y):\n",
    "    r = correlation(table, x, y)\n",
    "    return r * np.std(table[y])/np.std(table[x])\n",
    "\n",
    "def intercept(table, x, y):\n",
    "    a = slope(table, x, y)\n",
    "    return np.mean(table[y]) - a * np.mean(table[x])\n",
    "\n",
    "def fit(table, x, y):\n",
    "    a = slope(table, x, y)\n",
    "    b = intercept(table, x, y)\n",
    "    return a * table[x] + b\n",
    "\n",
    "def scatter_fit(table, x, y):\n",
    "    #fig, ax = plt.subplots(figsize=(7,6))\n",
    "    plt.scatter(table[x], \n",
    "               table[y],  \n",
    "               color='darkblue',\n",
    "               s=20)\n",
    "    \n",
    "    plt.plot(table[x], fit(table, x, y), lw=2, color='gold')\n",
    "    plt.xlabel(x)\n",
    "    plt.ylabel(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A Regression Model\n",
    "\n",
    "In brief, such models say that the underlying relation between the two variables is perfectly linear; this straight line is the *signal* that we would like to identify. However, we are not able to see the line clearly. What we see are points that are scattered around the line. In each of the points, the signal has been contaminated by *random noise*. Our inferential goal, therefore, is to separate the signal from the noise.\n",
    "\n",
    "In greater detail, the regression model specifies that the points in the scatter plot are generated at random as follows.\n",
    "\n",
    "- The relation between $x$ and $y$ is perfectly linear. We cannot see this \"true line\" but it exists.\n",
    "- The scatter plot is created by taking points on the line and pushing them off the line vertically, either above or below, as follows:\n",
    "    - For each $x$, find the corresponding point on the true line (that's the signal), and then generate the noise or error.\n",
    "    - The errors are drawn at random with replacement from a population of errors that has a normal distribution with mean 0.\n",
    "    - Create a point whose horizontal coordinate is $x$ and whose vertical coordinate is \"the height of the true line at $x$, plus the error\".\n",
    "- Finally, erase the true line from the scatter, and display just the points created."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on this scatter plot, how should we estimate the true line? The best line that we can put through a scatter plot is the regression line. So the regression line is a natural estimate of the true line. \n",
    "\n",
    "The simulation below shows how close the regression line is to the true line. The first panel shows how the scatter plot is generated from the true line. The second shows the scatter plot that we see. The third shows the regression line through the plot. The fourth shows both the regression line and the true line.\n",
    "\n",
    "To run the simulation, call the function `draw_and_compare` with three arguments: the slope of the true line, the intercept of the true line, and the sample size.\n",
    "\n",
    "Run the simulation a few times, with different values for the slope and intercept of the true line, and varying sample sizes. Because all the points are generated according to the model, you will see that the regression line is a good estimate of the true line if the sample size is moderately large."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": [
     "remove_input"
    ]
   },
   "outputs": [],
   "source": [
    "def draw_and_compare(true_slope, true_int, sample_size):\n",
    "    x = np.random.normal(50, 5, sample_size)\n",
    "    xlims = np.array([np.min(x), np.max(x)])\n",
    "    eps = np.random.normal(0, 6, sample_size)\n",
    "    y = (true_slope*x + true_int) + eps\n",
    "    tyche = pd.DataFrame({'x':x,'y':y})\n",
    "\n",
    "    plt.figure(figsize=(6, 16))\n",
    "    \n",
    "    plt.subplot(4, 1, 1)\n",
    "    plt.scatter(tyche['x'], tyche['y'], s=20)\n",
    "    plt.plot(xlims, true_slope*xlims + true_int, lw=2, color='green')\n",
    "    plt.title('True Line, and Points Created')\n",
    "\n",
    "#---\n",
    "    plt.subplot(4, 1, 2)\n",
    "    plt.scatter(tyche['x'], tyche['y'], s=20)\n",
    "    plt.title('What We Get to See')\n",
    "    \n",
    "#---\n",
    "    plt.subplot(4, 1, 3)\n",
    "    scatter_fit(tyche, 'x', 'y')\n",
    "    plt.xlabel(\"\")\n",
    "    plt.ylabel(\"\")\n",
    "    plt.title('Regression Line: Estimate of True Line')\n",
    "#---\n",
    "    plt.subplot(4, 1, 4)\n",
    "    scatter_fit(tyche, 'x', 'y')\n",
    "    \n",
    "    xlims = np.array([np.min(tyche['x']), np.max(tyche['x'])])\n",
    "    plt.plot(xlims, true_slope*xlims + true_int, lw=2, color='green')\n",
    "    plt.ylabel(\"\")\n",
    "    plt.title(\"Regression Line and True Line\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x1152 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The true line,\n",
    "# the points created,\n",
    "# and our estimate of the true line.\n",
    "# Arguments: true slope, true intercept, number of points\n",
    "\n",
    "draw_and_compare(4, -5, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In reality, of course, we will never see the true line. What the simulation shows that if the regression model looks plausible, and if we have a large sample, then the regression line is a good approximation to the true line."
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}