{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stats initialized\n",
      "\n",
      "Linear Regression Line:\n",
      "\tEstimated offset is:  1.474039\n",
      "\tEstimated slope is:  3.000136\n",
      "\tR^2 is:  0.999989\n"
     ]
    }
   ],
   "source": [
    "import stats from \"https://l12.xyz/x/shortcuts/raw/stat/mod.ts\";\n",
    "\n",
    "const xs = [];\n",
    "const ys = [];\n",
    "\n",
    "for (let i = 0; i < 100; i++) {\n",
    "  xs.push(i);\n",
    "  ys.push((1 + 3 * i) + Math.random());\n",
    "}\n",
    "\n",
    "const linreg = stats.LinearRegression(xs, ys, [], false);\n",
    "const r = stats.RSquared(xs, ys, [], linreg.alpha, linreg.beta);\n",
    "\n",
    "console.log(\"\\nLinear Regression Line:\");\n",
    "console.log(\"\\tEstimated offset is: \", linreg.alpha.toFixed(6));\n",
    "console.log(\"\\tEstimated slope is: \", linreg.beta.toFixed(6));\n",
    "console.log(\"\\tR^2 is: \", r.toFixed(6));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       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)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import plot from \"../plot/mod.ts?5\";\n",
    "\n",
    "plot.DrawHist(ys, 16, { title : \"Histogram of Y values\" });\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Deno",
   "language": "typescript",
   "name": "deno"
  },
  "language_info": {
   "codemirror_mode": "typescript",
   "file_extension": ".ts",
   "mimetype": "text/x.typescript",
   "name": "typescript",
   "nbconvert_exporter": "script",
   "pygments_lexer": "typescript",
   "version": "5.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}