ピアソンの積率相関係数

PearsonCorrelationAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = np.array([1018.4, 1007.6, 1006.2, 1009.9, 1010.8, 1013.2, 1016.2, 1009.1, 1003.1, 1012.5, 1006.4, 1006.3, 1012.2, 1015.0, 1017.4, 1016.5, 1012.1, 1008.7, 1009.2, 1009.2])\n",
    "Y = np.array([1019.4, 1005.7, 1002.0, 1006.7, 1005.1, 1010.1, 1016.7, 1011.0, 999.5, 1006.9, 1001.9, 1007.5, 1014.4, 1014.3, 1014.6, 1009.0, 1006.7, 1009.4, 1011.8, 1009.4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(995, 1020)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1157378d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X, Y)\n",
    "plt.xlabel(\"(x)\")\n",
    "plt.ylabel(\"(y)\")\n",
    "plt.xlim(1000, 1020)\n",
    "plt.ylim(995, 1020)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pearson correlation coefficient: 0.8029714610889633\n"
     ]
    }
   ],
   "source": [
    "r_xy = np.sum((X - np.mean(X)) * (Y - np.mean(Y))) / (np.sqrt(np.sum((X - np.mean(X)) ** 2)) * np.sqrt(np.sum((Y - np.mean(Y)) ** 2)))\n",
    "print(\"pearson correlation coefficient: {}\".format(r_xy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t: 5.71580452569847\n"
     ]
    }
   ],
   "source": [
    "t = r_xy * np.sqrt(X.shape[0] - 2) / np.sqrt(1 - r_xy ** 2)\n",
    "print(\"t: {}\".format(t))"
   ]
  }
 ],
 "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": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}