Multiple Linear Regression
/* July 2006. Annie Che <chea@stat.ucla.edu>. UCLA Statistics. Source of example data: An Introduction to Computational Statitics by Robert I Jennrich, Page 5, example of regression on students' midterm and final scores. */ package edu.ucla.stat.SOCR.analyses.example; import java.util.HashMap; import edu.ucla.stat.SOCR.analyses.data.Data; import edu.ucla.stat.SOCR.analyses.data.DataType; import edu.ucla.stat.SOCR.analyses.result.MultiLinearRegressionResult; public class MultiLinearRegressionExample { public static void main(String args[]) { double[] x1 = {68,49,60,68,97,82,59,50,73,39,71,95,61,72,87,40,66,58,58,77}; double[] x2 = {60,94,91,81,80,92,74,89,96,87,86,94,94,94,79,50,92,82,94,78}; double[] y = {75,63,57,88,88,79,82,73,90,62,70,96,76,75,85,40,74,70,75,72}; // you'll need to instantiate a data instance first. Data data = new Data(); /********************************************************************* then put the data into the Data Object. append the predictor data using method "addPredictor". append the response data using method "addResponse". **********************************************************************/ data.addPredictor("var 1", x1, DataType.QUANTITATIVE); data.addPredictor("var 2", x2, DataType.QUANTITATIVE); data.addResponse("var y", y, DataType.QUANTITATIVE); try { MultiLinearRegressionResult result = data.modelMultiLinearRegression(); if (result != null) { // Getting the model's parameter estiamtes and statistics. String[] varList = result.getVariableList(); double[] beta = result.getBeta(); double[] seBeta =result.getBetaSE(); double[] tStat = result.getBetaTStat(); String[] pValue = result.getBetaPValue(); int dfError = result.getDF(); double[] predicted = result.getPredicted(); double[] residuals = result.getResiduals(); // residuals after being sorted ascendantly. double[] sortedResiduals = result.getSortedResiduals(); // sortedResiduals after being standardized. double[] sortedStandardizedResiduals = result.getSortedStandardizedResiduals(); // the original index of sortedResiduals, stored as integer array. int[] sortedResidualsIndex = result.getSortedResidualsIndex(); // the normal quantiles of sortedResiduals. double[] sortedNormalQuantiles = result.getSortedNormalQuantiles(); // sortedNormalQuantiles after being standardized. double[] sortedStandardizedNormalQuantiles = result.getSortedStandardizedNormalQuantiles(); System.out.println("dfError = " + dfError); for (int i = 0; i < varList.length; i++) { System.out.println("varList["+i+"] = " + varList[i]); } for (int i = 0; i < beta.length; i++) { System.out.println("beta["+i+"] = " + beta[i]); } for (int i = 0; i < residuals.length; i++) { System.out.println("residuals["+i+"] = " + residuals[i]); } } } catch (Exception e) { System.out.println(e); } } }
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