Simple 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.SimpleLinearRegressionResult;
public class SimpleLinearRegressionExample {
public static void main(String args[]) {
double[] midtermGrade =
{68,49,60,68,97,82,59,50,73,39,71,95,61,72,87,40,66,58,58,77};
double[] finalGrade =
{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(midtermGrade, DataType.QUANTITATIVE);
data.addResponse(finalGrade, DataType.QUANTITATIVE);
try {
SimpleLinearRegressionResult result = data.modelSimpleLinearRegression();
if (result != null) {
// Getting the model's parameter estiamtes and statistics.
double alpha = result.getAlpha();
double beta = result.getBeta();
double meanX = result.getMeanX();
double meanY = result.getMeanY();
double seAlpha = result.getAlphaSE();
double seBeta = result.getBetaSE();
double tStatAlpha = result.getAlphaTStat();
double tStatBeta = result.getBetaTStat();
/* to avoid cases such "p-value < 0.0001" sometimes generated
by R, String is used for p-values. */
String pvAlpha = result.getAlphaPValue();
String pvBeta = result.getBetaPValue();
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("intercept = " + alpha);
System.out.println("slope = " + beta);
System.out.println("meanX = " + meanX);
System.out.println("meanY = " + meanY);
System.out.println("seAlpha = " + seAlpha);
System.out.println("seBeta = " + seBeta);
System.out.println("tStatAlpha = " + tStatAlpha);
System.out.println("tStatBeta = " + tStatBeta);
System.out.println("pvAlpha = " + pvAlpha);
System.out.println("pvBeta = " + pvBeta);
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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