IvoDinov at 05:46, 19 January 2007

2007-01-19T05:46:47Z

Annie at 21:04, 31 July 2006

2006-07-31T21:04:00Z

Annie at 21:03, 31 July 2006

2006-07-31T21:03:33Z

New page

/*

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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Simple Linear Regression - Revision history

IvoDinov at 05:46, 19 January 2007

Annie at 21:04, 31 July 2006

Annie at 21:03, 31 July 2006