Difference between revisions of "Two Independent Sample Wilcoxon Test"
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public static void main(String args[]) { | public static void main(String args[]) { | ||
− | double[] | + | double[] a = |
{79.98, 80.04, 80.02, 80.04, 80.03, 80.03, | {79.98, 80.04, 80.02, 80.04, 80.03, 80.03, | ||
80.04, 79.97, 80.05, 80.03, 80.02, 80.00, 80.02}; | 80.04, 79.97, 80.05, 80.03, 80.02, 80.00, 80.02}; | ||
− | double[] | + | double[] b = |
{80.02, 79.94, 79.98, 79.97, 79.97, 80.03, 79.95, 79.97}; | {80.02, 79.94, 79.98, 79.97, 79.97, 80.03, 79.95, 79.97}; | ||
Line 33: | Line 33: | ||
try { | try { | ||
TwoIndependentWilcoxonResult result = | TwoIndependentWilcoxonResult result = | ||
− | (TwoIndependentWilcoxonResult)data.modelTwoIndependentWilcoxon( | + | (TwoIndependentWilcoxonResult)data.modelTwoIndependentWilcoxon(a,b); |
− | + | ||
− | + | if (result != null) { | |
− | + | // Getting the model's parameter estiamtes and statistics. | |
− | |||
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double meanX = result.getMeanX(); | double meanX = result.getMeanX(); | ||
double meanY = result.getMeanY(); | double meanY = result.getMeanY(); |
Revision as of 16:12, 31 July 2006
/* July 2006. Annie Che <chea@stat.ucla.edu>. UCLA Statistics. Source of example data: Mathematical Statistics and Data Analysis, John Rice, Second Edition. Page 390, example A, determination of the laten heat of fusion of ice. */ 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.result.TwoIndependentWilcoxonResult; public class TwoIndependentWilcoxonExample { public static void main(String args[]) { double[] a = {79.98, 80.04, 80.02, 80.04, 80.03, 80.03, 80.04, 79.97, 80.05, 80.03, 80.02, 80.00, 80.02}; double[] b = {80.02, 79.94, 79.98, 79.97, 79.97, 80.03, 79.95, 79.97}; // you'll need to instantiate a data instance first. Data data = new Data(); // then use the following line to get the result. try { TwoIndependentWilcoxonResult result = (TwoIndependentWilcoxonResult)data.modelTwoIndependentWilcoxon(a,b); if (result != null) { // Getting the model's parameter estiamtes and statistics. double meanX = result.getMeanX(); double meanY = result.getMeanY(); System.out.println("meanX = " + meanX); System.out.println("meanY = " + meanY); double rankSumSmall = result.getRankSumSmallerGroup(); double rankSumLarge = result.getRankSumLargerGroup(); System.out.println("rankSumSmall = " + rankSumSmall); System.out.println("rankSumLarge = " + rankSumLarge); double uStatSmall = result.getUStatSmallerGroup(); double uStatLarge = result.getUStatLargerGroup(); System.out.println("uStatSmall = " + uStatSmall); System.out.println("uStatLarge = " + uStatLarge); double meanU = result.getMeanU(); double varU = result.getVarianceU(); System.out.println("meanU = " + meanU); System.out.println("varU = " + varU); double zScore = result.getZScore(); String pValue = result.getPValue(); boolean isLargeSample = result.isLargeSample(); System.out.println("zScore = " + zScore); System.out.println("pValue = " + pValue); System.out.println("isLargeSample = " + isLargeSample); } } catch (Exception e) { System.out.println(e); } } }