@Test public void testXbarVariance() { logger.info("xbarVariance"); FlatDataCollection flatDataCollection = generateFlatDataCollection(); double expResult = 405.75; double result = SystematicSampling.xbarVariance(flatDataCollection); assertEquals(expResult, result, Constants.DOUBLE_ACCURACY_HIGH); }
public static double xbarVariance(FlatDataCollection flatDataCollection) { double n = flatDataCollection.size(); if(n<=1) { throw new IllegalArgumentException("The provided collection must have more than 1 elements."); } Double previousValue = null; double xbarVariance = 0.0; Iterator<Double> it = flatDataCollection.iteratorDouble(); while(it.hasNext()) { Double yi = it.next(); if(previousValue!=null) { double delta = yi - previousValue; xbarVariance+=delta*delta; } previousValue = yi; } xbarVariance/=(2*n*(n-1)); return xbarVariance; }
SystematicSampling { public static double xbarVariance(FlatDataCollection flatDataCollection) { double n = flatDataCollection.size(); if(n<=1) { throw new IllegalArgumentException("The provided collection must have more than 1 elements."); } Double previousValue = null; double xbarVariance = 0.0; Iterator<Double> it = flatDataCollection.iteratorDouble(); while(it.hasNext()) { Double yi = it.next(); if(previousValue!=null) { double delta = yi - previousValue; xbarVariance+=delta*delta; } previousValue = yi; } xbarVariance/=(2*n*(n-1)); return xbarVariance; } }
SystematicSampling { public static double xbarVariance(FlatDataCollection flatDataCollection) { double n = flatDataCollection.size(); if(n<=1) { throw new IllegalArgumentException("The provided collection must have more than 1 elements."); } Double previousValue = null; double xbarVariance = 0.0; Iterator<Double> it = flatDataCollection.iteratorDouble(); while(it.hasNext()) { Double yi = it.next(); if(previousValue!=null) { double delta = yi - previousValue; xbarVariance+=delta*delta; } previousValue = yi; } xbarVariance/=(2*n*(n-1)); return xbarVariance; }  }
SystematicSampling { public static double xbarVariance(FlatDataCollection flatDataCollection) { double n = flatDataCollection.size(); if(n<=1) { throw new IllegalArgumentException("The provided collection must have more than 1 elements."); } Double previousValue = null; double xbarVariance = 0.0; Iterator<Double> it = flatDataCollection.iteratorDouble(); while(it.hasNext()) { Double yi = it.next(); if(previousValue!=null) { double delta = yi - previousValue; xbarVariance+=delta*delta; } previousValue = yi; } xbarVariance/=(2*n*(n-1)); return xbarVariance; }  static FlatDataCollection randomSampling(FlatDataList idList, int n, boolean randomizeRecords); static double mean(FlatDataCollection flatDataCollection); static double xbarVariance(FlatDataCollection flatDataCollection); static double xbarStd(FlatDataCollection flatDataCollection); }
SystematicSampling { public static double xbarVariance(FlatDataCollection flatDataCollection) { double n = flatDataCollection.size(); if(n<=1) { throw new IllegalArgumentException("The provided collection must have more than 1 elements."); } Double previousValue = null; double xbarVariance = 0.0; Iterator<Double> it = flatDataCollection.iteratorDouble(); while(it.hasNext()) { Double yi = it.next(); if(previousValue!=null) { double delta = yi - previousValue; xbarVariance+=delta*delta; } previousValue = yi; } xbarVariance/=(2*n*(n-1)); return xbarVariance; }  static FlatDataCollection randomSampling(FlatDataList idList, int n, boolean randomizeRecords); static double mean(FlatDataCollection flatDataCollection); static double xbarVariance(FlatDataCollection flatDataCollection); static double xbarStd(FlatDataCollection flatDataCollection);  }