/// @return double t-test confidence level with data accumulated
/// in the supplied moments.
/// The variance of both sets is assumed to be the same.
/// @param m DhbStatistics.StatisticalMoments
public double TConfidenceLevel(StatisticalMoments m)
{
int dof = (int)(Count + m.Count - 2);
double sbar = Math.Sqrt((UnnormalizedVariance
+ m.UnnormalizedVariance) / dof);
StudentDistribution tDistr = new StudentDistribution(dof);
return(tDistr.ConfidenceLevel((Average - m.Average)
/ (sbar * Math.Sqrt(1.0 / Count
+ 1.0 / m.Count))));
}
/// @return double t-test confidence level with data accumulated
/// in the supplied moments.
/// Approximation for the case where the variance of both sets may
/// differ.
/// @param m DhbStatistics.StatisticalMoments
public double TApproximateConfidenceLevel(StatisticalMoments m)
{
StudentDistribution tDistr = new StudentDistribution(
(int)(Count + m.Count - 2));
return(tDistr.ConfidenceLevel((Average / StandardDeviation
- m.Average
/ m.StandardDeviation)
/ Math.Sqrt(1 / Count
+ 1 / m.Count)));
}
/// @return double F-test confidence level with data accumulated
/// in the supplied moments.
/// @param m DhbStatistics.StatisticalMoments
public double FConfidenceLevel(StatisticalMoments m)
{
FisherSnedecorDistribution fDistr = new FisherSnedecorDistribution((int)Count, (int)m.Count);
return(fDistr.ConfidenceLevel(Variance / m.Variance));
}