/// @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 = System.Math.Sqrt((UnnormalizedVariance
+ m.UnnormalizedVariance) / dof);
var tDistr = new StudentDistribution(dof);
return tDistr.ConfidenceLevel((Average - m.Average)
/ (sbar * System.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)
{
var tDistr = new StudentDistribution(
(int)(Count + m.Count - 2));
return tDistr.ConfidenceLevel((Average / StandardDeviation
- m.Average
/ m.StandardDeviation)
/ System.Math.Sqrt(1 / Count
+ 1 / m.Count));
}
/// Constructor method.
/// @param x double
/// @param m DhbStatistics.StatisticalMoments
public WeightedPoint(double x, StatisticalMoments m)
: this(x, m.Average)
{
Error = m.ErrorOnAverage;
}
/// @return double F-test confidence level with data accumulated
/// in the supplied moments.
/// @param m DhbStatistics.StatisticalMoments
public double FConfidenceLevel(StatisticalMoments m)
{
var fDistr = new FisherSnedecorDistribution((int)Count, (int)m.Count);
return fDistr.ConfidenceLevel(Variance / m.Variance);
}
/// @param x double
/// @param m StatisticalMoments
public void AccumulateAverage(double x, StatisticalMoments m)
{
AccumulatePoint(x, m.Average, m.ErrorOnAverage);
}
