public static bool OneTailNormalHypoTest(double[] data, double mu, double sigma, int n, double signLevel = 0.05,
AlternateHypo tail = AlternateHypo.GreaterThan)
{
try
{
double?mean = Mean(data);
if (mean != null)
{
return(OneTailNormalHypoTest((double)mean, mu, sigma, n, signLevel, tail));
}
else
{
throw new InvalidOperationException();
}
}
catch (InvalidOperationException)
{
return(true);
}
catch
{
throw;
}
}
/// <summary>
/// This will perform a binomial hypothesis test.
/// </summary>
/// <param name="sampleSuccess">How many were successful</param>
/// <param name="n"></param>
/// <param name="p"></param>
/// <param name="tail">Perform test on upper or lower tail</param>
/// <param name="signLevel">Significance level.</param>
/// <returns>True means that there is enough evidence to reject the null hypothesis.</returns>
public static bool HypoTest(int sampleSuccess, int n, double p, AlternateHypo tail, double signLevel)
{
try
{
int[] x = Enumerable.Range(0, n + 1).ToArray();
BinomialDistribution distribution = new BinomialDistribution(n, p);
switch (tail)
{
case AlternateHypo.LessThan:
{
int criticalRegion = x.AsParallel()
.Where(num => distribution.DistributionFunction(num) < signLevel)
.OrderBy(num => num)
.First();
if (sampleSuccess <= criticalRegion)
{
return(true);
}
else
{
return(false);
}
}
default:
{
int criticalRegion = x.AsParallel()
.Where(num => (1 - distribution.DistributionFunction(num)) < signLevel)
.OrderBy(num => num)
.First();
if (sampleSuccess >= criticalRegion)
{
return(true);
}
else
{
return(false);
}
}
}
}
catch (InvalidOperationException)
{
return(false); //Because there is no evidence at all.
}
catch
{
throw;
}
}
public static bool OneTailNormalHypoTest(double meanOfSample, double mu, double sigma, int n, double signLevel = 0.05,
AlternateHypo tail = AlternateHypo.GreaterThan)
{
try
{
switch (tail)
{
case AlternateHypo.GreaterThan:
double upperTail = signLevel;
double upperCritRegion = new NormalDistribution(mu, sigma / Math.Sqrt(n)).InverseDistributionFunction(upperTail);
if (meanOfSample > upperCritRegion)
{
return(true);
}
else
{
return(false);
}
default:
double lowerTail = signLevel;
double lowerCritRegion = new NormalDistribution(mu, sigma / Math.Sqrt(n)).InverseDistributionFunction(lowerTail);
if (meanOfSample < lowerCritRegion)
{
return(true);
}
else
{
return(false);
}
}
}
catch (InvalidOperationException)
{
return(true);
}
catch
{
throw;
}
}
