Univariate Class 
Namespace: Meta.Numerics.Statistics
The Univariate type exposes the following members.
Name  Description  

CentralMoment 
Computes the given sample central moment.
 
ChiSquaredTest 
Tests whether the sample is compatible with the given discrete distribution.
 
CorrectedStandardDeviation 
Computes the Besselcorrected standard deviation.
 
FisherFTest 
Tests whether the variances of two samples are compatible.
 
FitToBeta 
Finds the Beta distribution that best fits the given sample.
 
FitToExponential 
Finds the exponential distribution that best fits the given sample.
 
FitToGamma 
Finds the Gamma distribution that best fits the given sample.
 
FitToGumbel 
Find the Gumbel distribution that best fit the given sample.
 
FitToLognormal 
Finds the lognormal distribution that best fits the given sample.
 
FitToNormal 
Finds the normal distribution that best fits the given sample.
 
FitToRayleigh 
Finds the Rayleigh distribution that best fits the given sample.
 
FitToWald 
Finds the Wald distribution that best fits a sample.
 
FitToWeibull 
Finds the Weibull distribution that best fits the given sample.
 
InterquartileRange 
Finds the interquartile range.
 
InverseLeftProbability 
Finds the sample value corresponding to a given percentile.
 
KolmogorovSmirnovTest(IReadOnlyListDouble, ContinuousDistribution) 
Tests whether the sample is compatible with the given distribution.
 
KolmogorovSmirnovTest(IReadOnlyListDouble, IReadOnlyListDouble) 
Tests whether the sample is compatible with another sample.
 
KruskalWallisTest(IReadOnlyListIReadOnlyListDouble) 
Performs a KruskalWallis test on the given samples.
 
KruskalWallisTest(IReadOnlyListDouble) 
Performs a KruskalWallis test on the given samples.
 
KuiperTest 
Tests whether the sample is compatible with the given distribution.
 
LeftProbability 
Gets the fraction of values equal to or less than the given value.
 
MannWhitneyTest 
Tests whether one sample median is compatible with another sample median.
 
Maximum 
Finds the maximum value.
 
MaximumLikelihoodFit 
Finds the parameters that make an arbitrary, parameterized distribution best fit the sample.
 
Mean 
Computes the sample mean.
 
Median 
Finds the median.
 
Minimum 
Finds the minimum value.
 
OneWayAnovaTest(IReadOnlyCollectionIReadOnlyCollectionDouble) 
Performs a oneway analysis of variance (ANOVA).
 
OneWayAnovaTest(IReadOnlyCollectionDouble) 
Performs a oneway analysis of variance (ANOVA).
 
PopulationCentralMoment 
Estimates the given central moment of the underlying population.
 
PopulationMean 
Estimates the mean of the underlying population.
 
PopulationRawMoment 
Estimates the given raw moment of the underlying population.
 
PopulationStandardDeviation 
Estimates of the standard deviation of the underlying population.
 
PopulationVariance 
Estimates of the variance of the underlying population.
 
RawMoment 
Computes the given sample raw moment.
 
ShapiroFranciaTest 
Performs a ShapiroFrancia test of normality on the sample.
 
SignTest 
Tests whether the sample median is compatible with the given reference value.
 
Skewness 
Computes the sample skewness.
 
StandardDeviation 
Computes the sample standard deviation.
 
StudentTTest(IReadOnlyCollectionDouble, IReadOnlyCollectionDouble) 
Tests whether one sample mean is compatible with another sample mean.
 
StudentTTest(IReadOnlyCollectionDouble, Double) 
Tests whether the sample mean is compatible with the reference mean.
 
Trimean 
Finds the trimean.
 
TwoWayAnovaTest 
Performs a twoway analysis of variance.
 
Variance 
Computes the sample variance.
 
ZTest 
Performs a ztest to test whether the given sample is compatible with the given normal reference population.

This is the central class for the analysis of univariate samples of independent, identically distributed values.
To compute moments of the sample data, you can use methods such as Mean, Variance, RawMoment, and CentralMoment. Note that these are moments of the sample data, not estimates of the moments of the underlying population from which the sample was drawn.
To obtain estimates of the moments of the underlying population from which the sample was drawn, you can use methods such as PopulationMean, PopulationStandardDeviation, PopulationRawMoment, and PopulationCentralMoment. These estimates all come with associated error bars, so they return UncertainValue structures.
You can fit a sample to any number of distributions using methods such as FitToExponential(IReadOnlyListDouble), FitToLognormal(IReadOnlyListDouble), FitToNormal(IReadOnlyListDouble), FitToWeibull(IReadOnlyListDouble).
You can perform statistical tests on a single sample, such as the StudentTTest(IReadOnlyCollectionDouble, Double) or SignTest(IReadOnlyCollectionDouble, Double) to compare a sample to a reference value, or the ShapiroFranciaTest(IReadOnlyListDouble) to test a sample for normality. You can also perform statistical tests comparing multiple samples, such as the twosample StudentTTest(IReadOnlyCollectionDouble, IReadOnlyCollectionDouble), and MannWhitneyTest(IReadOnlyListDouble, IReadOnlyListDouble), or the multisample OneWayAnovaTest(IReadOnlyCollectionDouble) and KruskalWallisTest(IReadOnlyListDouble).
Most of the methods in this class are extension methods that accept as a sample any type that implements the appropriate collection interface. So, for example, given sample values in a List<string> named s, you could estimate the variance of the population from which it was drawn either by s.PopulationVariance() or Univariate.PopulationVariance(s).