Meta.Numerics.Statistics Namespace 
Class  Description  

AnovaRow 
A row in an analysis of variance (ANOVA) table.
 
AnovaTestRow 
A row in an ANOVA table for which an Ftest is available.
 
AR1FitResult 
Contains the result of a fit of time series data to an AR(1) model.
 
BinaryContingencyTableOperations 
Exposes properties which are only defined for a 2 X 2 contingency table.
 
Bivariate 
Contains methods for analyzing on bivariate samples.
 
BivariateSample 
Represents a set of data points, where each data point is described by a pair of real numbers.
 
ContingencyTable 
Represents a contingency table without row and column labels.
 
ContingencyTableR, C 
Represents a contingency table.
 
ContinuousTestStatistic 
Describes a test statistic with a continuous distribution.
 
DiscreteTestStatistic 
Describes a test statistic with a discrete distribution.
 
FitResult 
The base class of results for all fits.
 
GeneralLinearRegressionResult 
Describes the result of any generalized linear regression.
 
Histogram 
Represents a histogram.
 
HistogramBinsCollection 
Represents a collection of histogram bins.
 
InsufficientDataException 
The exception that is thrown when an operation is attempted with less than the minimum required data.
 
LinearLogisticRegressionResult 
Describes the result of a linear logistic regression fit.
 
LinearRegressionResult 
Describes the result of a linear regression fit.
 
MA1FitResult 
Describes the result of a fit of time series data to an MA(1) model.
 
MeansClusteringResult 
Describes the result of a kmeans clustering analysis.
 
MultiLinearLogisticRegressionResult 
Describes the result of a linear logistic regression fit.
 
MultiLinearRegressionResult 
Describes the result of a multiple linear regression fit.
 
Multivariate 
Contains methods for analyzing multivariate samples.
 
MultivariateSample 
Represents a multivariate sample.
 
NonlinearRegressionResult 
Describes the result of a fit to a nonlinear function.
 
OneWayAnovaResult 
The result of a oneway ANOVA test.
 
Parameter 
Represents a parameter from a fit.
 
ParameterCollection 
Represents a collection of fit parameters.
 
PolynomialRegressionResult 
Describes the result of a polynomial regression fit.
 
PrincipalComponent 
Represents a component of a principal component analysis.
 
PrincipalComponentAnalysis 
Represents a principal component analysis.
 
PrincipalComponentCollection 
Represents a collection of principal components.
 
ResidualsResult 
Describes the result of a fit with residuals.
 
Sample 
Represents a set of data points, where each data point consists of a single real number.
 
Series 
Contains methods for the statistical analysis of time series.
 
SummaryStatistics 
Tracks summary statistics for a stream of data points.
 
TestResult 
Describes the result of a statistical test.
 
TimeSeries 
Represents an ordered series of data points.
 
TimeSeriesPopulationStatistics 
Contains estimates of the moments of the population from which a time series is drawn.
 
TwoWayAnovaResult 
Represents the result of a twofactor analysis of variance.
 
UncertainMeasurementT 
Represents an experimental data point that is a function of an arbitrary variable.
 
UncertainMeasurementFitResult 
Contains the result of a fit to a sample of uncertain measurements.
 
UncertainMeasurementSample 
Represents a set of UncertainMeasurementT measurements.
 
UncertainMeasurementSampleT 
Represents a set of measurements.
 
Univariate 
Contains methods for analyzing univariate samples.

Structure  Description  

HistogramBin 
Represents one bin in a histogram.

Enumeration  Description  

TestType 
Describes the sidedness of a statistical test.

This namespace contains types for doing basic and advanced statistics. It contains APIs for finding moments and percentiles, measuring associations, fitting to models, and other statistical operations.
The central class for operating on samples consisting of independent measurements of a single variable is the Univariate class. The central class for operating on samples of independent bivariate data (i.e. paired measurements) is the Bivariate class. The central class for operating on samples of independent measurements of multiple variables is the Multivariate class. The central class for operating on time series data is the Series class.
All of these central classes consist of static methods that accept one or more columns of data. Each column can be of any type that implements the appropriate collection interface (e.g. IReadOnlyListT). Many of the methods are extension methods, so they effectively become instance methods on all such types.
Some of the classes in this namespace are left over from earlier versions of Meta.Numerics which required users to store each kind of data in a particular storage class. Examples of these storage classes include Sample, BivariateSample, MultivariateSample, and TimeSeries. These storage classes each expose methods appropriate for the analysis of a particular type of data. The advantage of such a system is that it makes immediately clear to the user which methods are appropriate for which types of data. The disadvantage is that it requires users to transfer their data into our containers before it can be analyzed. You can still use these classes, if you prefer, but essentially all of their functionality is also exposed in the new central, static classes that can be applied to any appropriate collection.