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Meta.Numerics.Statistics Namespace

Contains types for the statistical analysis of data.
Classes
  ClassDescription
Public classAnovaRow
A row in an analysis of variance (ANOVA) table.
Public classAnovaTestRow
A row in an ANOVA table for which an F-test is available.
Public classAR1FitResult
Contains the result of a fit of time series data to an AR(1) model.
Public classBinaryContingencyTableOperations
Exposes properties which are only defined for a 2 X 2 contingency table.
Public classBivariate
Contains methods for analyzing on bivariate samples.
Public classBivariateSample
Represents a set of data points, where each data point is described by a pair of real numbers.
Public classContingencyTable
Represents a contingency table without row and column labels.
Public classContingencyTableR, C
Represents a contingency table.
Public classContinuousTestStatistic
Describes a test statistic with a continuous distribution.
Public classDiscreteTestStatistic
Describes a test statistic with a discrete distribution.
Public classFitResult
The base class of results for all fits.
Public classGeneralLinearRegressionResult
Describes the result of any generalized linear regression.
Public classHistogram
Represents a histogram.
Public classHistogramBinsCollection
Represents a collection of histogram bins.
Public classInsufficientDataException
The exception that is thrown when an operation is attempted with less than the minimum required data.
Public classLinearLogisticRegressionResult
Describes the result of a linear logistic regression fit.
Public classLinearRegressionResult
Describes the result of a linear regression fit.
Public classMA1FitResult
Describes the result of a fit of time series data to an MA(1) model.
Public classMeansClusteringResult
Describes the result of a k-means clustering analysis.
Public classMultiLinearLogisticRegressionResult
Describes the result of a linear logistic regression fit.
Public classMultiLinearRegressionResult
Describes the result of a multiple linear regression fit.
Public classMultivariate
Contains methods for analyzing multivariate samples.
Public classMultivariateSample
Represents a multivariate sample.
Public classNonlinearRegressionResult
Describes the result of a fit to a non-linear function.
Public classCode exampleOneWayAnovaResult
The result of a one-way ANOVA test.
Public classParameter
Represents a parameter from a fit.
Public classParameterCollection
Represents a collection of fit parameters.
Public classPolynomialRegressionResult
Describes the result of a polynomial regression fit.
Public classPrincipalComponent
Represents a component of a principal component analysis.
Public classPrincipalComponentAnalysis
Represents a principal component analysis.
Public classPrincipalComponentCollection
Represents a collection of principal components.
Public classSample
Represents a set of data points, where each data point consists of a single real number.
Public classSeries
Contains methods for the statistical analysis of time series.
Public classSummaryStatistics
Tracks summary statistics for a stream of data points.
Public classTestResult
Describes the result of a statistical test.
Public classTimeSeries
Represents an ordered series of data points.
Public classTimeSeriesPopulationStatistics
Contains estimates of the moments of the population from which a time series is drawn.
Public classTwoWayAnovaResult
Represents the result of a two-factor analysis of variance.
Public classUncertainMeasurementT
Represents an experimental data point that is a function of an arbitrary variable.
Public classUncertainMeasurementFitResult
Contains the result of a fit to a sample of uncertain measurements.
Public classUncertainMeasurementSample
Public classUncertainMeasurementSampleT
Represents a set of measurements.
Public classUnivariate
Contains methods for analyzing univariate samples.
Structures
  StructureDescription
Public structureHistogramBin
Represents one bin in a histogram.
Enumerations
  EnumerationDescription
Public enumerationTestType
Describes the sidedness of a statistical test.
Remarks

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.