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