Statistical Model Analysis

Mathematica's symbolic architecture makes possible a uniquely convenient approach to working with statistical models. Starting from arbitrary data, Mathematica generates symbolic representations of fitted models, from which a full spectrum of results and diagnostics can immediately be extracted, visualized, or used in other computations.


LinearModelFit construct a linear regression model from data

NonlinearModelFit construct a nonlinear regression model

GeneralizedLinearModelFit generalized linear models, with general link functions

LogitModelFit ▪ ProbitModelFit

model["property"] extract properties, diagnostics, etc. from a model

model[x1,...] compute values of the best fit at a particular point

"BestFit" ▪ "FitResiduals" ▪ "ANOVATable" ▪ "ParameterConfidenceIntervals" ▪ "CookDistances" ▪ "Deviances" ▪ "AIC" ▪ "FitCurvatureTable" ▪

FittedModel symbolic representation of a model

Normal extract an expression for the best fit from a symbolic model

Detailed Control

Weights ▪ NominalVariables ▪ LinkFunction ▪ LinearOffsetFunction

ConfidenceLevel ▪ VarianceEstimatorFunction ▪ DispersionEstimatorFunction

DesignMatrix construct a design matrix from data

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