Statistical Model Analysis

The Wolfram Language's symbolic architecture makes possible a uniquely convenient approach to working with statistical models. Starting from arbitrary data, the Wolfram Language 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.

ReferenceReference

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