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.

(RefLink[LinearModelFit, paclet:ref/LinearModelFit]) — construct a linear regression model from data

(RefLink[NonlinearModelFit, paclet:ref/NonlinearModelFit]) — construct a nonlinear regression model

(RefLink[GeneralizedLinearModelFit, paclet:ref/GeneralizedLinearModelFit]) — generalized linear models, with general link functions

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

model[(Subscript[x, 1]),…] — compute values of the best fit at a particular point

(RefLink[FittedModel, paclet:ref/FittedModel]) — symbolic representation of a model

(RefLink[Normal, paclet:ref/Normal]) — extract an expression for the best fit from a symbolic model

Detailed Control

(RefLink[DesignMatrix, paclet:ref/DesignMatrix]) — construct a design matrix from data

Symbolic Model Discovery

(RefLink[FindFormula, paclet:ref/FindFormula]) — try to find a simple symbolic formula for data

(RefLink[FindDistribution, paclet:ref/FindDistribution]) — try to find a simple symbolic form for the distribution of data