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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.
Reference
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
