BackwardElimination 
Constructs a GLM model for a set of observations using the backward elimination method.

EliminationByAIC 
In each step, a factor is dropped if the resulting model has the least AIC, until no factor
removal can result in a model with AIC lower than the current AIC.

EliminationByZValue 
In each step, the factor with the least zvalue is dropped, until all zvalues are greater than
the critical value (given by the significance level).

ForwardSelection 
Constructs a GLM model for a set of observations using the forward selection method.

GLMModelSelection 
Given a set of observations {y, X}, we would like to construct a GLM to explain the data.

SelectionByAIC 
In each step, a factor is added if the resulting model has the highest AIC, until no factor
addition can result in a model with AIC higher than the current AIC.

SelectionByZValue 
In each step, the most significant factor is added, until all remaining factors are
insignificant.
