Builds the multiple linear regression models using forward selection, backward selection, or stepwise selection.

Determines whether the specified Object is equal to the current Object.

Allows an Object to attempt to free resources and perform other cleanup operations before the Object is reclaimed by garbage collection.

Serves as a hash function for a particular type.

Gets the Type of the current instance.

Creates a shallow copy of the current Object.

Sets the means of the variables.

Returns a String that represents the current Object.

An analysis of variance table and related statistics.

The student-t test statistics for the regression coefficients.

The variance inflation factors for the final model in this invocation.

Results after cov has been swept for the columns corresponding to the variables in the model.

Forces independent variables into the model based on their level assigned from Levels.

The stepwise regression history for the independent variables.

Returns the intercept.

The levels of priority for variables entering and leaving the regression.

Specifies the stepwise selection method, forward, backward, or stepwise Regression.

Defines the largest p-value for variables entering the model.

Defines the smallest p-value for removing variables.

An array containing information indicating whether or not a particular variable is in the model.

The tolerance used to detect linear dependence among the independent variables.