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Home Multivariate Data Basic Knowledge Selection of Variables Pruning  
See also: selection of variables, forward selection  
Variable Selection  PruningBackward selection is the counterpart to forward selection: while forward selection starts with one variable, building up a model by adding variables, backward selection starts with all available variables, removing all "unnecessary" variables, step by step. This method is also known as the "pruning" of variables. The algorithm is defined as follows (specifically described here for
multiple linear regression; however, this technique may be used for other
modeling approaches, too):
1. calculate a model including all available variables
Note: you have to recalculate all partial F values after removing a
variable, since this changes the F values of the remaining variables.


Home Multivariate Data Basic Knowledge Selection of Variables Pruning 