Forward stepwise regression can be considered as the opposite of the backward one. In this regression, we start from the null model, that is, from a model having no variable at all, and we go on one step at a time trying possible combinations of n-m variables, until we reach n.

Rationales that can be applied to select among combinations and final models are exactly the same as for the backward one. Before moving on, I would like to talk briefly about the null model, which is employed as a baseline here. It is actually a constant value that shows for every value of explanatory variables the average level of y.