a technique used in creating
multiple regression models in which independent variables from a large set of such variables are added to the
regression equation in the order of their predictive power (i.e., largest to smallest increase in the
coefficient of multiple determination) until a preset criterion is reached and there is no further significant change in the model’s predictive power. Also called
forward inclusion;
forward stepwise regression;
stepup selection. See also
F-to-enter;
F-to-remove.