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.