In statistics, stepwise regression includes regression models in which the choice of predictive variables is carried out by an automatic procedure. Usually, this takes the form of a sequence of F-tests, but other techniques are possible, such as t-tests, adjusted R-square, Akaike information criterion, Bayesian information criterion, Mallows's Cp, or false discovery rate.
The frequent practice of fitting the final selected model followed by reporting estimates and confidence intervals without adjusting them to take the model building process into account has led to calls to stop using stepwise model building altogether or to at least make sure model uncertainty is correctly reflected.
Read more about Stepwise Regression: Main Approaches, Selection Criterion, Model Accuracy, Criticism, See Also