Stepwise regression is an automated tool used in the exploratory stages of model building to identify a useful subset of predictors. The process systematically adds the most significant variable or removes the least significant variable during each step.
For example, a housing market consulting company collects data on home sales for the previous year with the goal of predicting future sales prices. With more than 100 predictor variables, finding a model can be a time-consuming task. Minitab's stepwise regression feature automatically identifies a sequence of models to consider. Statistics such as AICc, BIC, R2, adjusted R2, predicted R2, S, and Mallows' Cp help you to compare models. Minitab displays complete results for the model that is best according to the stepwise procedure that you use.