In the stepwise forward regression, we always start with an empty model without predictors. For the model selection, in order to determine which parameter that should be included next in the model, a test statistic based on the partial correlation coefficient for each predictor is used, following an F-distribution under the null hypothesis. Specify the allowed level of type I errors as a probability in the ‘F-value include’ box. This significance level will set the probability of including an un-correlated predictor and is used as a stopping criterion for the stepwise regression. At each specific step in the procedure, if the program cannot find a predictor whose corresponding test statistic is significant, the maximum number of predictors is achieved. The stepwise procedure then makes a final turn by including the predictor with the lowest non-significant p-value, excludes the least significant predictor from the resulting model before it stops and returns the final model.