Forward Stepwise Regression

Forward stepwise regression adds one independent variable at a time to the multiple linear regression equation, starting with the independent variable with the most significant probability of the correlation (partial F statistic). It then recalculates the partial F statistic for the remaining independent variables, taking the existing regression equation into consideration.

The resulting multiple linear regression equation will always have at least one independent variable.

Forward stepwise regression continues to add independent variables until either:

It runs out of independent variables.
It reaches one of the selected stopping criteria in the Stepwise Options dialog.
The number of included independent variables reaches one-third the number of data points in the series.

There are two stopping criteria:

R-squared (R²) — Stops the stepwise regression if the difference between a specified statistic (either R² or adjusted R²) for the previous and new regression solutions is below a threshold value. When this happens, Predictor does not use the last independent variable. For example, the third step of a stepwise regression results in an R² value of 0.81, and the fourth step adds another independent variable and results in an R² value is 0.83. The difference between the R² values is 0.02. If the threshold value is 0.03, Predictor returns to the regression equation for the third step and stops the stepwise regression.
Partial F-test significance — Stops the stepwise regression if the probability of the partial F statistic for a new solution is above a maximum value. For example, if you set the maximum probability to 0.05 and the partial F statistic for the fourth step of a stepwise regression results in a probability of 0.08, Predictor returns to the regression equation for the third step and stops the stepwise regression.