Stepwise Regression model is a step-by-step iterative construction of a regression model. It is semi-automatic selection process of independent variables carried out in two ways – by including independent variables in the regression model one by one at a time if they are statistically significant, or by including all the independent variables initially and then removing them one by one if they prove to be statistically insignificant.
The stepwise regression model is a much more powerful tool than other multiple regression models and come in handy when working with a large number of potential independent variables and/or fine-tuning a model by selecting variables in or out.
The major approaches to stepwise regression model are as follows:
Forward Selection – starting with no variables initially, testing the addition of a new variable, and adding the variable if proves to improve the model
Backward Elimination – starting with all the variables initially, testing the elimination of a variable, and eliminating the variable if proves to improve the model
Bidirectional Elimination – combination of the above
Example: Various forecasting technologies (Load Pocket, etc.) use stepwise regression model at their core.
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