The Stepwise Regression function is a method of systematically selecting variables to fit a model.
This article describes how to apply a stepwise regression to an existing regression model. The below example applies a stepwise regression to an NBD model. It uses a forward selection approach.
- A Regression Model.
- Familiarity with the Structure and Value Attributes of Variable Sets, and how they are used in regression models per our Driver Analysis ebook.
- Go to Anything > Advanced Analysis > Regression > Stepwise.
- In the object inspector go to the Inputs tab.
- In the Regression Model menu select the model to which you wish to apply a stepwise regression.
- OPTIONAL: Select the desired Output type:
- Final: The non-detailed output of the regression model that was chosen as a result of the selection process. This is the default.
- Detailed: The detailed text output of the regression model that was chosen as a result of the selection process, as well as the initial and final model formulae, and an overview of which variables were added or removed at each step, with corresponding AIC values.
- All: Same as Detailed, plus complete information on each step of the selection process.
- OPTIONAL: Select the Direction of variables:
- Forward: Forward selection of variables, starting from an empty model with only the intercept.
- Backward: Backward elimination of variables, starting from the original model. This is the default.
- OPTIONAL: Select the Variables to always include in the selected model. These variables need to be in the original model.