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.

## Requirements

- 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.

## Method

- 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.

## See Also

How to Run Binary Logit Regression

How to Run a Generalized Linear Model

How to Run a Multinomial Logit Regression

How to Run NBD Regression in Displayr

How to Run Ordered Logit Regression

How to Run Quasi-Poisson Regression

How to Create Regression Multicollinearity Table (VIF)

How to Create a Prediction-Accuracy Table

How to Create a Goodness-of-Fit Plot

How to Test Residual Heteroscedasticity of Regression Models

How to Save Predicted Values of Regression Models

How to Save Fitted Values of Regression Models

How to Save Probabilities of Each Response of Regression Models

How to Test Residual Normality (Shapiro-Wilk) of Regression Models

How to Test Residual Serial Correlation (Durbin-Watson) of Regression Models

How to Save Residuals of Regression Models