# Logistic Regression Variable Selection Methods

Method selection allows you to specify how independent variables are entered into the analysis. Using different methods, you can construct a variety of regression models from the same set of variables.

- Enter. A procedure for variable selection in which all variables in a block are entered in a single step.
- Forward Selection (Conditional). Stepwise selection method with entry testing based on the significance of the score statistic, and removal testing based on the probability of a likelihood-ratio statistic based on conditional parameter estimates.
- Forward Selection (Likelihood Ratio). Stepwise selection method with entry testing based on the significance of the score statistic, and removal testing based on the probability of a likelihood-ratio statistic based on the maximum partial likelihood estimates.
- Forward Selection (Wald). Stepwise selection method with entry testing based on the significance of the score statistic, and removal testing based on the probability of the Wald statistic.
- Backward Elimination (Conditional). Backward stepwise selection. Removal testing is based on the probability of the likelihood-ratio statistic based on conditional parameter estimates.
- Backward Elimination (Likelihood Ratio). Backward stepwise selection. Removal testing is based on the probability of the likelihood-ratio statistic based on the maximum partial likelihood estimates.
- Backward Elimination (Wald). Backward stepwise selection. Removal testing is based on the probability of the Wald statistic.

The significance values in your output are based on fitting a single model. Therefore, the significance values are generally invalid when a stepwise method is used.

All independent variables selected are added to a single regression model. However, you can specify different entry methods for different subsets of variables. For example, you can enter one block of variables into the regression model using stepwise selection and a second block using forward selection. To add a second block of variables to the regression model, click Next.