What is the primary goal of using a Stepwise method in regression analysis?

The primary goal of using a Stepwise method in regression analysis is to iteratively add variables and check for improvement in model fit. This approach is designed to identify a subset of predictor variables that contributes significantly to explaining the variability in the response variable. By incorporating one variable at a time based on specific criteria, such as improved statistical significance or predictive power, the method helps streamline the model while ensuring that important factors are included.

Stepwise regression allows for a systematic exploration of the variable space, which can lead to better understanding and enhancement of the model’s performance. It balances the need for a parsimonious model with the inclusion of relevant predictors, making it a popular technique in model selection.

Other options, while related to regression analysis, do not capture the essence of the Stepwise method as effectively. For instance, including all possible variables in the model defeats the purpose of variable selection; focusing exclusively on eliminating variables with the largest p-values is not characteristic of the iterative addition process central to Stepwise methods; and evaluating models based on the lowest AIC value, although important in model selection, does not encapsulate the iterative nature of adding variables inherent in the Stepwise approach.