What does a forward selection method do in regression analysis?

In regression analysis, the forward selection method is a technique used for model building that sequentially adds predictors to the model based on their contribution to explaining the response variable. When starting with a model that contains no predictors, forward selection evaluates each candidate variable and adds the one that has the highest statistical significance, typically measured by a criterion such as the p-value. This process continues iteratively, with each subsequent step allowing for the introduction of an additional variable that improves the model the most at that stage.

This method is particularly effective for identifying which variables contribute important information to the model while achieving a balance between model complexity and goodness-of-fit. By focusing on adding the best variable at each step, forward selection helps to build a model that is both interpretable and predictive.

The other options do not accurately represent the forward selection process. Evaluating the overall model significance before adding variables describes a different type of analysis, while removing variables one at a time is indicative of backward elimination. Testing all variables simultaneously without bias contrasts with the sequential approach of forward selection, which methodically assesses and incorporates each variable one after the other.