Which regression procedure allows an independent variable to enter, be removed, and then re-enter the model?

Stepwise regression is a method used in multiple regression analysis that automatically selects and manages the variables for inclusion in the model based on predetermined criteria such as significance levels. This approach involves adding and removing predictors in a systematic way to find the best combination of variables. It can handle the possibility of variables being removed from the model and then later re-entered if their statistical significance changes during the iterative process of analyzing the data. This flexibility is a hallmark of stepwise regression, as it seeks to refine the model to achieve optimal performance based on the chosen criteria, often enhancing the interpretability and predictive power of the regression model.

In contrast, hierarchical regression focuses on entering variables based on theoretical assumptions or prior knowledge without the same systematic entry and removal of variables. Linear regression is a static method that does not involve such iterative variable selection procedures, and logistic regression is specialized for modeling binary outcomes rather than the dynamic variable management that characterizes stepwise regression.