Which regression method involves entering predictor variables based on mathematical criteria?

The chosen answer refers to Stepwise Regression, which is a method that systematically adds or removes predictor variables based on specific statistical criteria, such as p-values or adjusted R-squared values. This approach helps to identify a subset of predictor variables that contribute the most significant amount of variance to the outcome variable while improving the model's overall fit.

In Stepwise Regression, the process begins with either no predictors or a certain set of predictors, and then variables are added one at a time (forward selection) or removed one at a time (backward elimination) based on criteria established prior to the analysis. This method is particularly useful when dealing with a large number of potential predictor variables, as it can help streamline the model and focus on the most impactful variables based on data-driven decisions.

Hierarchical Regression, while also applicable in evaluating the contribution of variables, involves entering predictor variables in a predetermined order based on theory or previous research rather than mathematical criteria. Forced Entry Regression involves including all predictor variables in a model simultaneously, without regard to their statistical significance, making it distinct from Stepwise Regression. Mediation Analysis examines the relationships among variables to understand if a mediating variable influences the direct relationship between an independent and dependent variable, which does not involve the qualities of entering