Stepwise regression carried out by adding variables to a multiple regression model until no additional model makes a significant contribution is called ________.
A)forward selection
B)backward elimination
C)general stepwise
D)parsimony

Question

Quizplus · Accepted Answer

Forward selection is the process of adding variables to a multiple regression model one by one until no additional variable makes a significant contribution to the model. This is a form of stepwise regression that starts with an empty model and adds variables one by one, rather than starting with a full model and removing variables. Backward elimination, on the other hand, starts with a full model and removes variables one by one until no additional variable can be removed without significantly reducing model fit. General stepwise refers to a combination of forward selection and backward elimination, where variables are added and removed at each step based on their contribution to the model. Parsimony is a concept in statistics that refers to choosing the simplest model that adequately explains the data, and is not explicitly related to stepwise regression.

Stepwise Regression Carried Out by Adding Variables to a Multiple