The forward selection method of stepwise regression
A)starts with all the predictors in the model and deletes the weaker ones.
B)adds predictors one at a time starting with the best single predictor.
C)runs all possible models and then chooses the best fitting one.
D)requires nonlinear estimation using maximum likelihood.

Question

Quizplus · Accepted Answer

The forward selection method of stepwise regression starts with an empty model and adds predictors one at a time, starting with the best single predictor, based on some criterion (usually the F-test or t-test of the variable's coefficient). It only adds a predictor if it significantly improves the model fit, and the process stops when no more significant predictors can be added.

The Forward Selection Method of Stepwise Regression