The backward elimination of stepwise regression
A)sometimes misses the best model for a given number of predictors.
B)adds predictors one at a time starting with the best single predictor.
C)runs all possible models and then chooses the best one.
D)requires nonlinear estimation using maximum likelihood.

Question

Quizplus · Accepted Answer

Backward elimination of stepwise regression starts with all predictors included in the model and then removes one predictor at a time based on a certain criterion, such as the p-value or the change in R-squared. However, this iterative process may sometimes miss the best model because the order in which predictors are removed can affect the final model, and there may be multiple models with similar goodness-of-fit. Therefore, it is advisable to check the stability of the selected model using cross-validation or bootstrapping.

The Backward Elimination of Stepwise Regression