Which of the following is a false statement regarding the backward elimination procedure?

The backward elimination procedure is a statistical method used in regression analysis to simplify a model by removing insignificant variables. This method begins with a full model that includes all available independent variables, which is in line with choice A. The procedure involves estimating the model and then sequentially removing variables that do not meet a specified significance level—typically determined by their p-values, which relates to choice B.

Choice D states that backward elimination can enhance model fit by eliminating non-significant variables, which is also true, as it can lead to a more parsimonious model that better generalizes to new data.

In contrast, the false statement is that the procedure starts with a model containing zero independent variables. Instead, backward elimination starts with all candidate variables and then systematically eliminates the least significant ones. Therefore, stating that it starts with zero variables does not accurately reflect the nature of the backward elimination process.