Suppose we have a large number of possible independent variables that we want to explore, and we want to find a smaller set of these variables to use in predicting the dependent variable, eliminating other variables that add only an insignificant or trivial amount of explained variation in the dependent variable beyond the smaller set.Which method should we use regarding the order in which predictor variables are entered into our multiple regression analysis?
A)The all-possible-subsets method.
B)The hierarchical method.
C)The stepwise method.
D)The partial method.

Question

Quizplus · Accepted Answer

The stepwise method is most appropriate for this situation as it allows for an automated selection process of predictor variables based on their significance in explaining the dependent variable. The all-possible-subsets method would be too time-consuming and computationally intensive with a large number of possible independent variables. The hierarchical method and partial method are not appropriate for selecting predictor variables based on their significance alone.

Suppose We Have a Large Number of Possible Independent Variables