Insignificant variables in a multiple regression model can be removed using ________.
A)step-wise regression
B)dummy variables
C)the Durbin-Watson statistic
D)linear extrapolation

Question

Quizplus · Accepted Answer

Step-wise regression is a method of systematically selecting variables to remove from a multiple regression model. It starts with a full model and iteratively removes the variable with the least significant impact on the model until all remaining variables have a significant impact on the model. Dummy variables are used to represent categorical variables in a regression model and are not related to removing insignificant variables. The Durbin-Watson statistic is used to test for autocorrelation in a time series model and is not related to removing insignificant variables. Linear extrapolation is a method of estimating values outside of a given range and is not related to removing insignificant variables.

Insignificant Variables in a Multiple Regression Model Can Be Removed