If multicollinearity exists among the independent variables included in a multiple regression model, then:
A) regression coefficients will be difficult to interpret
B) standard errors of the regression coefficients for the correlated independent variables will increase
C) multiple coefficient of determination will assume a value close to zero
D) regression coefficients will be difficult to interpret and standard errors of the regression coefficients for the correlated independent variables will increase
E) none of these

Question

Quizplus · Accepted Answer

Multicollinearity causes the regression coefficients to become unstable and difficult to interpret, and the standard errors of the regression coefficients for the correlated independent variables to increase. This can also lead to the multiple coefficient of determination (R-squared) assuming a value close to zero or becoming unreliable.

If Multicollinearity Exists Among the Independent Variables Included in a Multiple