If multicollinearity exists among the independent variables included in a multiple regression model,then:
A)the regression coefficients will be difficult to interpret.
B)the standard errors of the regression coefficients for the correlated independent variables will increase.
C)one or more of the coefficients may have the wrong sign.
D)All of these choices are true.

Question

Quizplus · Accepted Answer

If multicollinearity exists among the independent variables included in a multiple regression model, all of these choices are true. The regression coefficients will be difficult to interpret, the standard errors of the regression coefficients for the correlated independent variables will increase, and one or more of the coefficients may have the wrong sign. This occurs because multicollinearity causes the independent variables to be highly correlated, making it difficult to determine the effect of each variable independently on the dependent variable. Additionally, the instability introduced by multicollinearity leads to high standard errors, producing unstable and unreliable coefficients. Finally, multicollinearity can create situations where the sign of the coefficient may be opposite of what is expected.

If Multicollinearity Exists Among the Independent Variables Included in a Multiple