If multicollinearity exists among the independent variables included in a multiple regression model, then:
A) regression coefficients will be difficult to interpret.
B) the 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) the standard errors of the regression coefficients for the correlated independent variables will increase and and multiple coefficient of determination will assume a value close to zero.

Question

Quizplus · Accepted Answer

Multicollinearity makes it difficult to interpret the regression coefficients and leads to an increase in the standard errors of the regression coefficients for the correlated independent variables. Additionally, multicollinearity reduces the ability of the model to explain the variation in the dependent variable, leading to a lower multiple coefficient of determination. Therefore, option D is the correct answer.

If Multicollinearity Exists Among the Independent Variables Included in a Multiple