In multiple regression, when two or more independent variables are correlated with one another, the situation is known as
A)heteroscedasticity.
B)homoscedasticity.
C)spurious correlation.
D)autocorrelation.
E)multicollinearity.

Question

Quizplus · Accepted Answer

When two or more independent variables are correlated with one another, it leads to multicollinearity, which means that the independent variables are not independent of each other, and this can cause problems in the interpretation of the coefficients and the overall model.

In Multiple Regression, When Two or More Independent Variables Are