In multiple regression analysis,when the independent variables are highly correlated,this situation is called:
A)autocorrelation.
B)homoscedasticity.
C)multicollinearity.
D)linearity.

Question

Quizplus · Accepted Answer

In multiple regression analysis, when the independent variables are highly correlated, it creates a situation known as multicollinearity. Multicollinearity can cause inaccuracies in the regression coefficients and standard errors, making it difficult to determine the true relationship between the independent variables and the dependent variable. Therefore, it is best to try and avoid multicollinearity by either removing one or more of the highly correlated variables or by using more advanced regression techniques that can handle multicollinearity.

In Multiple Regression Analysis,when the Independent Variables Are Highly Correlated,this