In multiple regression analysis, when the independent variables are highly correlated, this situation is called __________________.
A) Autocorrelation
B) Multicollinearity
C) Homoscedasticity
D) Curvilinearity

Question

Quizplus · Accepted Answer

When the independent variables in multiple regression are highly correlated, it creates a problem known as multicollinearity. This can lead to unreliable regression coefficients and difficulties in interpreting the model. Autocorrelation refers to the correlation between error terms in a time series analysis. Homoscedasticity refers to the equal variance of error terms in regression, and curvilinearity refers to the relationship between the independent and dependent variables being nonlinear.

In Multiple Regression Analysis, When the Independent Variables Are Highly