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 are highly correlated, it is called multicollinearity. Multicollinearity can lead to unstable and unreliable estimates of the regression coefficients and can make it difficult to interpret the significance of individual independent variables. Therefore, it is important to detect and address multicollinearity before interpreting the results of a multiple regression analysis. Autocorrelation refers to correlation between successive observations in a time series or panel data. Homoscedasticity refers to the condition where the variance of the errors is constant across all levels of the independent variables. Curvilinearity refers to a nonlinear relationship between the independent variable(s) and the dependent variable.

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