Multicollinearity in a regression model can be detected when
A) two or more independent variables are highly correlated with each other.
B) an independent variable known to be an important predictor ends up having a partial regression coefficient that is not significant.
C) a partial regression coefficient that should be positive turns out to be negative,or vise versa.
D) when an independent variable is added or removed ,the partial regression coefficients for the other independent variables change drastically.
E) All of the above could be evidence that multicollinearity is present in the model.

Question

Quizplus · Accepted Answer

Multicollinearity refers to a situation in regression analysis where two or more independent variables are highly correlated with each other, which can lead to unreliable and unstable estimates of regression coefficients. All the scenarios described in options A, B, C, and D are potential indicators of multicollinearity. High correlation between independent variables (A), significant changes in regression coefficients upon adding or removing a variable (D), non-significant coefficients for important predictors (B), and unexpected signs of coefficients (C) can all suggest the presence of multicollinearity in a regression model.

Multicollinearity in a Regression Model Can Be Detected When