Multicollinearity in a Regression Model Can Be Detected When

Multiple Choice

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.