A multiple regression model is observed to contain multicollinearity. Consider t

Question

A multiple regression model is observed to contain multicollinearity. Consider the following statements:

(A) The independent variables are not correlated

(B) The coefficient of multiple determination does reliably indicate the percent of variation in Y that is explained by the independent variables

(C) The estimated regression coefficients for the independent variables will not vary substantially depending on which other independent variables are included in the model

(D) The regression equation can be used to obtain a point estimate of Y

Explanation / Answer

Ans:

All of the above statements are false.

The situation where the explanatory variables(independent variables) are highly intercorrelated is referred to as multicollinearity.

In statistics, multicollinearity (also collinearity) is a phenomenon in which one predictor variable in a multiple regression model can be linearly predicted from the others with a substantial degree of accuracy.

Multicollinearity exists when two or more of the predictors in a regression model are moderately or highly correlated. Unfortunately, when it exists, it can wreak havoc on our analysis and thereby limit the research conclusions we can draw. As we will soon learn, when multicollinearity exists, any of the following pitfalls can be exacerbated:

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