Which of the followings is correct when a multicollinearity presents in a MLR? C

Question

Which of the followings is correct when a multicollinearity presents in a MLR? Choose all correct answers.

a. The marginal sum of squares SSR(X1)  is very similar to the extra sum of squares SSR(X1 | X2 ).

b. When independent variables are correlated, the effect on the response ascribed to an independent variable is similar regardless of the other independent variables in the model.

c. The common interpretation of a regression coefficient as measuring the change in the expected value of the response variable when the given predictor variable is increased by one unit while all other predictor variables are held constant is still valid when multicollinearity exists.

d. The precision of the estimated regression coefficients increases as more predictor variables are added to the model. Variables in model.

e. The individual estimates could be statistically signifficant, whereas the overall f-test is insignificant.

f. The regression coefficient of any one variable does not depend on which other predictor variables are not included in the model.

g. Large changes in the estimated regression coefficients when a predictor variable is added or deleted do not indicate a multicollinearity in a data set.

h. When some or all predictor variables are correlated among themselves, in general, it inhibits our ability to obtain a good fit and tends to affect inferences about mean responses or predictions of new observations, provided these inferences are made within the region of observations.

i. VIFs measure how much the variances of the estimated regression coefficients are inflated as compared to when the independent variables are not linearly related.

j. Multicollinearity needs to be corrected if a regression is used only for forecasting.

Explanation / Answer

a,j,i,h are correct answers

Related Questions

Navigate

• Browse (All)
• Browse W
• Subjects

Integrity-first tutoring: explanations and feedback only — we do not complete graded work. Learn more.