. Suppose a multivariate regression resulted in R2 = 0.71. What can we say about the sums of
squares in this regression?
It can be greater than the regular R2.
It can be negative.
It can be greater than one.
It will always increase if a regressor is added to the model. 3. Consider the model . In which of the following cases will we be unable to compute the OLS estimators
,
, and
a. Corr[ y , u ] = 1
b. Corr[ y , x ] = 1
c. Corr[ x , u ] = 1
d. Corr[ x , z ] = 1 ? 4. Suppose we are trying to decide whether to use the model
the model
seen are R2, or . Three model selection devices that we have
, and information criteria. Which of these devices may be used here?
a. All of them work fine.
b. We should not use R2, but and information criteria work fine. c. We should not use R2 or
, but information criteria work fine.
d. None of them should be used here. 5. Consider the model
. What needs to be true in order for the
total effect of x on y to be equal to the partial effect of x on y?
a. and Corr[ x , z ] = 0. b. , it does not matter what Corr[ x , z ] is. c. Corr[ x , z ] = 0, it does not matter what is. d. One of
and Corr[ x , z ] needs to be zero, it does not matter which one.
6. Which of the following is NOT always equal to zero in the multivariate
regression model?
The correlation between the fitted values and the
residuals.
The correlation between the residuals and any of the
regressors.
The correlation between the fitted values and any of
the regressors.
The sum of all residuals. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20.

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