econ2p91_ReviewQuestionsMultipleChoice_Chapters6_7 - Review...

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Chapters 6 and 7 Linear Regression with Multiple Regressors 1) In the multiple regression model, the adjusted R 2 , 2 R .a cannot be negative. .b will never be greater than the regression R 2 . .c equals the square of the correlation coefficient r . .d cannot decrease when an additional explanatory variable is added. )2 Under imperfect multicollinearity .a the OLS estimator cannot be computed. .b two or more of the regressors are highly correlated. .c the OLS estimator is biased even in samples of n > 100. .d the error terms are highly, but not perfectly, correlated. 3) Imagine you regressed earnings of individuals on a constant, a binary variable (“ Male ”) which takes on the value 1 for males and is 0 otherwise, and another binary variable (“ Female ”) which takes on the value 1 for females and is 0 otherwise. Because females typically earn less than males, you would expect a. the coefficient for Male to have a positive sign, and for Female a negative sign. .b both coefficients to be the same distance from the constant, one above and the other below. .c none of the OLS estimators to exist because there is perfect multicollinearity. .d this to yield a difference in means statistic. 4) If you had a two regressor regression model, then omitting one variable which is relevant .a will have no effect on the coefficient of the included variable if the correlation between the excluded and the included variable is negative. .b will always bias the coefficient of the included variable upwards. .c can result in a negative value for the coefficient of the included variable, even though the coefficient will have a significant positive effect on Y if the omitted variable were included. .d
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This note was uploaded on 04/28/2011 for the course ECON 2P91 taught by Professor Ogwang during the Winter '09 term at Brock University, Canada.

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econ2p91_ReviewQuestionsMultipleChoice_Chapters6_7 - Review...

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