Assume That You Have Collected Cross-Sectional Data for Average Hourly

Question

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The Answer is a.For every additional year of education,you receive $1.71 additional earnings.It is best not to interpret the intercept,since there are no (or extremely few)observations at the origin.
b.The regression R2 cannot decrease if you add an explanatory variable.If the additional variable does not contribute anything to the fit,then this measure will remain the same.However,in practice,this does not happen.The standard error is a measure of the SSR,and these will almost always decrease with the addition of an explanatory variable.As a result,the observed pattern in the two statistics is to be expected.
c.There are two conditions for omitted variable bias to be present.First,DFemme must be a determinant of ahe;and second,it must be correlated with educ.Given that you have not learned how to test for statistical significance in the multiple regression model,the first question is hard to determine at this point.However,you might argue that the coefficient seems large and that you have read elsewhere that there is evidence of females earning less using this type of equation.With regard to the second question,you could argue that the coefficient on educ has changed somewhat,although the increase does not seem to be large ($0.05).For there to be a correlation between education and the binary female variable,you would have to argue that males and females receive years of education.Either way,the omitted variable bias in the first equation does not appear to be large.

Assume That You Have Collected Cross-Sectional Data for Average Hourly

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