The median starting salary for new law school graduates is d

The median starting salary for new law school graduates is determined by log(salary) = beta0 + beta1LSAT + bebta2GPA + beta3log (libvol) + beta4log (cost) + beta5rank + u, where LSAT is the median LSAT score for the graduating class, GPA is the median college GPA for the class, libvol is the number of volumes in the law school library, cost is the annual cost of attending law school, and rank is a law school ranking (with rank = 1 being the best). (i) Explain why we expect beta5 0. What signs do you expect for the other slope parameters? Justify your answers. Using the data in LAWSCH85.RAW, the estimated equation is = 8.34 + .0047 LSAT + .248 GPA + .095 log (libvol) + .038 log(cost) - .0033 rank n = 136, R2 = .842. What is the predicted ceteris paribus difference in salary for schools with a median GPA different by one point? (Report your answer as a percentage.) Interpret the coefficient on the variable log (libvol). Would you say it is better to attend a higher ranked law school? How much is a difference in ranking of 20 worth in terms of predicted starting salary? Which of the following can cause OLS estimators to be biased? Heteroskedasticity. Omitting an important variable. A sample correlation coefficient of .95 between two independent variables both included in the model. Suppose that average worker productivity at manufacturing firms (avgprod) depends on two factors, average hours of training (avgtrain) and average worker ability (avgabil): avgprod = beta0 + beta1avgtrain + beta2avgabil + u. Assume that this equation satisfies the Gauss-Markov assumptions. If grants have been given to firms whose workers have less than average ability, so that avgtrain and avgabil are negatively correlated, what is the likely bias in obtained from the simple regression of avgprod on avgtrain?

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