1 Consider an equation to explain salaries of CEOs in terms of annual firm sales, return on equity (roe, in percentage form), and return on the firm's stock (ros, in percentage form): log(salary) = Be + Bilog(sales) + B-roe + Bxros + u. (1) In terms of the model parameters, state the null hypothesis that, after controlling for sales and roe, ros has no effect on CEO salary, State the alternative that better stock market performance increases a CEO's salary. (2) Using the data in CEOSALI, the following equation was obtained by OLS: log(salary) = 4.32 + 280 log(sales) + .0174 roe +.00024 ros (-32) (035) (.0041) (.00054) n = 209, R2 = .283. By what percentage is salary predicted to increase if ros increases by 50 points? Does ros have a practically large effect on salary? (3) Test the null hypothesis that ros has no effect on salary against the alternative that ros has a positive effect. Carry out the test at the 10% significance level. (4) Would you include ros in a final model explaining CEO compensation in terms of firm perfor- mance? Explain