R =.78 n = 44 a. b. 1. Consider the following sample regression results: Y hat= 17000 + 1000 X1 + 3000 X2 + 5000 X (2121

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R 78 N 44 A B 1 Consider The Following Sample Regression Results Y Hat 17000 1000 X1 3000 X2 5000 X 2121 1 (53.48 KiB) Viewed 96 times

R =.78 n = 44 a. b. 1. Consider the following sample regression results: Y hat= 17000 + 1000 X1 + 3000 X2 + 5000 X (2121) (167.1) (365.7) (562.3) where Y = annual salary of the employee X1 = years of seniority of the employee Xz = 1 i B.A. is the highest degree of employee: 0 otherwise. X = 1 if M.S. or higher is highest degree of the employee; otherwise. Xt = 1 if high school or lower is highest degree: 0 otherwise. The values in parentheses are estimated standard errors of the coefficients. Why was the variable Xe excluded from the model? Is the sign of the coefficient for the Xz variable plausible? Explain. Predict the salary of an employee with a B.A. degree and 10 years of seniority. Predict the salary of an employee with a high school degree and 10 years of seniority Is a year of seniority worth less to a high school graduate that to a person with a Ph.D. degree? Explain f. At the 5% level of significance, would you conclude that, on average, an employee with a Ph.D. degree has a higher salary that an employee with a high school degree? 9. At the 1% level of significance, is there evidence that this particular model explains a statistically significant portion of the variation in annual salary? h. Suppose you were to re-estimate the original model and replace the variable X2 with the variable Xe What would be the new estimated y-intercept? 1. Suppose you were to re-estimate the original model and replace the variable Xs with the variable X. What would the signs be for the coefficients for the variables X2 and X ? c. d. e. SIGN OF X2 SIGN OF XA