20. Hourly Wage (USD) = 0.25 + 0.05*Education - 0.45*Female a. The marginal impact of Education is 0.05 b. The marginal impact of Female is 0.45 c. The constant term is 0.25 d. All the above

21. In this estimation output: Hourly Wage $= 0.25 + 0.05*Education + 0.02*Experience 0.02 * Experience Squared a. The total impact of experience is 0 b. The partial impact of education is 0.05 c. The total impact of experience is found through d(Y)/Experience d. A and B

e. A and C f. B and C 22. Using this estimation output, build a 95% confidence interval for (education) knowing that the standard error of (education) is 0.025. Sample size = 450 Observations. Hourly Wage $= 0.25 + 0.05*Education + 0.02*Experience 0.02 * Experience Squared

a. Upper Bound: 0.099 and Lower Bound: 0.001 b. Upper Bound: 0.091 and Lower Bound: 0.009 c. Upper Bound: 0.114 and Lower Bound: -0.014 d. Upper Bound: 0.114 and Lower Bound: 0.014 23. In this estimation output: Hourly Wage $= 0.25 + 0.05*Education + 0.02*Experience 0.02 * Female a. Gender dummy is statistically significant b. Impact of Gender on Y is found to be negative c. No inference can be drawn with respect to the gender dummy d. None of the above e. All the above

(note: we lack the standard error, thus Hypothesis testing cannot be employed)

24. In this estimation output: R-squared = 0.45 Hourly Wage $= 0.25 + 0.05*Education + 0.02*Experience 0.02 * Female a. The coefficient of determination is 45% b. The explained variation of hourly wages is 55% c. None of the regressors are significant at any conventional significance levels d. All regressors are significant at any conventional significance levels 25. In this estimation output: R-squared = 0.45

Hourly Wage $= 0.25 + 0.05*Education + 0.02*Experience 0.02 * Female

a. The coefficient of determination is 45% b. The explained variation of hourly wages is 55% c. The unexplained variation of hourly wages is 55% d. A and C e. A and B 26. Irrelevant-variable bias occurs when a statistical model leaves out one or more relevant variables.

(True / False)

27. Omitted-variable bias (OVB) occurs when a statistical model leaves out one or more relevant variables. (True / False) 28. The presence of OVB (omitted variable bias) violates the Zero-Conditional Mean Assumption. (True / False)

29. When the regression model is found to suffer from Omitted Variable Bias (OVB): a. One is likely to understate the impact of the regressors on Y b. One is likely to overstate the impact of the regressors on Y c. It means that the omitted variable is correlated with the dependent variable and with at least one other explanatory variable. d. None of the above