Using the regression results in column (3):

a. Do there appear to be important regional differences? Use an appropriate hypothesis test to explain your answer.

b. Juanita is a 28-year-old female college graduate from the South. Molly is a 28 -year-old female college graduate from the West. Jennifer is a 28-year-old female college graduate from the Midwest.

i. Construct a 95\% confidence interval for the difference in expected earnings between Juanita and Molly.

ii. Explain how you would construct a 95\% confidence interval for the difference in expected earnings between Juanita and Jennifer. What would happen if you included West and excluded Midwest from the regression?

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AHE average hourly earnings College = binary variable (1 if college, 0 if high school) Female = binary variable (1 if female,0 if male) Age = (in years) Northeast = binary variable (1 if Region = Northeast, 0 otherwise) Midwest = binary variable (1 if Region Midwest, 0 otherwise) South = binary variable (1 if Region = South, 0 otherwise) West = binary variable (1 if Region = West, 0 otherwise) Results of Regressions of Average Hourly Earnings on Sex and Education Binary Variables and Other Characteristics Using 2015 Data from the Current Population Survey Dependent variable: average hourly earnings (AHE). Regressor (1) College (X1) Female (X2) Age (X3) Northeast (X4) Midwest (X's) (2) (3) 10.47 10.44 10.42 (0.29) (0.29) (0.29) -4.69 -4.56 (0.29) (0.29) -4.57 (0.29) 0.61 (0.05) 0.61 (0.05) 0.74 (0.47) -1.54 (0.40) South (X) -0.44 (0.37) Intercept 18.15 0.11 (0.19) (1.46) 0.33 (1.47) Summary Statistics and Joint Tests F-statistic testing regional effects =0 9.32 SER 12.15 12.03 12.01 R 0.165 0.182 0.185 7178 7178 7178 n