Refer to the table of estimated regressions on page 228, com-puted using data for 2015 from the Current Population Surve
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this regression statistically significant at the 5% level?
Construct a 95% confidence interval of the difference.
b. Is the male–female earnings difference estimated from this
regression statistically significant at the 5% level? Construct a
95% confidence interval for the difference.
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. (Hint: What would happen if you included West and
excluded Midwest from the regression?)
Refer to the table of estimated regressions on page 228, com-puted using data for 2015 from the Current Population Survey which consists of information on 7178 full-time, full-year workers. The highest educational achieve-ment for each worker was either a high school diploma or a bachelor's degree. The workers' ages ranged from 25 to 34 years. The data set also contains information on the region of the country where the person lived, marital status, and number of chil-dren. For the purposes of these exercises, let 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) (2) (3) College (X1) 10.47 (0.29) 10.44 (0.29) 10.42 (0.29) Female (X2) -4.69 (0.29) -4.56 (0.29) -4.57 (0.29) Age (X3) 0.61 (0.05) 0.61 (0.05) Northeast (X4) 0.74 (0.47) Midwest (Xs) -1.54 (0.40) South (X) -0.44 (0.37) Intercept 18.15 (0.19) 0.11 (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 R2 0.165 0.182 0.185 n 7178 7178 7178