SER 6.46 6.41 6.40 R² 0.181 0.196 0.200 R² 0.181 0.195 0.199 n 4300 4300 4300 Using the regression results in column (3): Workers in the Northeast earn $.71 more per hour than workers in the West, on average, controlling for other variables in the regression. (Round your response to two decimal places.) Workers in the South earn $.28 less per hour than workers in the West, on average, controlling for other variables in the regression. (Round your response to two decimal places.) Do there appear to be important regional differences? A. Yes, because workers in the Northeast earn significantly more than workers in the South. B. No, because workers in the Midwest earn more per hour than workers in the Northeast, while workers in the South earn less per hour than workers in the West. C. No, because the difference in wages is minimal. D. Yes, because wages are not consistent across the region. Why is the regressor West omitted from the regression? What would happen if it was included? A. The regressor West is omitted to avoid perfect multicollinearity. If West is included, then the OLS estimator cannot be computed in this situation. B. The regressor West is omitted to make the OLS estimator linear. If West is included, then the intercept can no longer be written as a perfect linear function of the four regional regressors. C. The regressor West is omitted to minimize the sum of squared residuals. If West is included, then the sum of squared residuals is no longer minimized. D. The regressor West is omitted to avoid the OLS estimator bias. If West is included, then the OLS estimator is biased. Juanita is a 35-year-old female college graduate from the South. Jennifer is a 35-year-old female college graduate from the Midwest. Calculate the expected difference in earnings between Juanita and Jennifer. The expected difference in earnings between Juanita and Jennifer is $ per hour. (Round your response to two decimal places.) The data set consists of information on 4300 full-time full-year workers. The highest educational achievement for each worker was either a high school diploma or a bachelor's degree. The worker's ages ranged from 25 to 45 years. The data set also contained information on the region of the country where the person lived, marital status, and number of children. For the purposes of these exercises, let AHE = average hourly earnings (in 2005 dollars) College = binary variable (1 if college, 0 if high school) Female = binary variable (1 if female, O if male) Age = age (in years) Ntheast = 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 Gender and Education Binary Variables and Other Characteristics Using Data from the Current Population Survey Dependent variable: average hourly earnings (AHE). Regressor (1) (2) (3) College (X1) Female (X2) 5.62 - 2.72 5.64 -2.70 5.60 - 2.70 Age (X3) 0.30 0.30 Northeast (X4) 0.71 Midwest (X5) 0.62 South (X6) - 0.28 Intercept 13.07 4.53 3.86 Summary Statistics SER R² 6.46 6.41 6.40 0.181 0.196 0.200