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10. The following table is computed using data from 2012 from the Current Population Survey. The data set consists of information on 7440 full-time, full-year workers. The highest educa- || tional achievement for each worker was either a high school diploma or a bachelor's degree. College (X) Dependent Variable: Avg Hourly Earnings (AHE) | (1) | (2) | (3) 8.31 8.32 8.34 The workers' age ranged from 25 to 34 years. The data set also contains information on the (0.23) (0.22) | (0.22) region of the country where the person lived, marital status, and the number of children. For Female (X2) the purposes of the exercises, let: -3.85 -3.81 -3.80 (0.23) (0.22) | (0.22) AHE=average hourly earnings (in 2012 dollars) Age (Xa) 0.51 0.52 College=binary variable (1 if college or more than college education, 0 if less than a college | (0.04) (0.04) education) Northeast (X4) 0.18 (0.36) Female=binary variable (1 if female, 0 if male) Midwest (Xs) -1.23 Age= age (in years) Northeast=binary variable (1 if Region=Northeast, 0 otherwise), including states such as Massachusetts, New Hampshire, and New Jersey (0.31) South (Xa) -0.43 (0.30) Midwest=binary variable (1 if Region=Midwest, 0 otherwise), including states such as Michi- gan, Minnesota, and Missouri South= binary variable (1 if Region = South, 0 otherwise), including states such as Louisiana, latercept 17.02 1.87 2.05 (0.17) | (1.18) | (1.18) Summary Statistics and Joint Tests and Texas P-value for test that coefficients West = binary variable (1 if Region=West, 0 otherwise), including states such as California, on Be = 0, 8, = 0, & ß = 0 0.0001 Oregon and Washington 0.162 0.180 0.181

10. The following table is computed using data from 2012 from the Current Population Survey. The data set consists of information on 7440 full-time, full-year workers. The highest educa- || tional achievement for each worker was either a high school diploma or a bachelor's degree. College (X) Dependent Variable: Avg Hourly Earnings (AHE) | (1) | (2) | (3) 8.31 8.32 8.34 The workers' age ranged from 25 to 34 years. The data set also contains information on the (0.23) (0.22) | (0.22) region of the country where the person lived, marital status, and the number of children. For Female (X2) the purposes of the exercises, let: -3.85 -3.81 -3.80 (0.23) (0.22) | (0.22) AHE=average hourly earnings (in 2012 dollars) Age (Xa) 0.51 0.52 College=binary variable (1 if college or more than college education, 0 if less than a college | (0.04) (0.04) education) Northeast (X4) 0.18 (0.36) Female=binary variable (1 if female, 0 if male) Midwest (Xs) -1.23 Age= age (in years) Northeast=binary variable (1 if Region=Northeast, 0 otherwise), including states such as Massachusetts, New Hampshire, and New Jersey (0.31) South (Xa) -0.43 (0.30) Midwest=binary variable (1 if Region=Midwest, 0 otherwise), including states such as Michi- gan, Minnesota, and Missouri South= binary variable (1 if Region = South, 0 otherwise), including states such as Louisiana, latercept 17.02 1.87 2.05 (0.17) | (1.18) | (1.18) Summary Statistics and Joint Tests and Texas P-value for test that coefficients West = binary variable (1 if Region=West, 0 otherwise), including states such as California, on Be = 0, 8, = 0, & ß = 0 0.0001 Oregon and Washington 0.162 0.180 0.181

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
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ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
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10. The following table is computed using data from 2012 from the Current Population Survey. Dependent Variable: Avg Hourly Earnings (AHE) The data set consists of information on 7440 full-time, full-year workers. The highest educa- | (1) | (2) | (3) tional achievement for each worker was either a high school diploma or a bachelor's degree. College (X) 8.31 8.32 8.34 The workers' age ranged from 25 to 34 years. The data set also contains information on the (0.23) (0.22) | (0.22) region of the country where the person lived, marital status, and the number of children. For Female (X2) the purposes of the exercises, let: AHE=average hourly earnings (in 2012 dollars) College=binary variable (1 if college or more than college education, 0 if less than a college -3.85 -3.81 -3.80 (0.23) (0.22) (0.22) Age (X3) 0.51 0.52 (0.04) (0.04) Northeast (X4) 0.18 education) Female=binary variable (1 if female, O if male) (0.36) Midwest (Xs) -1.23 Age= age (in years) Northeast=binary variable (1 if Region=Northeast, 0 otherwise), including states such as (0.31) South (Xs) -0.43 Massachusetts, New Hampshire, and New Jersey (0.30) Midwest=binary variable (1 if Region=Midwest, 0 otherwise), including states such as Michi- gan, Minnesota, and Missouri South= binary variable (1 if Region = South, O otherwise), including states such as Louisiana, Intercept 17.02 1.87 2.05 (0.17) | (1.18) | (1.18) Summary Statistics and Joint Tests and Texas p-value for test that coefficients West = binary variable (1 if Region=West, 0 otherwise), including states such as California, on B = 0, 8s = 0, & B6 = 0 0.0001 Oregon and Washington 0.162 0.180 0.181 Note: The four regions encompass all states in the U.S. 7440 7440 7440 (a) Using the regression results in column (1): Compute the 99% confidence interval for the effect of a gender on earnings. Based on the confidence interval, is there statistically significant evidence that women's average hourly earning is $4.50 less than men's? (b) Using the regression results in column (1): Interpret the coefficient on college in words. (c) Using the regression results in column (1): Does a college education have a statistically significant effect on earnings? Explain and use the appropriate statistical test to explain your answer. (d) We are primarily interested in the effect of a college education on earnings. Provide an example of a variable that could potentially result in omitted variable bias on our coefficient of interest. Explain why the variable could result in omitted variable bias and the direction (positive/negative) and magnitude (overestimate/underestimate) of the bias.
Transcribed Image Text:10. The following table is computed using data from 2012 from the Current Population Survey. Dependent Variable: Avg Hourly Earnings (AHE) The data set consists of information on 7440 full-time, full-year workers. The highest educa- | (1) | (2) | (3) tional achievement for each worker was either a high school diploma or a bachelor's degree. College (X) 8.31 8.32 8.34 The workers' age ranged from 25 to 34 years. The data set also contains information on the (0.23) (0.22) | (0.22) region of the country where the person lived, marital status, and the number of children. For Female (X2) the purposes of the exercises, let: AHE=average hourly earnings (in 2012 dollars) College=binary variable (1 if college or more than college education, 0 if less than a college -3.85 -3.81 -3.80 (0.23) (0.22) (0.22) Age (X3) 0.51 0.52 (0.04) (0.04) Northeast (X4) 0.18 education) Female=binary variable (1 if female, O if male) (0.36) Midwest (Xs) -1.23 Age= age (in years) Northeast=binary variable (1 if Region=Northeast, 0 otherwise), including states such as (0.31) South (Xs) -0.43 Massachusetts, New Hampshire, and New Jersey (0.30) Midwest=binary variable (1 if Region=Midwest, 0 otherwise), including states such as Michi- gan, Minnesota, and Missouri South= binary variable (1 if Region = South, O otherwise), including states such as Louisiana, Intercept 17.02 1.87 2.05 (0.17) | (1.18) | (1.18) Summary Statistics and Joint Tests and Texas p-value for test that coefficients West = binary variable (1 if Region=West, 0 otherwise), including states such as California, on B = 0, 8s = 0, & B6 = 0 0.0001 Oregon and Washington 0.162 0.180 0.181 Note: The four regions encompass all states in the U.S. 7440 7440 7440 (a) Using the regression results in column (1): Compute the 99% confidence interval for the effect of a gender on earnings. Based on the confidence interval, is there statistically significant evidence that women's average hourly earning is $4.50 less than men's? (b) Using the regression results in column (1): Interpret the coefficient on college in words. (c) Using the regression results in column (1): Does a college education have a statistically significant effect on earnings? Explain and use the appropriate statistical test to explain your answer. (d) We are primarily interested in the effect of a college education on earnings. Provide an example of a variable that could potentially result in omitted variable bias on our coefficient of interest. Explain why the variable could result in omitted variable bias and the direction (positive/negative) and magnitude (overestimate/underestimate) of the bias.
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