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The data set consists of information on 4500 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 = hinary variable (1 if college O if high school)

The data set consists of information on 4500 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 = hinary variable (1 if college O if high school)

College Algebra
College Algebra
1st Edition
ISBN:9781938168383
Author:Jay Abramson
Publisher:Jay Abramson
Chapter9: Sequences, Probability And Counting Theory
Section9.5: Counting Principles
Problem 5SE: Answer the following questions. 5. What is the term for the arrangement that selects r objects from...
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Summary Statistics SER 5.77 5.72 5.71 0.162 0.175 0.178 0.162 0.174 0.177 4500 4500 4500 Using the regression results in column (2): On average, a worker earns $ per hour for each year that he or she ages. Is age an important determinant of earnings? O A. Yes, because wages are not consistent across different age groups. O B. No, because the difference in wages is minimal. O C. Yes, because younger workers earn more than older workers. O D. No, because older workers do not earn significantly more than younger workers. Sally is a 27-year-old female college graduate. Betsy is a 39-year-old female college graduate. Predict Sally's and Betsy's earnings. Sally's earnings prediction is $ per hour. (Round your response to two decimal places.) Betsy's earnings prediction is $per hour. (Round your response to two decimal places.) The expected difference in earnings between Sally and Betsy is $ per hour. (Round your response to two decimal places.)
Transcribed Image Text:Summary Statistics SER 5.77 5.72 5.71 0.162 0.175 0.178 0.162 0.174 0.177 4500 4500 4500 Using the regression results in column (2): On average, a worker earns $ per hour for each year that he or she ages. Is age an important determinant of earnings? O A. Yes, because wages are not consistent across different age groups. O B. No, because the difference in wages is minimal. O C. Yes, because younger workers earn more than older workers. O D. No, because older workers do not earn significantly more than younger workers. Sally is a 27-year-old female college graduate. Betsy is a 39-year-old female college graduate. Predict Sally's and Betsy's earnings. Sally's earnings prediction is $ per hour. (Round your response to two decimal places.) Betsy's earnings prediction is $per hour. (Round your response to two decimal places.) The expected difference in earnings between Sally and Betsy is $ per hour. (Round your response to two decimal places.)
The data set consists of information on 4500 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, 0 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) %3D %3D %3D %3D %3D %3D %3D 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) C College (X, ) Female (X2) 5.02 5.04 5.00 - 2.43 - 2.41 - 2.41 Age (X3) 0.27 0.27 Northeast (X4) 0.63 Midwest (X5) 0.55 1 South (X6) - 0.25 Intercept 11.67 4.05 3.45
Transcribed Image Text:The data set consists of information on 4500 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, 0 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) %3D %3D %3D %3D %3D %3D %3D 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) C College (X, ) Female (X2) 5.02 5.04 5.00 - 2.43 - 2.41 - 2.41 Age (X3) 0.27 0.27 Northeast (X4) 0.63 Midwest (X5) 0.55 1 South (X6) - 0.25 Intercept 11.67 4.05 3.45
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