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The dataset consists of information on 3200 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 high school) Female binary variable (1 if female. O if male) Age age (in years) Ntheast binary variable (1 i Region Northeast otherwise) 0 Midwest binary variable (1 if Region Midwest 0 otherwise) Southbinary variable (1 ir Region South Otherwise) Weat binary variable (1 ir 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 (AME) Regressor (1) (2) 5.30 5.32 5.28 -2.50 -2.54 -2.54 0.28 0.28 0.67 0.58 -0.20 3.04 4.27 6.02 0.188 3200 Compute for each of the regressions for column (1) for column (2) for column (3) (Round your response to three decimal places) (Round your response to three decimal places) (Round your response to three decimal places) Colege (X₂) Female (₂) Age (₂) Northeast (₂) Midwest (₂) South (X₂) Intercept Summary Statistics SER R² 12.31 0.171 3200 6.03 0.104 3200

The dataset consists of information on 3200 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 high school) Female binary variable (1 if female. O if male) Age age (in years) Ntheast binary variable (1 i Region Northeast otherwise) 0 Midwest binary variable (1 if Region Midwest 0 otherwise) Southbinary variable (1 ir Region South Otherwise) Weat binary variable (1 ir 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 (AME) Regressor (1) (2) 5.30 5.32 5.28 -2.50 -2.54 -2.54 0.28 0.28 0.67 0.58 -0.20 3.04 4.27 6.02 0.188 3200 Compute for each of the regressions for column (1) for column (2) for column (3) (Round your response to three decimal places) (Round your response to three decimal places) (Round your response to three decimal places) Colege (X₂) Female (₂) Age (₂) Northeast (₂) Midwest (₂) South (X₂) Intercept Summary Statistics SER R² 12.31 0.171 3200 6.03 0.104 3200

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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The data set consists of information on 3200 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, O 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) 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) 5.30 5.32 -2.54 5.28 -2.54 -2.56 0.28 College (X₁) Female (X₂) Age (X3) Northeast (X₂) Midwest (X₂) South (XB) 0.28 0.67 0.58 -0.26 3.64 Intercept 12.31 4.27 Summary Statistics SER 6.08 0.171 6.02 0.188 2 R² Д 3200 3200 Compute R for each of the regressions. ² for column (1) is. (Round your response to three decimal places.) R² for column (2) is. (Round your response to three decimal places.) ² for column (3) is. (Round your response to three decimal places.) Using the regression results in column (1): Workers with college degrees earn $ per hour ▼ on average, than workers with only high school degrees. (Round your response to two decimal places.) Men earn $ per hour than women on average. (Round your response to two decimal places.) 6.03 0.184 3200
Transcribed Image Text:The data set consists of information on 3200 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, O 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) 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) 5.30 5.32 -2.54 5.28 -2.54 -2.56 0.28 College (X₁) Female (X₂) Age (X3) Northeast (X₂) Midwest (X₂) South (XB) 0.28 0.67 0.58 -0.26 3.64 Intercept 12.31 4.27 Summary Statistics SER 6.08 0.171 6.02 0.188 2 R² Д 3200 3200 Compute R for each of the regressions. ² for column (1) is. (Round your response to three decimal places.) R² for column (2) is. (Round your response to three decimal places.) ² for column (3) is. (Round your response to three decimal places.) Using the regression results in column (1): Workers with college degrees earn $ per hour ▼ on average, than workers with only high school degrees. (Round your response to two decimal places.) Men earn $ per hour than women on average. (Round your response to two decimal places.) 6.03 0.184 3200
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