The data set consists of information on 3600 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 containe information on the region of the country where the person lived, marital status, and number of children. For the purposes of these exercises, let HE= average hourly eamings (in 2005 dollars) College binary variable (1 if college, 0 high school) Female binary variable (1 if female, 0 if male) Age-age (in years) theast-binary variable (1 if Region Northeast 0 otherwise) idwest-binary variable (1 i Region Midwest 0 otherwise) South-binary variable (1 i 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 College (X₂) Female (X₂) Age (X₂) Northeast (X) Midwest (X) South (X) Intercept Summary Statistics (1) R n 5.13 (0.20) -2.48 (0.19) 11.93 (0.13) F-statistic for regional effects=0 SER 5.89 0.105 3000 (2) 5.15 (0.20) -2.48 (0.10) 0.27 (0.04) 4.14 (0.90) 5.85 0.179 3000 Using the regression results in column (1) The f-statistic for the college-high school earnings difference estimated from this regression is (Round your response to two decimal places) es the college-high school earnings difference estimated from this regression statistically significant at the 1% level? Since the absolute value of the I-statistic is (3) 5.11 (0.20) <-2.46 (0.19) 0.27 (0.04) 0.65 (0.28) 0.50 (0.26) -0.25 (0.24) 3.53 (1.00) 6.43 5.84 0.182 3600 than the critical value for 99% confidence, the college-high school earnings difference estimated from this regression statistically significant at the 1% level
The data set consists of information on 3600 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 containe information on the region of the country where the person lived, marital status, and number of children. For the purposes of these exercises, let HE= average hourly eamings (in 2005 dollars) College binary variable (1 if college, 0 high school) Female binary variable (1 if female, 0 if male) Age-age (in years) theast-binary variable (1 if Region Northeast 0 otherwise) idwest-binary variable (1 i Region Midwest 0 otherwise) South-binary variable (1 i 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 College (X₂) Female (X₂) Age (X₂) Northeast (X) Midwest (X) South (X) Intercept Summary Statistics (1) R n 5.13 (0.20) -2.48 (0.19) 11.93 (0.13) F-statistic for regional effects=0 SER 5.89 0.105 3000 (2) 5.15 (0.20) -2.48 (0.10) 0.27 (0.04) 4.14 (0.90) 5.85 0.179 3000 Using the regression results in column (1) The f-statistic for the college-high school earnings difference estimated from this regression is (Round your response to two decimal places) es the college-high school earnings difference estimated from this regression statistically significant at the 1% level? Since the absolute value of the I-statistic is (3) 5.11 (0.20) <-2.46 (0.19) 0.27 (0.04) 0.65 (0.28) 0.50 (0.26) -0.25 (0.24) 3.53 (1.00) 6.43 5.84 0.182 3600 than the critical value for 99% confidence, the college-high school earnings difference estimated from this regression statistically significant at the 1% level
Holt Mcdougal Larson Pre-algebra: Student Edition 2012
1st Edition
ISBN:9780547587776
Author:HOLT MCDOUGAL
Publisher:HOLT MCDOUGAL
Chapter7: Percents
Section7.5: Percent Of Change
Problem 26E
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Transcribed Image Text:The data set consists of information on 3800 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 eamings (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)
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
College (X₁)
Female (X₂)
Age (X3)
Northeast (X₂)
Midwest (X)
South (X)
Intercept
Summary Statistics
(1)
Using the regression results in column (1)
regression is
The f-statistic for the college-high school earnings difference estimated from
Is the college-high school earnings difference estimated from this regression statistically significant at the 1% level?
Since the absolute value of the t-statistic is
5.13
(0.20)
-2.48
(0.19)
11.93
(0.13)
F-statistic for regional effects=0
SER
5.89
R
0.105
n
3600
(2)
5.15
(0.20)
-2.48
(0.19)
0.27
(0.04)
4.14
(0.90)
5.85
0.179
3600
(Round your response to two decimal places.)
5.11
(0.20)
-2.40
(0.19)
0.27
(0.04)
0.65
(0.28)
0.56
(0.26)
-0.25
(0.24)
3.53
(1.00)
6.43
5.84
0.182
3600
than the critical value for 99% confidence, the college-high school earnings difference estimated from this regression
statistically significant at the 1% level.
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