Consider the simple linear regression model: Wage B1+ẞ2 educ+e, where = 1. Wage means daily wage measured in dollars. 2. educ means years of education (schooling) The regression output is given below: . reg wage educ Source | SS df MS Number of obs = 100 F(2, 97) = 97.14 Model | 10000.00 Residual | 10000.00 1 5000.00 Prob > F = 0.0000 98 100.00 R-squared = 0.5000 Adj R-squared = 0.4900 Total | 20000.00 99 200.00 Root MSE = 10.000 wage | Coef. Std. Err. t P>|t| [95% Conf. Interval] educ _cons | 10.0000 10.0000 5.0000 10.0000 2.00 1.00 0.050 0.317 0.0000 -20.0000 20.0000 30.0000

Consider the simple linear regression model: Wage B1+ẞ2 educ+e, where = 1. Wage means daily wage measured in dollars. 2. educ means years of education (schooling) The regression output is given below: . reg wage educ Source | SS df MS Number of obs = 100 F(2, 97) = 97.14 Model | 10000.00 Residual | 10000.00 1 5000.00 Prob > F = 0.0000 98 100.00 R-squared = 0.5000 Adj R-squared = 0.4900 Total | 20000.00 99 200.00 Root MSE = 10.000 wage | Coef. Std. Err. t P>|t| [95% Conf. Interval] educ _cons | 10.0000 10.0000 5.0000 10.0000 2.00 1.00 0.050 0.317 0.0000 -20.0000 20.0000 30.0000

Advanced Engineering Mathematics

10th Edition

ISBN:9780470458365

Author:Erwin Kreyszig

Publisher:Erwin Kreyszig

Chapter2: Second-order Linear Odes

Section: Chapter Questions

Problem 1RQ

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Question

- What is the expected value of the Wage for a person with 15 years of education?

(a) 15

(b) 25

(d) 250

(e) 160

- What is the expected value of the marginal effect of an additional year of education for a person with 15 years of education?

(a) 100

(b) 10

(d) 15

(e) 115

- What is the expected value of the elasticity of education on wages for a person with 15 years of education at its expected wage?

(a) 1.5

(b) 150

(d) 1

(e) 160 / 150

Consider the simple linear regression model:
Wage B1+ẞ2 educ+e, where
=
1. Wage means daily wage measured in dollars.
2. educ means years of education (schooling)
The regression output is given below:
. reg wage educ
Source |
SS
df
MS
Number of obs
=
100
F(2, 97)
=
97.14
Model | 10000.00
Residual | 10000.00
1
5000.00
Prob > F
=
0.0000
98
100.00
R-squared
=
0.5000
Adj R-squared
=
0.4900
Total | 20000.00
99
200.00
Root MSE
=
10.000
wage |
Coef.
Std. Err.
t
P>|t|
[95% Conf. Interval]
educ
_cons |
10.0000
10.0000
5.0000
10.0000
2.00
1.00
0.050
0.317
0.0000
-20.0000
20.0000
30.0000

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