1. Write down the econometric model that this OLS regression estimates 2. Interpret the belavg coefficient. 3. At how many years of experience is the marginal effect of experience on predicted wage equal to zero? 4. What is predicted effect of an increasing work experience from 5 to 6 years on wage? 5. What is the predicted hourly wage for an average looking worker with 10 years of experience?
1. Write down the econometric model that this OLS regression estimates 2. Interpret the belavg coefficient. 3. At how many years of experience is the marginal effect of experience on predicted wage equal to zero? 4. What is predicted effect of an increasing work experience from 5 to 6 years on wage? 5. What is the predicted hourly wage for an average looking worker with 10 years of experience?
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
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Problem 1P
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Hello, was just wondering if anyone could help out with these? All of the info required should be in the photo attached. Thanks.
1. Write down the econometric model that this OLS regression estimates
2. Interpret the belavg coefficient.
3. At how many years of experience is the marginal effect of experience on predicted
wage equal to zero?
4. What is predicted effect of an increasing work experience from 5 to 6 years on wage?
5. What is the predicted hourly wage for an average looking worker with 10 years of
experience?
![Consider the following OLS regression output
Linear regression
wage
be lavg
abvavg
exper
expersq
_cons
Robust
std. err.
Coefficient
t P>|t|
-1.269988
.2589818
-4.00 0.000
0.84 0.402
10.69 0.000
.3314639
-.0054869
0007058 -7.77 0.000
2.95287 .2500308 11.81 0.000
Number of obs
F(4, 1255)
Prob > F
R-squared
Root MSE
.3172118
.3090035
. 0310071
||||||||||
=
=
=
1,260
46.49
0.0000
0.0908
4.4511
[95% conf. interval]
-1.892312
-.6476643
-.3472386
.8652022
.2706325
.3922954
-.0068716 -.0041021
3.443394
2.462345
where wage is hourly wage in US$, belavg is a dummy variable which is equal to 1 if the
person is below average looking (and 0 otherwise), abvavg is a dummy variable which is
equal to 1 if the person is above average looking (and 0 otherwise), exper is years of work
experience, and expersq is experience squared (exper* exper). Assume that MLR 1-6 hold.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Ffd09c145-5240-491d-9cee-d827ecae77c8%2F13620e4c-e466-43f3-99b0-69b1e56ad63c%2Fd5o00e_processed.png&w=3840&q=75)
Transcribed Image Text:Consider the following OLS regression output
Linear regression
wage
be lavg
abvavg
exper
expersq
_cons
Robust
std. err.
Coefficient
t P>|t|
-1.269988
.2589818
-4.00 0.000
0.84 0.402
10.69 0.000
.3314639
-.0054869
0007058 -7.77 0.000
2.95287 .2500308 11.81 0.000
Number of obs
F(4, 1255)
Prob > F
R-squared
Root MSE
.3172118
.3090035
. 0310071
||||||||||
=
=
=
1,260
46.49
0.0000
0.0908
4.4511
[95% conf. interval]
-1.892312
-.6476643
-.3472386
.8652022
.2706325
.3922954
-.0068716 -.0041021
3.443394
2.462345
where wage is hourly wage in US$, belavg is a dummy variable which is equal to 1 if the
person is below average looking (and 0 otherwise), abvavg is a dummy variable which is
equal to 1 if the person is above average looking (and 0 otherwise), exper is years of work
experience, and expersq is experience squared (exper* exper). Assume that MLR 1-6 hold.
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