Part 3: Interpretation of OLS regression Consider the following OLS regression output which is estimated with Wage2.dta (see tutorial folder on Blackboard) Source s df MS Number of obs 935 36.82 F(4, 930) Prob > F Model 22.6467366 4 5.66168416 0.0000 Residual 143.009547 930 .153773706 0.1367 R-squared Adj R-squared 0.1330 .39214 Total 165.656283 934 .177362188 Root MSE lwage Coef. Std. Err. t P>|t| [95% Conf. Interval) educ .0400982 .0068351 5.87 0.000 .0266843 .0535122 IQ .005914 .0009967 5.93 0.000 .0039579 .0078701 .013037 .0001298 .3240363 0.783 0.517 hours .0035899 0.28 -.0219954 .0291752 hours2 -.0000842 -0.65 -.0003389 .0001706 cons 5.649044 17.43 0.000 5.013117 6.284971 where Iwage is the natural logarithm of monthly wage in US$, educ is years of education, IQ is points on an IQ intelligence test, hours is average weekly hours worked, and hours2 is experience squared (hours * hours). Assume that MLR 1-6 hold. 2 8. Write down the econometric model that this OLS regression estimates. 9. Interpret the educ coefficient. 10. At how many average weekly hours worked is the marginal effect of hours on predicted wage equal to zero? 11. What is the exact effect (in percent) of an increase of 10 years of education on predicted wage? | 12. What is the predicted monthly wage in US$ for a worker with 10 years of education, an IQ score of l100, who works on average 40 hours per week?
Part 3: Interpretation of OLS regression Consider the following OLS regression output which is estimated with Wage2.dta (see tutorial folder on Blackboard) Source s df MS Number of obs 935 36.82 F(4, 930) Prob > F Model 22.6467366 4 5.66168416 0.0000 Residual 143.009547 930 .153773706 0.1367 R-squared Adj R-squared 0.1330 .39214 Total 165.656283 934 .177362188 Root MSE lwage Coef. Std. Err. t P>|t| [95% Conf. Interval) educ .0400982 .0068351 5.87 0.000 .0266843 .0535122 IQ .005914 .0009967 5.93 0.000 .0039579 .0078701 .013037 .0001298 .3240363 0.783 0.517 hours .0035899 0.28 -.0219954 .0291752 hours2 -.0000842 -0.65 -.0003389 .0001706 cons 5.649044 17.43 0.000 5.013117 6.284971 where Iwage is the natural logarithm of monthly wage in US$, educ is years of education, IQ is points on an IQ intelligence test, hours is average weekly hours worked, and hours2 is experience squared (hours * hours). Assume that MLR 1-6 hold. 2 8. Write down the econometric model that this OLS regression estimates. 9. Interpret the educ coefficient. 10. At how many average weekly hours worked is the marginal effect of hours on predicted wage equal to zero? 11. What is the exact effect (in percent) of an increase of 10 years of education on predicted wage? | 12. What is the predicted monthly wage in US$ for a worker with 10 years of education, an IQ score of l100, who works on average 40 hours per week?
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
Related questions
Question
just need the answers to 10, 11 & 12 please
Transcribed Image Text:Part 3: Interpretation of OLS regression
Consider the following OLS regression output which is estimated with Wage2.dta (see
tutorial folder on Blackboard)
Source
s
df
MS
Number of obs
935
36.82
F(4, 930)
Prob > F
Model
4 5.66168416
.153773706
22.6467366
0.0000
Residual
143.009547
930
0.1367
R-squared
Adj R-squared
0.1330
.39214
Total
165.656283
934
.177362188
Root MSE
lwage
Coef.
Std. Err.
t
P>|t|
[95% Conf. Interval)
educ
.0400982
.0068351
5.87
0.000
.0266843
.0535122
IQ
.005914
.0009967
5.93
0.000
.0039579
.0078701
.013037
.0001298
0.783
0.517
hours
.0035899
0.28
-.0219954
.0291752
hours2
-.0000842
-0.65
-.0003389
.0001706
cons
5.649044
.3240363
17.43
0.000
5.013117
6.284971
where Iwage is the natural logarithm of monthly wage in US$, educ is years of education, IQ
is points on an IQ intelligence test, hours is average weekly hours worked, and hours2 is
experience squared (hours * hours). Assume that MLR 1-6 hold.
2
8. Write down the econometric model that this OLS regression estimates.
9. Interpret the educ coefficient.
10. At how many average weekly hours worked is the marginal effect of hours on
predicted wage equal to zero?
11. What is the exact effect (in percent) of an increase of 10 years of education on
predicted wage? |
12. What is the predicted monthly wage in US$ for a worker with 10 years of education,
an IQ score of l100, who works on average 40 hours per week?
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