MATLAB: An Introduction with Applications
6th Edition
ISBN: 9781119256830
Author: Amos Gilat
Publisher: John Wiley & Sons Inc
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- Could someone please help with these.
-
At how many average weekly hours worked is the marginal effect of hours on predicted wage equal to zero?
-
-
What is the exact effect (in percent) of an increase of 10 years of education on predicted wage?
-
![Consider the following OLS regression output which is estimated with Wage2.dta (see
tutorial folder on Blackboard)
Source
df
MS
Number of obs
935
F (4, 930)
36.82
Model
22.6467366
4
5.66168416
Prob > F
0.0000
%3D
Residual
143.009547
930
.153773706
R-squared
Adj R-squared
0.1367
%3D
0.1330
%3D
Total
165.656283
934
.177362188
Root MSE
.39214
%3D
lwage
Сoef.
Std. Err.
P>|t|
[95% Conf. Interval]
educ
.0400982
.0068351
5.87
0.000
.0266843
.0535122
IQ
.005914
.0009967
5.93
0.000
.0039579
.0078701
hours
.0035899
.013037
0.28
0.783
-.0219954
.0291752
hours2
-.0000842
.0001298
-0.65
0.517
-.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$, educ is years of education, IQ
is points on an IQ intelligence test, hours is average weekly hours worked, and hours2 is
experience squared (hours * hours). Assume that MLR 1-6 hold.](https://content.bartleby.com/qna-images/question/b2d5f33c-ae78-41ed-81b8-d30f18a3c571/3efe89fe-8733-472e-90a2-b19b54aab86e/avlz8qk_processed.png)
Transcribed Image Text:Consider the following OLS regression output which is estimated with Wage2.dta (see
tutorial folder on Blackboard)
Source
df
MS
Number of obs
935
F (4, 930)
36.82
Model
22.6467366
4
5.66168416
Prob > F
0.0000
%3D
Residual
143.009547
930
.153773706
R-squared
Adj R-squared
0.1367
%3D
0.1330
%3D
Total
165.656283
934
.177362188
Root MSE
.39214
%3D
lwage
Сoef.
Std. Err.
P>|t|
[95% Conf. Interval]
educ
.0400982
.0068351
5.87
0.000
.0266843
.0535122
IQ
.005914
.0009967
5.93
0.000
.0039579
.0078701
hours
.0035899
.013037
0.28
0.783
-.0219954
.0291752
hours2
-.0000842
.0001298
-0.65
0.517
-.0003389
.0001706
cons
5.649044
.3240363
17.43
0.000
5.013117
6.284971
where Iwage is the natural logarithm of monthly wage in US$, 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.
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