Consider the following OLS regression output which is estimated with Wage2.dta (see tutorial folder on Blackboard) df Number of obs F(4, 930) 935 36.82 0.0000 0.1367 0.1330 .39214 Source MS Model Residual 22.6467366 143.009547 4 5.66168416 930 .153773706 Prob > F R-squared Adj R-squared Total 165.656283 934 .177362188 Root MSE 1wage Coef. Std. Err. (951 Conf. Interval] .0400982 .005914 .0035899 0000842 5.649044 .0068351 .0009967 .013037 .0001298 3240363 5.87 5.93 0.28 -0.65 17,43 0.000 0.000 0.783 0.517 0.000 .0266843 .0039579 -.0219954 -.0003389 5.013117 .0535122 .0078701 .0291752 .0001706 6.284971 educ hours hours2 cons
Consider the following OLS regression output which is estimated with Wage2.dta (see tutorial folder on Blackboard) df Number of obs F(4, 930) 935 36.82 0.0000 0.1367 0.1330 .39214 Source MS Model Residual 22.6467366 143.009547 4 5.66168416 930 .153773706 Prob > F R-squared Adj R-squared Total 165.656283 934 .177362188 Root MSE 1wage Coef. Std. Err. (951 Conf. Interval] .0400982 .005914 .0035899 0000842 5.649044 .0068351 .0009967 .013037 .0001298 3240363 5.87 5.93 0.28 -0.65 17,43 0.000 0.000 0.783 0.517 0.000 .0266843 .0039579 -.0219954 -.0003389 5.013117 .0535122 .0078701 .0291752 .0001706 6.284971 educ hours hours2 cons
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
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Hi Just question 8 and 9.
Thank you.
![Consider the following OLS regression output which is estimated with Wage2.dta (see
tutorial folder on Blackboard)
935
36.82
df
Number of obs
F(4, 930)
Source
MS
4 5.66168416
0.0000
0.1367
0.1330
Model
22.6467366
Prob > F
Residual
143.009547
930
.153773706
R-squared
Adj R-squared
Total
165.656283
934
.177362188
Root MSE
.39214
1wage
Coef.
Std. Err.
P>|t|
[95% Conf. Interval]
t
educ
.0400982
.005914
.0068351
5.87
5.93
0.000
.0266843
.0535122
1Q
.0009967
0.000
.0039579
.0078701
0.783
0.517
hours
.0035899
.013037
0.28
-.0219954
.0291752
hours2
-.0000842
.0001298
-0.65
17.43
-.0003389
.0001706
cons
5.649044
.3240363
0.000
5.013117
6.284971
where Iwage is the natural logarithm of monthly wage in USS, 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.
8) Write down the econometric model that this OLS regression estimates.
9) Interpret the educ coefficient](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F909b168f-c772-43f0-b38c-6f8b46c0e487%2Fac0925ed-16d6-456e-8cb4-5836e6f20d05%2Fwgzdiw_processed.png&w=3840&q=75)
Transcribed Image Text:Consider the following OLS regression output which is estimated with Wage2.dta (see
tutorial folder on Blackboard)
935
36.82
df
Number of obs
F(4, 930)
Source
MS
4 5.66168416
0.0000
0.1367
0.1330
Model
22.6467366
Prob > F
Residual
143.009547
930
.153773706
R-squared
Adj R-squared
Total
165.656283
934
.177362188
Root MSE
.39214
1wage
Coef.
Std. Err.
P>|t|
[95% Conf. Interval]
t
educ
.0400982
.005914
.0068351
5.87
5.93
0.000
.0266843
.0535122
1Q
.0009967
0.000
.0039579
.0078701
0.783
0.517
hours
.0035899
.013037
0.28
-.0219954
.0291752
hours2
-.0000842
.0001298
-0.65
17.43
-.0003389
.0001706
cons
5.649044
.3240363
0.000
5.013117
6.284971
where Iwage is the natural logarithm of monthly wage in USS, 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.
8) Write down the econometric model that this OLS regression estimates.
9) Interpret the educ coefficient
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