Part 3: Interpretation of OLS regression Consider the following OLS regression output which is estimated with Wage2.dta (see tutorial folder on Blackboard) Source ss df MS Number of obs 935 F (4, 930) 36.82 Model 22.6467366 4 5.66168416 Prob > F 0.0000 Residual 143.009547 930 .153773706 R-squared Adj R-squared 0.1367 0.1330 Total 165.656283 934 .177362188 Root MSE .39214 lwage Coef. Std. Err. t P>|t| [95% Conf. Interval] educ .0400982 .0068351 5.87 0.000 .0266843 .0535122 IO .005914 0009967 5.93 0.000 .0039579 .0078701 I|||| I
Part 3: Interpretation of OLS regression Consider the following OLS regression output which is estimated with Wage2.dta (see tutorial folder on Blackboard) Source ss df MS Number of obs 935 F (4, 930) 36.82 Model 22.6467366 4 5.66168416 Prob > F 0.0000 Residual 143.009547 930 .153773706 R-squared Adj R-squared 0.1367 0.1330 Total 165.656283 934 .177362188 Root MSE .39214 lwage Coef. Std. Err. t P>|t| [95% Conf. Interval] educ .0400982 .0068351 5.87 0.000 .0266843 .0535122 IO .005914 0009967 5.93 0.000 .0039579 .0078701 I|||| I
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
Related questions
Question
![Part 3: Interpretation of OLS regression
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
Residual
143.009547
930
.153773706
R-squared
Adj R-squared
0.1367
0.1330
.39214
Total
165.656283
934
.177362188
Root MSE
%3D
lwage
Coef.
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
0.28
0.783
0.517
hours
.0035899
.013037
-.0219954
.0291752
hours2
-.0000842
.0001298
-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$, educ is years of education, IQ
is points on an IQ intelligence test, hours is average weekly hours worked, and hours2 is
experience squared (hours * hours). Assume that MLR 1-6 hold.
2
8. Write down the econometric model that this OLS regression estimates.
9. Interpret the educ coefficient.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fea35d65d-c5a4-4815-8933-ba8e9e4a80fc%2F8b29a712-04e1-4d59-b0a1-0c3efe17ea99%2Fqzf2y4w_processed.png&w=3840&q=75)
Transcribed Image Text:Part 3: Interpretation of OLS regression
Consider the following OLS regression output which is estimated with Wage2.dta (see
tutorial folder on Blackboard)
Source
df
MS
Number of obs
935
F (4, 930)
36.82
Model
22.6467366
4
5.66168416
Prob > F
0.0000
Residual
143.009547
930
.153773706
R-squared
Adj R-squared
0.1367
0.1330
.39214
Total
165.656283
934
.177362188
Root MSE
%3D
lwage
Coef.
Std. Err.
P>|t|
[95% Conf. Interval]
educ
.0400982
.0068351
5.87
0.000
.0266843
.0535122
IQ
.005914
.0009967
5.93
0.000
.0039579
.0078701
0.28
0.783
0.517
hours
.0035899
.013037
-.0219954
.0291752
hours2
-.0000842
.0001298
-0.65
-.0003389
.0001706
cons
5.649044
.3240363
17.43
0.000
5.013117
6.284971
where Iwage is the natural logarithm of monthly wage in US$, 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.
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