variable descriptions and Stata outputs from the simple and multiple linear regression are available in the exam handout file. Use this file to answer the following questions. 1. Firstly, report the results from the regression of wage on educ in the form of a fitted line, with the standard error of coefficients presented in parentheses underneath the corresponding coefficients. Round the numbers to two decimal places. 2. Is the coefficient of educ statistically significant? 3. Now consider the multiple linear regression that includes
variable descriptions and Stata outputs from the simple and multiple linear regression are available in the exam handout file. Use this file to answer the following questions. 1. Firstly, report the results from the regression of wage on educ in the form of a fitted line, with the standard error of coefficients presented in parentheses underneath the corresponding coefficients. Round the numbers to two decimal places. 2. Is the coefficient of educ statistically significant? 3. Now consider the multiple linear regression that includes
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
The variable descriptions and Stata outputs from the simple and multiple linear regression are available in the exam handout file. Use this file to answer the following questions.
1. Firstly, report the results from the regression of wage on educ in the form of a fitted line, with the standard error of coefficients presented in parentheses underneath the corresponding coefficients. Round the numbers to two decimal places.
2. Is the coefficient of educ statistically significant?
3. Now consider the multiple linear regression that includes KWW as one of the explanatory variables. Between this regression and the simple linear regression in part (a), which model is more likely to measure the ceteris paribus effect of education on wages? Explain and when possible use evidence to support your answer.
4. What happened to the standard error of educ after adding KWW to the model? Discuss.
- Do you agree or disagree with the following statement? “If the log of the dependent variable appears in the regression, changing the unit of measurement of any independent variable affects both the slope and intercept coefficients”. Discuss
![reg wage educ
Source
Model
Residual
Total
wage
educ
_cons
Source
Model
Residual
Total
educ
KWW
__cons
reg wage educ KWW
reg educ KWW
Source
Model
Residual
Figure (1)
Variable name Variable description
monthly earnings
years of education
Total
wage
educ
KWW
KWW
_cons
SS
16340644.5
136375524
152716168
60.21428
146.9524
SS
23473093.6
129243075
Coefficient Std. err.
152716168
General test of work-
related skills
SS
Figure (2)
678.944165
3827.87509
df
wage Coefficient Std. err.
4506.81925
16340644.5
1
933 146168.836
934 163507.675
5.694982
77.71496
Figure (3)
df
43.46013 6.018896
12.41303 1.730828
-71.09109 81.57352
934
2 11736546.8
932 138672.827
Figure (4)
MS
df
educ Coefficient Std. err.
t P>|t|
.1116141 .0086764
9.478871 .3171285
10.57
0.000
1.89 0.059
MS
163507.675
678.944165
1
933 4.10276001
MS
934 4.82528828
Number of obs
F(1, 933)
Prob> F
R-squared
Adj R-squared
Root MSE
t P>|t|
7.22 0.000
7.17 0.000
-0.87 0.384
t
Number of obs
F(2, 932)
Prob F
R-squared
Adj R-squared
Root MSE
P>|t|
12.86 0.000
29.89 0.000
49.03783
-5.56393
=
31.64797
9.016253
-231.1802
=
=
=
[95% conf. interval]
.0945866
8.856503
=
=
=
=
Number of obs
F(1, 933)
Prob > F
R-squared
Adj R-squared =
Root MSE
=
=
[95% conf. interval]
55.27229
15.8098
88.99797
=
935
111.79
0.0000
0.1070
0.1060
382.32
=
=
71.39074
299.4688
935
84.63
0.0000
0.1537
0.1519
372.39
935
165.48
0.0000
0.1506
0.1497
2.0255
[95% conf. interval]
.1286417
10.10124](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F244be6eb-07da-4aa9-92ad-dac5735e946a%2F1fa67b11-b3ac-4af5-a593-a76f674433f7%2Fwaz4ks_processed.png&w=3840&q=75)
Transcribed Image Text:reg wage educ
Source
Model
Residual
Total
wage
educ
_cons
Source
Model
Residual
Total
educ
KWW
__cons
reg wage educ KWW
reg educ KWW
Source
Model
Residual
Figure (1)
Variable name Variable description
monthly earnings
years of education
Total
wage
educ
KWW
KWW
_cons
SS
16340644.5
136375524
152716168
60.21428
146.9524
SS
23473093.6
129243075
Coefficient Std. err.
152716168
General test of work-
related skills
SS
Figure (2)
678.944165
3827.87509
df
wage Coefficient Std. err.
4506.81925
16340644.5
1
933 146168.836
934 163507.675
5.694982
77.71496
Figure (3)
df
43.46013 6.018896
12.41303 1.730828
-71.09109 81.57352
934
2 11736546.8
932 138672.827
Figure (4)
MS
df
educ Coefficient Std. err.
t P>|t|
.1116141 .0086764
9.478871 .3171285
10.57
0.000
1.89 0.059
MS
163507.675
678.944165
1
933 4.10276001
MS
934 4.82528828
Number of obs
F(1, 933)
Prob> F
R-squared
Adj R-squared
Root MSE
t P>|t|
7.22 0.000
7.17 0.000
-0.87 0.384
t
Number of obs
F(2, 932)
Prob F
R-squared
Adj R-squared
Root MSE
P>|t|
12.86 0.000
29.89 0.000
49.03783
-5.56393
=
31.64797
9.016253
-231.1802
=
=
=
[95% conf. interval]
.0945866
8.856503
=
=
=
=
Number of obs
F(1, 933)
Prob > F
R-squared
Adj R-squared =
Root MSE
=
=
[95% conf. interval]
55.27229
15.8098
88.99797
=
935
111.79
0.0000
0.1070
0.1060
382.32
=
=
71.39074
299.4688
935
84.63
0.0000
0.1537
0.1519
372.39
935
165.48
0.0000
0.1506
0.1497
2.0255
[95% conf. interval]
.1286417
10.10124
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