Source I Model I Residual I Total I lwage I conomics SS 160.407298 284.572674 444.979972 Coef. df 8 1251 1259 MS 20.0509122 227476159 353439215 Std. Err. t P>ltl belavg I -.156349 abvavg I -.0061272 .0424071 .0306777 -3.69 0.000 -0.20 0.842 .0053614 12.27 0.000 8.86 0.000 -6.12 0.000 educ I .0657757 exper I .0399953 .0045128 expersq 1 -.0006135 .0001002 black I -.0494744 .0527382 -0.94 0.348 female I -.4451518 .0304546 -14.62 0.000 married I .0192245 .031952 _cons I 5608266 .0830026 0.60 0.548 6.76 0.000 Number of obs= F( 8. 1251) = Prob > F R-squared Adj R-squared = Root MSE = 1260 88.15 0.0000 0.3605 0.3564 .47694 [95% Conf. Interval] -.2395458 -.0663125 .0552574 .0311419 -.0008099 -.1529395 -.5048996 -.0434609 .397987 -.0731522 .0540581 .0762939 .0488487 -.000417 NUK .0539907 -.385404 .08191 .7236662 a. Interpret the coefficient of educ and female. b. What is the effect of one year increase in working experience in wage? c. Can you infer that the physical attractiveness is related one's wage? d. Please verify the Adjusted R-squared using the ANOVA table from the above. e. Test the validity of the model.(Please state the null and the alternative hypothesis) f. If "avg" is added into the regression, which assumptions of Least Square regression will be violated and why? g. If you are wondering whether or not the return to education is different between male and female workers, how do you modify the estimated model above to clarify your doubt? Please explain.
Source I Model I Residual I Total I lwage I conomics SS 160.407298 284.572674 444.979972 Coef. df 8 1251 1259 MS 20.0509122 227476159 353439215 Std. Err. t P>ltl belavg I -.156349 abvavg I -.0061272 .0424071 .0306777 -3.69 0.000 -0.20 0.842 .0053614 12.27 0.000 8.86 0.000 -6.12 0.000 educ I .0657757 exper I .0399953 .0045128 expersq 1 -.0006135 .0001002 black I -.0494744 .0527382 -0.94 0.348 female I -.4451518 .0304546 -14.62 0.000 married I .0192245 .031952 _cons I 5608266 .0830026 0.60 0.548 6.76 0.000 Number of obs= F( 8. 1251) = Prob > F R-squared Adj R-squared = Root MSE = 1260 88.15 0.0000 0.3605 0.3564 .47694 [95% Conf. Interval] -.2395458 -.0663125 .0552574 .0311419 -.0008099 -.1529395 -.5048996 -.0434609 .397987 -.0731522 .0540581 .0762939 .0488487 -.000417 NUK .0539907 -.385404 .08191 .7236662 a. Interpret the coefficient of educ and female. b. What is the effect of one year increase in working experience in wage? c. Can you infer that the physical attractiveness is related one's wage? d. Please verify the Adjusted R-squared using the ANOVA table from the above. e. Test the validity of the model.(Please state the null and the alternative hypothesis) f. If "avg" is added into the regression, which assumptions of Least Square regression will be violated and why? g. If you are wondering whether or not the return to education is different between male and female workers, how do you modify the estimated model above to clarify your doubt? Please explain.
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
![Department of Applied Economics
Source I
Model I
Residual I
Total I
lwage I
SS
160.407298
284.572674
444.979972
df
8
1251
1259
20.0509122
.227476159
MS
.353439215
Coef. Std. Err.
-.156349
belavg I
abvavg I
.0424071
.0306777
-.0061272
.0657757
.0053614
educ I
exper 1
.0399953
.0045128
expersq 1 -.0006135
.0001002
black I .0494744
.0527382
female I -.4451518 .0304546
married I 0192245 .031952
5608266 .0830026
cons I
t P>ltl
-3.69
0.000
-0.20
0.842
12.27 0.000
8.86 0.000
-6.12 0.000
-0.94 0.348
- 14.62 0.000
0.60
0.548
6.76 0.000
Number of obs =
F( 8.
1251) =
Prob > F
R-squared
=
=
Adj R-squared =
Root MSE
1260
88.15
0.0000
0.3605
0.3564
= .47694
[95% Conf. Interval]
-.2395458
-.0663125
.0552574
.0311419
-.0008099
-.1529395
-.5048996
.0434609
.397987
NUK
-.0731522
.0540581
.0762939
.0488487
-.000417
.0539907
-.385404
.08191
.7236662
a. Interpret the coefficient of educ and female.
b. What is the effect of one year increase in working experience in wage?
c. Can
you infer that the physical attractiveness is related one's wage?
d. Please verify the Adjusted R-squared using the ANOVA table from the above.
e. Test the validity of the model. (Please state the null and the alternative hypothesis)
f. If "avg" is added into the regression, which assumptions of Least Square regression will be
violated and why?
g. If you are wondering whether or not the return to education is different between male and
female workers, how do you modify the estimated model above to clarify your doubt? Please
explain.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F5ca49771-ba78-45d8-b8c2-2c40b09abf36%2F66591bee-a473-49d8-b005-45acbb87263f%2Fgo00hsi_processed.jpeg&w=3840&q=75)
Transcribed Image Text:Department of Applied Economics
Source I
Model I
Residual I
Total I
lwage I
SS
160.407298
284.572674
444.979972
df
8
1251
1259
20.0509122
.227476159
MS
.353439215
Coef. Std. Err.
-.156349
belavg I
abvavg I
.0424071
.0306777
-.0061272
.0657757
.0053614
educ I
exper 1
.0399953
.0045128
expersq 1 -.0006135
.0001002
black I .0494744
.0527382
female I -.4451518 .0304546
married I 0192245 .031952
5608266 .0830026
cons I
t P>ltl
-3.69
0.000
-0.20
0.842
12.27 0.000
8.86 0.000
-6.12 0.000
-0.94 0.348
- 14.62 0.000
0.60
0.548
6.76 0.000
Number of obs =
F( 8.
1251) =
Prob > F
R-squared
=
=
Adj R-squared =
Root MSE
1260
88.15
0.0000
0.3605
0.3564
= .47694
[95% Conf. Interval]
-.2395458
-.0663125
.0552574
.0311419
-.0008099
-.1529395
-.5048996
.0434609
.397987
NUK
-.0731522
.0540581
.0762939
.0488487
-.000417
.0539907
-.385404
.08191
.7236662
a. Interpret the coefficient of educ and female.
b. What is the effect of one year increase in working experience in wage?
c. Can
you infer that the physical attractiveness is related one's wage?
d. Please verify the Adjusted R-squared using the ANOVA table from the above.
e. Test the validity of the model. (Please state the null and the alternative hypothesis)
f. If "avg" is added into the regression, which assumptions of Least Square regression will be
violated and why?
g. If you are wondering whether or not the return to education is different between male and
female workers, how do you modify the estimated model above to clarify your doubt? Please
explain.
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