Consider the following regression estimates (FN6) Source SS df MS Number of obs = 526 64.11 F(3, 522) = Model 1927.87673 3 642.625576 Prob > F = 0.0000 Residual 5232.53756 522 10.0240183 R-squared 0.2692 Adj R-squared = 0.2650 Total 7160.41429 525 13.6388844 Root MSE = 3.1661 wage Coef. Std. Err. t P>|t| [95% Conf. Interval] educ .5953429 .0530251 11.23 .4911741 .6995118 0.000 7.27 0.000 exper .268287 .0368969 .1958023 .3407717 exper2 -.0046123 .000822 -5.61 0.000 -.006227 -.0029975 _cons -3.96489 .7521526 -5.27 0.000 -5.442508 -2.487272 where wage is hourly wage in US$, educ is years of education, exper is years of work experience and exper2 is experience squared (exper* exper). At how many years is the marginal effect of increasing experience on predicted wage equal to zero? Only state the number in your answer (e.g. don't write US$ or $ or years).
Consider the following regression estimates (FN6) Source SS df MS Number of obs = 526 64.11 F(3, 522) = Model 1927.87673 3 642.625576 Prob > F = 0.0000 Residual 5232.53756 522 10.0240183 R-squared 0.2692 Adj R-squared = 0.2650 Total 7160.41429 525 13.6388844 Root MSE = 3.1661 wage Coef. Std. Err. t P>|t| [95% Conf. Interval] educ .5953429 .0530251 11.23 .4911741 .6995118 0.000 7.27 0.000 exper .268287 .0368969 .1958023 .3407717 exper2 -.0046123 .000822 -5.61 0.000 -.006227 -.0029975 _cons -3.96489 .7521526 -5.27 0.000 -5.442508 -2.487272 where wage is hourly wage in US$, educ is years of education, exper is years of work experience and exper2 is experience squared (exper* exper). At how many years is the marginal effect of increasing experience on predicted wage equal to zero? Only state the number in your answer (e.g. don't write US$ or $ or years).
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
![Consider the following regression estimates (FN6)
Source
SS
df
MS
526
64.11
Model
1927.87673
3
642.625576
0.0000
Residual
5232.53756
522 10.0240183
0.2692
0.2650
Total
7160.41429
525
13.6388844
3.1661
wage
Coef. Std. Err.
t P>|t|
[95% Conf. Interval]
educ
.5953429
.0530251
11.23 0.000
.4911741
.6995118
exper
.268287
.0368969
0.000
.1958023
.3407717
7.27
-5.61 0.000
exper2
-.0046123
.000822
-.006227
-.0029975
_cons
-3.96489
.7521526
-5.27 0.000
-5.442508
-2.487272
where wage is hourly wage in US$, educ is years of education, exper is years of work experience and exper2 is experience
squared (exper * exper).
At how many years is the marginal effect of increasing experience on predicted wage equal to zero?
Only state the number in your answer (e.g. don't write US$ or $ or years).
Number of obs
=
F(3, 522)
=
Prob > F
R-squared
Adj R-squared =
Root MSE
11](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fea35d65d-c5a4-4815-8933-ba8e9e4a80fc%2Ff99b5e34-845f-401c-8589-e04eedba60b2%2Fnwti0i_processed.png&w=3840&q=75)
Transcribed Image Text:Consider the following regression estimates (FN6)
Source
SS
df
MS
526
64.11
Model
1927.87673
3
642.625576
0.0000
Residual
5232.53756
522 10.0240183
0.2692
0.2650
Total
7160.41429
525
13.6388844
3.1661
wage
Coef. Std. Err.
t P>|t|
[95% Conf. Interval]
educ
.5953429
.0530251
11.23 0.000
.4911741
.6995118
exper
.268287
.0368969
0.000
.1958023
.3407717
7.27
-5.61 0.000
exper2
-.0046123
.000822
-.006227
-.0029975
_cons
-3.96489
.7521526
-5.27 0.000
-5.442508
-2.487272
where wage is hourly wage in US$, educ is years of education, exper is years of work experience and exper2 is experience
squared (exper * exper).
At how many years is the marginal effect of increasing experience on predicted wage equal to zero?
Only state the number in your answer (e.g. don't write US$ or $ or years).
Number of obs
=
F(3, 522)
=
Prob > F
R-squared
Adj R-squared =
Root MSE
11
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