Consider the following Stata regression output (IDMC5) Source SS df MS Number of obs 935 %3D F(1, 933) 111.79 %3D Model 16340644.5 1 16340644.5 Prob > F %3D 0.0000 Residual R-squared Adj R-squared 136375524 933 146168.836 0.1070 %3D %3D 0.1060 Total 152716168 934 163507.675 Root MSE 382.32 %3D wage Coefficient Std. err. t P>|t| [95% conf. interval] educ 60.21428 5.694982 10.57 0.000 49.03783 71.39074 cons 146.9524 77.71496 1.89 0.059 -5.56393 299.4688 where wage is monthly wage in US$ and educ is years of education. What is the predicted effect of a 5 year increase of education on wage?

MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
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Consider the following Stata regression output (IDMC5)
Source
SS
df
MS
Number of obs
935
%3D
F(1, 933)
111.79
Model
16340644.5
1
16340644.5
Prob > F
0.0000
Residual
136375524
933
146168.836
R-squared
0.1070
Adj R-squared
0.1060
%3D
Total
152716168
934 163507.675
Root MSE
382.32
wage
Coefficient Std. err.
P>|t|
[95% conf. interval]
educ
60.21428
5.694982
10.57
0.000
49.03783
71.39074
cons
146.9524
77.71496
1.89
0.059
-5.56393
299.4688
where wage is monthly wage in US$ and educ is years of education.
What is the predicted effect of a 5 year increase of education on wage?
Il ||
II
Transcribed Image Text:Consider the following Stata regression output (IDMC5) Source SS df MS Number of obs 935 %3D F(1, 933) 111.79 Model 16340644.5 1 16340644.5 Prob > F 0.0000 Residual 136375524 933 146168.836 R-squared 0.1070 Adj R-squared 0.1060 %3D Total 152716168 934 163507.675 Root MSE 382.32 wage Coefficient Std. err. P>|t| [95% conf. interval] educ 60.21428 5.694982 10.57 0.000 49.03783 71.39074 cons 146.9524 77.71496 1.89 0.059 -5.56393 299.4688 where wage is monthly wage in US$ and educ is years of education. What is the predicted effect of a 5 year increase of education on wage? Il || II
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