Week 3 Answers
1. Run a regression of wage on education (educ).
reg wage educ
Source |
SS
df
MS
Number of obs =
935
-------------+------------------------------
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
Total |
152716168
934
163507.675
Root MSE
=
382.32
------------------------------------------------------------------------------
wage |
Coef.
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
------------------------------------------------------------------------------
2. Interpret the slope and the intercept of the estimated regression line.
Slope: A 1 unit increase in education increases the predicted wage by 60.21.
Intercept: If education is zero, then the average wage is 146.95.
3. Is the slope coefficient statistically significant?
Statistically significant implies that you need to test the following:
H
0
:
β
1
= 0
H
A
:
β
1
6
= 0
Stata gives the relevant
t
stat
which is 10.57 in this case. At the 5% level of significance, the
critical value is 1.96. Thus,
|
t
stat
|
>
1
.
96, which implies that the null can be rejected.
Alternatively, the p-value is 0.000 (also given in the Stata table) which is less than 0.05,
therefore, the null can be rejected.

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- Winter '09
- Ramirez
- Regression Analysis, 5%, 2 years, $80
-
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