
The following results were obtained by regressing mean hourly wage in dollars (Y) on years of schooling (X).
Dependent Variable: MEAN_WAGE |
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Method: Least Squares |
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Date: 02/15/15 Time: 11:11 |
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Sample: 1 13 |
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Included observations: 13 |
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Variable |
Coefficient |
Std. Error |
t-Statistic |
Prob. |
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C |
-0.014453 |
0.874624 |
-0.016525 |
0.9871 |
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YEARS_SCHOOLING |
0.724097 |
0.069581 |
10.40648 |
0.0000 |
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R-squared |
0.907791 |
Mean dependent var |
8.674708 |
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Adjusted R-squared |
0.899409 |
S.D. dependent var |
2.959706 |
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S.E. of regression |
0.938704 |
Akaike info criterion |
2.852004 |
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Sum squared resid |
9.692810 |
Schwarz criterion |
2.938920 |
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Log likelihood |
-16.53803 |
Hannan-Quinn criter. |
2.834139 |
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F-statistic |
108.2948 |
Durbin-Watson stat |
1.737984 |
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Prob(F-statistic) |
0.000000 |
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- Interpret the regression coefficients. Do they make economic sense?
- Would you conclude that there is a statistically significant relationship at the 5% level between mean hourly wage (Y) and years of schooling (X)? (Hint: Test H0: β2=0)
- Comment about the goodness of fit of the above regression model?
- Test the null hypothesis, H0:β2=1, (vs. H1: β2≠1) at the 1% level of significance.


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