Consider the following regression estimates: . reg wage educ female nonwhite Source SS df MS 526 60.79 Model 1853.93665 617.978884 0.0000 3 522 10.165666 Residual 5306.47764 0.2589 0.2547 Total 7160.41429 525 13.6388844 3.1884 wage Coef. Std. Err. t P>|t| [95% Conf. Interval] 9.98 0.000 .4058751 .6047738 -8.14 0.000 -2.823452 -1.725915 educ .5053245 .0506227 female -2.274684 .2793401 nonwhite -.1192245 .4597555 _cons .6498561 .6811618 -0.26 0.795 -1.022423 .783974 . 0.95 0.341 -.6882992 1.988011 where wage is monthly wage in US$, educ is years of education, female is a dummy variable which is equal to 1 if the person is female and 0 otherwise, and nonwhite is a dummy variable which is equal to 1 if the person is not White and 0 otherwise. 1. The standard error of the constant is 2. The t-statistic for the female coefficient is 3. The sum of squared residuals (SSR) is 4. The explained sum of squares (SSE) is 5. The predicted wage in US$ for a White man with 5 years of education is 6. The predicted wage in US$ for a Hispanic woman with 6 years of education is Number of obs F(3, 522) Prob > F R-squared Adj R-squared Root MSE