Labor economists are often interested in the returns to education as observed through income. Using the General Social Survey which goes from 1972 to 1996, you attempt to build a model that will also incorporate family and personal background. You randomly sample 1000 individuals from the dataset and keep information on their income (in $ per year), years of education, age (in years), gender (if the individual is male, MALE=1), race, and also father and mother's years of education. The variable race in the original dataset, only has information on White, Black, and Other (which are included in your model as typical qualitative variables). You are given the following output with the individuals income as the dependent variable Regression Statistics Multiple R 0.527 R Square 0.278 Adjusted R Square 0.272 Standard Error 13262.945 Observations 1000 ANOVA df SS MS Regression 7 6.71E+10 9.58E+09 54.456 Residual 992 1.74E+11 1.76E+08 Total 999 2.42E+11 Coefficients Standard Error t Stat P-value Intercept -25734.772 2792.407 -9.216 1.80E-19 Years Education 1946.536 173.017 11.251 1.01E-27 Age 154.982 34.352 4.512 7.20E-06 Father Education -112.940 144.880 -0.780 0.4358 Mother Education 438.284 173.868 2.521 0.0119 Male 10532.687 852.148 12.360 9.62E-33 Other Race 3115.934 2425.640 1.285 0.1992 Black 680.174 1409.041 0.483 0.6294 How do you interpret the estimated coefficient for the variable Male? Select one: O a. For each additional unit, the estimated income will increase by $10,532 O b. Compared to women, men have an estimated income of $10,532 more O c. Compared to white individuals, men have an estimated income of $10,532 more O d. The average male in this sample has an estimated income of $10,532 O e. You should not interpret the Male coefficient because it is statistically insignificant