I am confused trying to interpret how two observations are otherwise identical but differ by a dummy variable. For example if we have the following model with a factor variable race being White race the reference category:
Call: lm(formula = Score ~ ., data = pisaTrain) Residuals: Min 1Q Median 3Q Max -247.44 -48.86 1.86 49.77 217.18 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 143.766333 33.841226 4.248 2.24e-05 *** grade 29.542707 2.937399 10.057 < 2e-16 *** male -14.521653 3.155926 -4.601 4.42e-06 *** raceEuropean -67.277327 16.786935 -4.008 6.32e-05 *** raceAsian -4.110325 9.220071 -0.446 0.65578 raceBlack -67.012347 5.460883 -12.271 < 2e-16 *** raceHispanic 38.975486 5.177743 -7.528 7.29e-14 *** raceOther -16.922522 8.496268 -1.992 0.04651 *
Given two people that are otherwise identical, what would be the absolute difference in predicted score given that one person is European and the other is Hispanic? Also in the case when the race is white, the difference will differ by the coefficient on other variables?
Somebody might help me out, thank you in advance!