Thank you so, so much. I do have class race (ref='White')/param=ref;  because my professor quickly typed up the procedure and left me to my own devices to report the result (before I've taken a formal class on regression analysis). I was just hoping for a quick answer to interpret the results so I can finalize for my project, but I guess I need to take the time to dig deeper into the procedure. Thanks for your help, remote professor 😉 ... View more

Ok, I'm looking at the link and found the appropriate sub section, but it will take time to digest, so while I still have you (and I promise to throw you an accepted solution), it seems that it would be best to co-vary race and income, and keep race and black_perc as binary and continuous predictor, respectively. Now when I run the logistic regression by neighborhoods, it seems to pick an income specific to that neighborhood through which to compare the odds ratios of binary variables (e.g. black1 vs white1 at Income1, black2 vs white2 at Income2, black3 vs white3 at Income3, etc). My last question is, would you happen to know how that neighborhood specific income is determined? ... View more

From the link: We can interpret the odds ratio as follows: for a one unit change in the predictor variable, the odds ratio for a positive outcome is expected to change by the respective coefficient, given the other variables in the model are held constant So that means for a continuous predictor variable like income, increasing income by one unit corresponds to a change in the Odds Ratio equal to the coefficient (parameter) ? Is that an incorrect interpretation (sorry for turning this into a stats question) ... View more