In the past two lessons, we've explored ways to fit and evaluate logistic regression models for a binary response. These results generalize what we saw earlier for two and three-way tables to explain associations between two variables while controlling for additional variables and allowing for both categorical and continuous types. One shortcoming, however, would be in accommodating response variables with more than two levels (something more general than "success" or "failure"). For this, we can utilize a multinomial random component; the link and systematic components need not change. The challenge, as we'll see, is in defining and interpreting odds when more than one complementary category is involved.