While we stated that logistic regression violates some of the assumptions of linear regression, it still requires the following:

(a) independence of observations,

(b) little to no multicollinearity among predictors, and

(c) a linear relationship between the predictors and response.

However, a key subtlety is that the linearity is based on the **logit** of the response, not the raw 0/1 values. Thus, this assumption can only really be validated after fitting the model by examining the relationship between each predictor and the fitted logit values.