Logistic regression is a powerful method but relies on key assumptions:

• The errors must be independent of each other.
• For continuous variables, there must be linearity in the logit.
• There should be no multicollinearity between predictors.
• Strong outliers should not overly influence the results (Stoltzfus, 2011).