There are three primary types of logistic regression: multinomial, ordinal, and binary. For scenarios where there are only two possible outcomes, like loan approval, cancer risk assessment, or sports match predictions, Binary Logistic Regression is perfect. In situations when the dependent variable has ordered categories with unequal intervals, such as student selections or pet food types, ordinal logistic regression is utilized. However, when the dependent variable is nominal, meaning it has more than two levels and no particular order—such as test scores, survey replies, or shirt sizes—multinomial logistic regression is appropriate.

Key practices for the effective application of logistic regression include understanding the technical requirements of the model, carefully choosing dependent variables to maintain consistency in the model, accurate estimation, interpreting results meaningfully, and comprehensive validation to guarantee the accuracy and reliability of the model.