Difference Between Multiple Logistic Regression And Multinomial Logistic Regression. It does not make any assumptions of linearity normality and homogeneity of variance for the independent variables. In some but not all situations you could use eitherSo lets look at how they differ when you might want to use one or the other and how to decide.
Logistic regression can be extended to handle responses that are polytomousie. Multinomial and ordinal varieties of logistic regression are incredibly useful and worth knowingThey can be tricky to decide between in practice however. The word polychotomous is sometimes used but this word does not exist When analyzing a polytomous response its important to note whether the response is ordinal.
Multiple regression means you are predicting several variables and each can typically be any real number.
Is there a difference between the two models Multinomial logit regression Multinomial logistic regression. Multinomial logistic regression analysis requires that the independent variables be metric or dichotomous. Multiple regression means you are predicting several variables and each can typically be any real number. Linear regression is the easiest and simplest machine learning algorithm to both understand and deploy.
