binomial logistic regression (often referred to simply as logistic regression), predicts the probability that an observation falls into one of two categories of a dichotomous dependent variable, based on one or more independent variables that can be either continuous or categorical.
If, on the other hand, your dependent variable is a count, see our Poisson regression guide. Alternatively, if you have more than two categories of the dependent variable, see our multinomial logistic regression guide.
Poisson regression is used to predict a dependent variable that consists of "count data" given one or more independent variables. The variable we want to predict is called the dependent variable (or sometimes the response, outcome, target or criterion variable). The variables we are using to predict the value of the dependent variable are called the independent variables (or sometimes the predictor, explanatory or regressor variables). Some examples where Poisson regression could be used are described below:
Multinomial logistic regression (often just called 'multinomial regression') is used to predict a nominal dependent variable given one or more independent variables. It is sometimes considered an extension of binomial logistic regression to allow for a dependent variable with more than two categories. As with other types of regression, multinomial logistic regression can have nominal and/or continuous independent variables and can have interactions between independent variables to predict the dependent variable.