Statistical technique to determine the statistical association or relation between/among two or more variables, and where one of the variables, the dependent variable, is dichotomous (has only two levels of magnitude) (e.g., abstinent vs. smoking).

A variant of multiple regression, used when the dependent variable is a dichotomy, such as success/failure

a form of regression equation where the output is transformed into a probability

a particular case of what is known as a generalised linear model

a variation on OLS regression that predicts a binary outcome such as agreement (yes vs. no) or purchase (buy vs. not buy). Discriminant analysis can also be used to predict dichotomous group membership, but "logit" is generally preferred due to its broader applicability. Logit output indicates whether each predictor variable increases or decreases the probability of the outcome.

a statistical technique that determines the probability of a dependent variable (outcome) occurring when the independent (explanatory) variables are present or absent when the outcome is a dichotomous (binary) variable. It determines whether a model that includes the variable(s) explains more about the outcome variable than a model that does not include the variable(s).

The regression technique used when the outcome is a binary, or dichotomous variable.

A linear regression that predicts the proportions of a categorical target variable, such as type of customer, in a population.

A generalization of linear regression. It is used for predicting a binary variable (with values such as yes/no or 0/1). An example of its use is modeling the odds that a borrower will default on a loan based on the borrower's income, debt and age.

Logistic regression is a statistical regression model for Bernoulli-distributed dependent variables. It is a generalized linear model that utilizes the logit as its link function. Logistic regression is extensively used in the medical and social sciences.