Binary regression: Difference between revisions

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In [[statistics]], specifically [[regression analysis]], a '''binary regression''' estimates a relationship between one or more [[explanatory variable]]s and a single output [[binary variable]]. Generally the probability of the two alternatives is modeled, instead of simply outputting a single value, as in [[linear regression]].

Binary regression is usually analyzed as a special case of [[binomial regression]], with a single outcome ({{tmath|<math>n = 1}}</math>), and one of the two alternatives considered as "success" and coded as 1: the value is the count of successes in 1 trial, either 0 or 1. The most common binary regression models are the [[logit model]] ([[logistic regression]]) and the [[probit model]] ([[probit regression]]).

==Applications==