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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 (<math>n = 1</math>), and one of the two alternatives considered as "success" and coded as 1: the value is the [[Count data|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==
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{{reflist}}
{{refbegin}}
* {{cite
|title=Regression Models for Categorical Dependent Variables Using Stata, Second Edition
|chapter=4. Models for binary outcomes: 4.1 The statistical model
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|year=2006
|isbn=978-1-59718011-5
}}
* {{cite
|last=Agresti |first=Alan
|chapter=3.2 Generalized Linear Models for Binary Data
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|url=https://archive.org/details/introductiontoca00agre |url-access=limited |edition=2nd
|pages=[https://archive.org/details/introductiontoca00agre/page/n88 68]–73
}}
{{refend}}
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