Binary regression

<p>In <a href="/facts/Statistics/DNPsKGYU">statistics</a>, specifically <a href="/facts/Regression_analysis/n6z5Tf7K">regression analysis</a>, a binary regression estimates a relationship between one or more <a href="/facts/Explanatory_variable/dINfzIF2">explanatory variables</a> and a single output <a href="/facts/Binary_variable/PSFa5S2Q">binary variable</a>. Generally the probability of the two alternatives is modeled, instead of simply outputting a single value, as in <a href="/facts/Linear_regression/5n998IhK">linear regression</a>.
</p><p>Binary regression is usually analyzed as a special case of <a href="/facts/Binomial_regression/PKoTBPja">binomial regression</a>, with a single outcome (
  
    
      
        n
        =
        1
      
    
    {\displaystyle n=1}
  
), and one of the two alternatives considered as "success" and coded as 1: the value is the <a href="/facts/Count_data/vPP2VEyk">count</a> of successes in 1 trial, either 0 or 1. The most common binary regression models are the <a href="/facts/Logit_model/mGj6mc8y">logit model</a> (<a href="/facts/Logistic_regression/mGj6mc8y">logistic regression</a>) and the <a href="/facts/Probit_model/k7hCvtYj">probit model</a> (<a href="/facts/Probit_regression/k7hCvtYj">probit regression</a>).
</p>

Binary regression open-in-new

Binary regression