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In statistics, binomial regression is a regression analysis technique in which the response (often referred to as Y) has a binomial distribution: it is the number of successes in a series of independent Bernoulli trials, where each trial has probability of success . In binomial regression, the probability of a success is related to explanatory variables: the corresponding concept in ordinary regression is to relate the mean value of the unobserved response to explanatory variables.

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rdfs:label	Binomial regression (en)
rdfs:comment	In statistics, binomial regression is a regression analysis technique in which the response (often referred to as Y) has a binomial distribution: it is the number of successes in a series of independent Bernoulli trials, where each trial has probability of success . In binomial regression, the probability of a success is related to explanatory variables: the corresponding concept in ordinary regression is to relate the mean value of the unobserved response to explanatory variables. (en)
dcterms:subject	Factorial and binomial topics Generalized linear models
Wikipage page ID	4916857 (xsd:integer)
Wikipage revision ID	1119123308 (xsd:integer)
Link from a Wikipage to another Wikipage	Probabilistic classification Probability distribution Probit model Dummy variable (statistics) Predictive modelling Quantile function Factorial and binomial topics Generalized extreme value distribution Generalized linear model Poisson regression Generative model Conditional expectation Conditional variance Bernoulli trial Likelihood Logistic function Machine learning Statistics Identifiability Student's t-distribution Linear probability model Logistic distribution Logistic regression Cumulative distribution function Normal distribution Discrete choice Dependent variable Random variable Regression analysis Response variable Generalized linear models Count data Latent variable Latent variable model Binary classification Binary regression Binary variable Binomial distribution Support (mathematics) Grouped data Explanatory variable Independent variable Indicator function Categorical variable Maximum likelihood Scale parameter Ungrouped data Overdispersion Ordinal variable Regression coefficient Error variable Generalised linear model Binary choice model Logit function Logit model Standard normal distribution
Link from a Wikipage to an external page	https://data.princeton.edu/wws509/notes/ https://books.google.com/books%3Fid=RBKEwpsxmN4C
sameAs	Binomial regression Binomial regression Binomial regression Binomial regression
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has abstract	In statistics, binomial regression is a regression analysis technique in which the response (often referred to as Y) has a binomial distribution: it is the number of successes in a series of independent Bernoulli trials, where each trial has probability of success . In binomial regression, the probability of a success is related to explanatory variables: the corresponding concept in ordinary regression is to relate the mean value of the unobserved response to explanatory variables. Binomial regression is closely related to binary regression: a binary regression can be considered a binomial regression with , or a regression on ungrouped binary data, while a binomial regression can be considered a regression on grouped binary data (see ). Binomial regression models are essentially the same as binary choice models, one type of discrete choice model: the primary difference is in the theoretical motivation (see ). In machine learning, binomial regression is considered a special case of probabilistic classification, and thus a generalization of binary classification. (en)
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is Link from a Wikipage to another Wikipage of	List of analyses of categorical data Count data Binary data Binary regression Statistical data type Wald test

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