Comparer des méthodes

	Régression logistique	Régression de Poisson et binomiale négative
Domaine	Statistiques de recherche	Économétrie
Famille	Process / pipeline	Regression model
Année d'origine	1958	1998
Auteur d'origine	David Roxbee Cox	Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)
Type	Method	Generalized linear model for count data
Source fondatrice	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press.
Alias	logit model, binomial logistic regression, LR	count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon
Apparentées	3	4
Résumé	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.	Poisson regression is a generalized linear model for count outcomes — events tallied as non-negative integers such as hospital admissions, accidents, or article counts. It models the log of the expected count as a linear function of the predictors, and is developed in the standard count-data treatment of Cameron and Trivedi (1998); when the counts are over-dispersed, the closely related negative binomial model (Hilbe, 2011) is preferred.