Comparer des méthodes

	Régression logistique	Régression binomiale négative
Domaine	Statistiques de recherche	Économétrie
Famille	Process / pipeline	Regression model
Année d'origine	1958	2011
Auteur d'origine	David Roxbee Cox	Hilbe (textbook treatment); generalized linear model framework
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.	Hilbe, J. M. (2011). Negative Binomial Regression (2nd ed.). Cambridge University Press.
Alias	logit model, binomial logistic regression, LR	NB regression, NB2 regression, negatif binom regresyonu
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.	Negative Binomial Regression is a generalized linear model for count outcomes that extends Poisson regression to handle overdispersion, where the variance of the counts exceeds their mean. Developed in the GLM tradition and treated in depth by Hilbe (2011), it adds a dispersion parameter so that inference stays valid when Poisson would understate the spread of the data.