Comparar métodos

	Regressão Logística	Regressão Binomial Negativa	Regressão de Poisson e Binomial Negativa
Área	Estatística para pesquisa	Econometria	Econometria
Família	Process / pipeline	Regression model	Regression model
Ano de origem	1958	2011	1998
Autor original	David Roxbee Cox	Hilbe (textbook treatment); generalized linear model framework	Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)
Tipo	Method	Generalized linear model for count data	Generalized linear model for count data
Fonte seminal	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.	Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press.
Outros nomes	logit model, binomial logistic regression, LR	NB regression, NB2 regression, negatif binom regresyonu	count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon
Relacionados	3	4	4
Resumo	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.	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.