Compara mètodes

	Regressió Logística	Regressió de Poisson i binomial negativa
Camp	Estadística per a la recerca	Econometria
Família	Process / pipeline	Regression model
Any d'origen	1958	1998
Autor original	David Roxbee Cox	Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)
Tipus	Method	Generalized linear model for count data
Font seminal	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.
Àlies	logit model, binomial logistic regression, LR	count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon
Relacionats	3	4
Resum	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.