Krahasoni metodat

	Regresioni Poisson dhe Binomial Negativ	Regresioni logjistik
Fusha	Ekonometri	Statistika e hulumtimit
Familja	Regression model	Process / pipeline
Viti i origjinës	1998	1958
Krijuesi	Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)	David Roxbee Cox
Lloji	Generalized linear model for count data	Method
Burimi themelues	Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
Emërtime të tjera	count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon	logit model, binomial logistic regression, LR
Të lidhura	4	3
Përmbledhja	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.	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.