방법 비교

	로지스틱 회귀	포아송 및 음이항 회귀분석
분야	연구 통계	계량경제학
계열	Process / pipeline	Regression model
기원 연도	1958	1998
창시자	David Roxbee Cox	Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)
유형	Method	Generalized linear model for count data
원전	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.
별칭	logit model, binomial logistic regression, LR	count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon
관련	3	4
요약	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.