เปรียบเทียบวิธี

	การถดถอยพัวซงและทวินามเชิงลบ	การถดถอยโลจิสติก
สาขาวิชา	เศรษฐมิติ	สถิติการวิจัย
ตระกูล	Regression model	Process / pipeline
ปีกำเนิด	1998	1958
ผู้ริเริ่ม	Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)	David Roxbee Cox
ประเภท	Generalized linear model for count data	Method
แหล่งต้นตำรับ	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.
ชื่อเรียกอื่น	count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon	logit model, binomial logistic regression, LR
ที่เกี่ยวข้อง	4	3
สรุป	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.