قارن الطرق

	انحدار بواسون والانحدار ذي الحدين السالب	الانحدار اللوجستي
المجال	الاقتصاد القياسي	إحصاء البحث
العائلة	Regression model	Process / pipeline
سنة النشأة	1998	1958
صاحب الطريقة	Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)	David Roxbee Cox
النوع	Generalized linear model for count data	Method
المصدر التأسيسي	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.
الأسماء البديلة	count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon	logit model, binomial logistic regression, LR
ذات صلة	4	3
الملخص	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.