Сравнение на методи

	Логистична регресия	Негативно-биномна регресия
Област	Статистика за изследвания	Иконометрия
Семейство	Process / pipeline	Regression model
Година на възникване	1958	2011
Създател	David Roxbee Cox	Hilbe (textbook treatment); generalized linear model framework
Тип	Method	Generalized linear model for count data
Основополагащ източник	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Hilbe, J. M. (2011). Negative Binomial Regression (2nd ed.). Cambridge University Press.
Други названия	logit model, binomial logistic regression, LR	NB regression, NB2 regression, negatif binom regresyonu
Свързани	3	4
Резюме	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.	Negative Binomial Regression is a generalized linear model for count outcomes that extends Poisson regression to handle overdispersion, where the variance of the counts exceeds their mean. Developed in the GLM tradition and treated in depth by Hilbe (2011), it adds a dispersion parameter so that inference stays valid when Poisson would understate the spread of the data.