পদ্ধতির তুলনা করুন

	লজিস্টিক রিগ্রেশন	নেগেটিভ বাইনোমিয়াল রিগ্রেশন	পয়সোঁ ও নেগেটিভ বাইনোমিয়াল রিগ্রেশন
ক্ষেত্র	গবেষণা পরিসংখ্যান	অর্থমিতি	অর্থমিতি
পরিবার	Process / pipeline	Regression model	Regression model
উদ্ভবের বছর	1958	2011	1998
প্রবর্তক	David Roxbee Cox	Hilbe (textbook treatment); generalized linear model framework	Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)
ধরন	Method	Generalized linear model for count data	Generalized linear model for count data
মৌলিক উৎস	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.	Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press.
অপর নাম	logit model, binomial logistic regression, LR	NB regression, NB2 regression, negatif binom regresyonu	count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon
সম্পর্কিত	3	4	4
সারসংক্ষেপ	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.	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.