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	पॉइसन और ऋणात्मक द्विपद प्रतिगमन (Poisson and Negative Binomial Regression)	लॉजिस्टिक रिग्रेशन	साधारण न्यूनतम वर्ग (OLS) समाश्रयण	पैनल डेटा फिक्स्ड इफेक्ट्स मॉडल
क्षेत्र	अर्थमिति	अनुसंधान सांख्यिकी	अर्थमिति	अर्थमिति
परिवार	Regression model	Process / pipeline	Regression model	Regression model
उद्भव वर्ष	1998	1958	2019	2014
प्रवर्तक	Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)	David Roxbee Cox	Wooldridge (textbook treatment); classical least squares	Hsiao (textbook treatment); within transformation of panel data
प्रकार	Generalized linear model for count data	Method	Linear regression	Panel data regression
मौलिक स्रोत	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.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press.
उपनाम	count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon	logit model, binomial logistic regression, LR	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli
संबंधित	4	3	5	5
सारांश	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.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).	The Panel Data Fixed Effects model estimates relationships from panel data (the same units observed over several time periods) while controlling for unit- and/or time-specific effects, supporting causal inference. It is developed as the within estimator in standard treatments such as Hsiao's Analysis of Panel Data (2014).