विधियों की तुलना करें

	पॉइसन और ऋणात्मक द्विपद प्रतिगमन (Poisson and Negative Binomial Regression)	साधारण न्यूनतम वर्ग (OLS) समाश्रयण
क्षेत्र	अर्थमिति	अर्थमिति
परिवार	Regression model	Regression model
उद्भव वर्ष	1998	2019
प्रवर्तक	Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)	Wooldridge (textbook treatment); classical least squares
प्रकार	Generalized linear model for count data	Linear regression
मौलिक स्रोत	Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
उपनाम	count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
संबंधित	4	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.	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).