Salīdzināt metodes

	Ordinālā loģistikās regresijas modelis (proporcionālo izredžu modelis)	Puasona un negatīvās binomiālās regresijas
Nozare	Statistika	Ekonometrija
Saime	Regression model	Regression model
Izcelsmes gads	2010	1998
Autors	Agresti (textbook treatment); proportional odds model	Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)
Tips	Ordinal logistic regression	Generalized linear model for count data
Pirmavots	Agresti, A. (2010). Analysis of Ordinal Categorical Data (2nd ed.). Wiley.	Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press.
Citi nosaukumi	proportional odds model, ordered logit, ordinal logistic regression, Ordinal Regresyon (Proportional Odds)	count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon
Saistītās	5	4
Kopsavilkums	Ordinal logistic regression models an ordered categorical outcome — such as a Likert rating, a satisfaction level, or an education tier — as a function of predictors. It is the ordinal extension of logistic regression, developed in standard treatments such as Agresti's Analysis of Ordinal Categorical Data (2010), and in its most common form it is the proportional odds model.	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.