Compară metode

	Regresia logistică ordinală (modelul cotelor proporționale)	Regresia Logistică
Domeniu	Statistică	Statistică pentru cercetare
Familie	Regression model	Process / pipeline
Anul apariției	2010	1958
Autorul original	Agresti (textbook treatment); proportional odds model	David Roxbee Cox
Tip	Ordinal logistic regression	Method
Sursa seminală	Agresti, A. (2010). Analysis of Ordinal Categorical Data (2nd ed.). Wiley.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
Denumiri alternative	proportional odds model, ordered logit, ordinal logistic regression, Ordinal Regresyon (Proportional Odds)	logit model, binomial logistic regression, LR
Înrudite	5	3
Rezumat	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.	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.