Bandingkan metode

	Regresi Logistik Berurutan (Logit/Probit Berurutan)	Regresi Logistik	Regresi Binomial Negatif
Bidang	Ekonometrika	Statistika Penelitian	Ekonometrika
Keluarga	Regression model	Process / pipeline	Regression model
Tahun asal	1980	1958	2011
Pencetus	McCullagh (proportional odds / cumulative model)	David Roxbee Cox	Hilbe (textbook treatment); generalized linear model framework
Tipe	Cumulative ordinal regression	Method	Generalized linear model for count data
Sumber perintis	McCullagh, P. (1980). Regression Models for Ordinal Data. Journal of the Royal Statistical Society: Series B, 42(2), 109-142.	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.
Alias	ordinal logistic regression, proportional odds model, cumulative logit model, ordered probit	logit model, binomial logistic regression, LR	NB regression, NB2 regression, negatif binom regresyonu
Terkait	4	3	4
Ringkasan	Ordered logit is a cumulative regression model for an ordinal dependent variable, fitting a logit (or probit) link to the cumulative category probabilities. Developed in McCullagh's 1980 treatment of regression models for ordinal data, it is the standard tool for Likert-scale, rating, and ranked outcomes.	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.