Sammenlign metoder

	Ordinær logistisk regresjon (Ordinær logit/probit)	Logistisk regresjon	Minste kvadraters metode (OLS)
Fagfelt	Økonometri	Forskningsstatistikk	Økonometri
Familie	Regression model	Process / pipeline	Regression model
Opprinnelsesår	1980	1958	2019
Opphavsperson	McCullagh (proportional odds / cumulative model)	David Roxbee Cox	Wooldridge (textbook treatment); classical least squares
Type	Cumulative ordinal regression	Method	Linear regression
Opprinnelig kilde	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.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Alias	ordinal logistic regression, proportional odds model, cumulative logit model, ordered probit	logit model, binomial logistic regression, LR	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Relaterte	4	3	5
Sammendrag	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.	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).