Сравнение методов

	Ординальная логистическая регрессия	Регрессия методом обыкновенных наименьших квадратов (ОНМК)
Область	Статистика	Эконометрика
Семейство	Regression model	Regression model
Год появления	1980	2019
Автор метода	Peter McCullagh	Wooldridge (textbook treatment); classical least squares
Тип	Ordinal regression / GLM	Linear regression
Основополагающий источник	McCullagh, P. (1980). Regression models for ordinal data. Journal of the Royal Statistical Society: Series B (Methodological), 42(2), 109–142.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Другие названия	proportional-odds model, cumulative link model, ordered logit, OLR	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Связанные	6	5
Сводка	Ordinal logistic regression — most commonly the proportional-odds model — estimates the relationship between one or more predictors and an ordered categorical outcome (e.g., Likert scales, disease severity grades, educational attainment levels). It models cumulative log-odds across the ordered categories while assuming a single shared effect of each predictor at all thresholds.	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).