Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Régression logistique ordinale× | Régression logistique× | |
|---|---|---|
| Domaine≠ | Statistique | Statistiques de recherche |
| Famille≠ | Regression model | Process / pipeline |
| Année d'origine≠ | 1980 | 1958 |
| Auteur d'origine≠ | Peter McCullagh | David Roxbee Cox |
| Type≠ | Ordinal regression / GLM | Method |
| Source fondatrice≠ | McCullagh, P. (1980). Regression models for ordinal data. Journal of the Royal Statistical Society: Series B (Methodological), 42(2), 109–142. DOI ↗ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ |
| Alias≠ | proportional-odds model, cumulative link model, ordered logit, OLR | logit model, binomial logistic regression, LR |
| Apparentées≠ | 6 | 3 |
| Résumé≠ | 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. | 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. |
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