Confronta i metodi
Esamina i metodi selezionati fianco a fianco; le righe che differiscono sono evidenziate.
| Regressione Logistica Ordinata (Logit/Probit Ordinato)× | Regressione Logistica× | |
|---|---|---|
| Campo≠ | Econometria | Statistica per la ricerca |
| Famiglia≠ | Regression model | Process / pipeline |
| Anno di origine≠ | 1980 | 1958 |
| Ideatore≠ | McCullagh (proportional odds / cumulative model) | David Roxbee Cox |
| Tipo≠ | Cumulative ordinal regression | Method |
| Fonte seminale≠ | McCullagh, P. (1980). Regression Models for Ordinal Data. Journal of the Royal Statistical Society: Series B, 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≠ | ordinal logistic regression, proportional odds model, cumulative logit model, ordered probit | logit model, binomial logistic regression, LR |
| Correlati≠ | 4 | 3 |
| Sintesi≠ | 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. |
| ScholarGateInsieme di dati ↗ |
|
|