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| Regressione logistica ordinale (Modello a odds proporzionali)× | Regressione Logistica Multinomiale× | |
|---|---|---|
| Campo | Statistica | Statistica |
| Famiglia | Regression model | Regression model |
| Anno di origine≠ | 2010 | 1966–1974 |
| Ideatore≠ | Agresti (textbook treatment); proportional odds model | Cox (1966); Theil (1969); formalized by McFadden (1974) |
| Tipo≠ | Ordinal logistic regression | Generalized linear model |
| Fonte seminale≠ | Agresti, A. (2010). Analysis of Ordinal Categorical Data (2nd ed.). Wiley. DOI ↗ | Agresti, A. (2002). Categorical Data Analysis (2nd ed.). Wiley-Interscience. ISBN: 978-0471360933 |
| Alias | proportional odds model, ordered logit, ordinal logistic regression, Ordinal Regresyon (Proportional Odds) | polytomous logistic regression, softmax regression, multinomial logit, nominal logistic regression |
| Correlati≠ | 5 | 4 |
| Sintesi≠ | Ordinal logistic regression models an ordered categorical outcome — such as a Likert rating, a satisfaction level, or an education tier — as a function of predictors. It is the ordinal extension of logistic regression, developed in standard treatments such as Agresti's Analysis of Ordinal Categorical Data (2010), and in its most common form it is the proportional odds model. | Multinomial logistic regression extends binary logistic regression to outcomes with three or more unordered categories. It models the log-odds of each category relative to a chosen reference category as a linear function of the predictors, and estimates all parameters simultaneously via maximum likelihood. It is the standard choice when the dependent variable is nominal with multiple levels. |
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