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Esamina i metodi selezionati fianco a fianco; le righe che differiscono sono evidenziate.
| Regressione Lineare Semplice× | Regressione Logistica× | |
|---|---|---|
| Campo≠ | Statistica | Statistica per la ricerca |
| Famiglia≠ | Regression model | Process / pipeline |
| Anno di origine≠ | 1805 | 1958 |
| Ideatore≠ | Adrien-Marie Legendre (least squares, 1805); Francis Galton (regression concept, 1886) | David Roxbee Cox |
| Tipo≠ | Parametric bivariate regression | Method |
| Fonte seminale≠ | Legendre, A. M. (1805). Nouvelles méthodes pour la détermination des orbites des comètes. Firmin Didot, Paris. [Appendix: Sur la méthode des moindres quarrés, pp. 72–80] link ↗ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ |
| Alias≠ | SLR, ordinary least squares regression, OLS regression, bivariate regression | logit model, binomial logistic regression, LR |
| Correlati≠ | 7 | 3 |
| Sintesi≠ | Simple linear regression is the foundational parametric method for modelling a straight-line relationship between one continuous predictor and one continuous outcome, estimating the slope and intercept by ordinary least squares (OLS). The least squares principle was first published by Adrien-Marie Legendre in 1805, and Francis Galton introduced the concept of regression to the mean in 1886, coining the term that names the entire family of methods. | 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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