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| Regressione Lineare Semplice× | Regressione polinomiale× | |
|---|---|---|
| Campo | Statistica | Statistica |
| Famiglia | Regression model | Regression model |
| Anno di origine≠ | 1805 | 2012 |
| Ideatore≠ | Adrien-Marie Legendre (least squares, 1805); Francis Galton (regression concept, 1886) | Montgomery, Peck & Vining (textbook treatment); classical least squares |
| Tipo≠ | Parametric bivariate regression | Linear regression in transformed predictors |
| 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 ↗ | Montgomery, D. C., Peck, E. A. & Vining, G. G. (2012). Introduction to Linear Regression Analysis. Wiley. ISBN: 978-0470542811 |
| Alias≠ | SLR, ordinary least squares regression, OLS regression, bivariate regression | polynomial least squares, curvilinear regression, Polinom Regresyonu |
| Correlati≠ | 7 | 4 |
| 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. | Polynomial regression is a regression method that models non-linear relationships by including squared and higher-degree terms of an explanatory variable, and it is a core tool of response surface analysis. As developed in Montgomery, Peck and Vining's Introduction to Linear Regression Analysis (2012), it remains linear in its parameters even though the fitted curve bends. |
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