Compară metode

	Regresie Locală LOESS / LOWESS	Regresie polinomială
Domeniu	Învățare automată	Statistică
Familie	Machine learning	Regression model
Anul apariției	1979	2012
Autorul original	William S. Cleveland	Montgomery, Peck & Vining (textbook treatment); classical least squares
Tip	Local nonparametric regression smoother	Linear regression in transformed predictors
Sursa seminală	Cleveland, W. S. (1979). Robust locally weighted regression and smoothing scatterplots. Journal of the American Statistical Association, 74(368), 829–836.	Montgomery, D. C., Peck, E. A. & Vining, G. G. (2012). Introduction to Linear Regression Analysis. Wiley. ISBN: 978-0470542811
Denumiri alternative	LOWESS, local regression, locally weighted scatterplot smoothing, yerel regresyon	polynomial least squares, curvilinear regression, Polinom Regresyonu
Înrudite	3	4
Rezumat	LOESS (locally estimated scatterplot smoothing), introduced by William Cleveland in 1979 and extended with Susan Devlin in 1988, fits a smooth curve through data by performing a separate weighted polynomial regression in the neighbourhood of each point. Nearby observations count more than distant ones, so the method follows local structure without assuming any global functional form, making it a popular exploratory smoother for scatterplots.	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.