Salīdzināt metodes

	Polinomu regresija	Metodoloģija virsmas atbildes (RSM)
Nozare	Statistika	Eksperimentu plānošana
Saime	Regression model	Hypothesis test
Izcelsmes gads	2012	1951
Autors	Montgomery, Peck & Vining (textbook treatment); classical least squares	George E. P. Box & K. B. Wilson
Tips	Linear regression in transformed predictors	Second-order polynomial response surface model
Pirmavots	Montgomery, D. C., Peck, E. A. & Vining, G. G. (2012). Introduction to Linear Regression Analysis. Wiley. ISBN: 978-0470542811	Box, G. E. P. & Wilson, K. B. (1951). On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society, Series B, 13(1), 1–45.
Citi nosaukumi	polynomial least squares, curvilinear regression, Polinom Regresyonu	RSM, Central Composite Design, Box-Behnken Design, CCD
Saistītās	4	7
Kopsavilkums	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.	Response Surface Methodology is a collection of statistical and mathematical techniques for building an empirical second-order polynomial model that relates a continuous response variable to two or more controllable input factors, and then locating the factor settings that optimize that response. The approach was introduced by George E. P. Box and K. B. Wilson in their landmark 1951 paper and has since become a cornerstone of process optimization across engineering, chemistry, food science, and pharmaceutics.