Сравнение методов

	Полиномиальная регрессия	Регрессия методом обыкновенных наименьших квадратов (ОНМК)	Методология поверхности отклика (RSM)
Область	Статистика	Эконометрика	Планирование эксперимента
Семейство	Regression model	Regression model	Hypothesis test
Год появления	2012	2019	1951
Автор метода	Montgomery, Peck & Vining (textbook treatment); classical least squares	Wooldridge (textbook treatment); classical least squares	George E. P. Box & K. B. Wilson
Тип	Linear regression in transformed predictors	Linear regression	Second-order polynomial response surface model
Основополагающий источник	Montgomery, D. C., Peck, E. A. & Vining, G. G. (2012). Introduction to Linear Regression Analysis. Wiley. ISBN: 978-0470542811	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	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.
Другие названия	polynomial least squares, curvilinear regression, Polinom Regresyonu	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	RSM, Central Composite Design, Box-Behnken Design, CCD
Связанные	4	5	7
Сводка	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.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).	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.