قارن الطرق

	الانحدار متعدد الحدود	انحدار لاسو	منهجية سطح الاستجابة (RSM)
المجال	الإحصاء	تعلم الآلة	التصميم التجريبي
العائلة	Regression model	Machine learning	Hypothesis test
سنة النشأة	2012	1996	1951
صاحب الطريقة	Montgomery, Peck & Vining (textbook treatment); classical least squares	Tibshirani, R.	George E. P. Box & K. B. Wilson
النوع	Linear regression in transformed predictors	Regularized linear regression (L1 penalty)	Second-order polynomial response surface model
المصدر التأسيسي	Montgomery, D. C., Peck, E. A. & Vining, G. G. (2012). Introduction to Linear Regression Analysis. Wiley. ISBN: 978-0470542811	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.	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.
الأسماء البديلة	polynomial least squares, curvilinear regression, Polinom Regresyonu	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization	RSM, Central Composite Design, Box-Behnken Design, CCD
ذات صلة	4	4	7
الملخص	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.	Lasso regression, introduced by Robert Tibshirani in 1996, is a linear regression method that adds an L1 penalty to the loss so that it shrinks coefficients and performs variable selection at the same time, producing a sparse model. By driving some coefficients exactly to zero it keeps only the predictors that matter.	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.