قارن الطرق

	انحدار المربعات الصغرى العادية (OLS)	منهجية سطح الاستجابة (RSM)	انحدار ريدج (Ridge Regression)
المجال	الاقتصاد القياسي	التصميم التجريبي	تعلم الآلة
العائلة	Regression model	Hypothesis test	Machine learning
سنة النشأة	2019	1951	1970
صاحب الطريقة	Wooldridge (textbook treatment); classical least squares	George E. P. Box & K. B. Wilson	Hoerl, A.E. & Kennard, R.W.
النوع	Linear regression	Second-order polynomial response surface model	L2-regularized linear regression
المصدر التأسيسي	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.	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.
الأسماء البديلة	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	RSM, Central Composite Design, Box-Behnken Design, CCD	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
ذات صلة	5	7	4
الملخص	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.	Ridge Regression is an L2-regularized linear regression method, introduced by Arthur Hoerl and Robert Kennard in 1970, that reduces multicollinearity by adding a penalty on the size of the coefficients. It shrinks coefficients toward zero without setting any of them exactly to zero, producing more stable estimates when predictors are highly correlated.