方法对比

	普通最小二乘法 (OLS) 回归	响应面方法 (RSM)
领域	计量经济学	实验设计
方法族	Regression model	Hypothesis test
起源年份	2019	1951
提出者	Wooldridge (textbook treatment); classical least squares	George E. P. Box & K. B. Wilson
类型	Linear regression	Second-order polynomial response surface model
开创性文献	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.
别名	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	RSM, Central Composite Design, Box-Behnken Design, CCD
相关	5	7
摘要	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.