방법 비교

	강건 ANOVA (Welch 및 절사 평균)	최소제곱법(OLS) 회귀	순열 (무작위화) 검정
분야	통계학	계량경제학	통계학
계열	Regression model	Regression model	Regression model
기원 연도	1951	2019	2005
창시자	Welch (1951); robust trimmed-mean approach popularised by Wilcox	Wooldridge (textbook treatment); classical least squares	Good (2005); Edgington & Onghena (2007); resampling tradition
유형	Robust one-way analysis of variance	Linear regression	Nonparametric resampling test
원전	Welch, B. L. (1951). On the comparison of several mean values: an alternative approach. Biometrika, 38(3/4), 330-336.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	Good, P. (2005). Permutation, Parametric and Bootstrap Tests of Hypotheses (3rd ed.). Springer. ISBN: 978-0387202792
별칭	Welch ANOVA, trimmed-mean ANOVA, heteroscedastic one-way ANOVA, Robust ANOVA (Welch & Trimmed Mean)	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	randomization test, exact permutation test, re-randomization test, Permütasyon Testi
관련	5	5	5
요약	Robust ANOVA compares the central tendency of three or more groups when the classical assumptions of normality and equal variances fail. It combines Welch's heteroscedasticity-adjusted statistic, introduced by Welch in 1951, with trimmed-mean tests advanced by Wilcox, giving reliable comparisons in the presence of outliers and unequal group spreads.	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).	The permutation test is a nonparametric resampling procedure that builds the sampling distribution of a test statistic directly from the data by repeatedly shuffling the group labels. Developed in the resampling tradition and treated systematically by Good (2005) and Edgington & Onghena (2007), it requires no parametric distributional assumption and yields an exact p-value.