Bandingkan kaedah

	Regresi Linear Mudah Robust	Regresi Linear Berganda Teguh
Bidang	Statistik	Statistik
Keluarga	Regression model	Regression model
Tahun asal	1964-1987	1964–1980s
Pengasas	Peter J. Huber (M-estimators, 1964); Rousseeuw & Leroy (practical framework, 1987)	Peter J. Huber (M-estimators, 1964); extended by Rousseeuw, Yohai, and Maronna
Jenis	Robust linear regression	Robust linear regression
Sumber perintis	Rousseeuw, P. J., & Leroy, A. M. (1987). Robust Regression and Outlier Detection. John Wiley & Sons. ISBN: 978-0471852339	Huber, P. J. (1964). Robust estimation of a location parameter. Annals of Mathematical Statistics, 35(1), 73–101.
Alias	robust SLR, M-estimator simple regression, outlier-resistant simple regression, robust bivariate regression	robust MLR, M-estimator regression, resistant multiple regression, robust OLS
Berkaitan	6	6
Ringkasan	Robust simple linear regression fits a straight line through bivariate data using loss functions or weighting schemes that down-weight outliers, producing slope and intercept estimates that are far less sensitive to extreme observations than ordinary least squares while remaining easy to interpret.	Robust multiple linear regression estimates the linear relationship between a continuous outcome and several predictors while being resistant to outliers and violations of the normality assumption. Instead of minimising the sum of squared residuals, it uses a bounded loss function — most commonly Huber's or Tukey's bisquare — so that extreme observations receive limited influence on the estimated coefficients.