Chapter 10 summarizes a wide range of robust regression estimators. Their relative merits are discussed. Generally, these estimators deal effectively with regression outliers and leverage points. Some can offer a substantial advantage, in terms of efficiency, when there is heteroscedasticity. Included are robust versions of logistic regression and recently derived methods for dealing with multivariate regression, two of which take into account the association among the outcome variables, in contrast to most estimators that have been proposed. R functions for applying these estimators are described.