Robust regression is a method used when there are errors in data that don't follow a normal pattern or when there are outliers affecting the results. Different techniques have been developed over time to handle these issues, with some focusing on high breakdown points and others on minimizing the impact of outliers. The chapter discusses various robust regression estimators, including least median of squares, least trimmed squares, and S-estimators, which can be used together to improve accuracy.