Regression analysis of data obtained in statistically designed experiments has become an important source of information for the quality practitioner working on improving industrial processes. Least squares regression analysis is the usual method used to fit an equation to data. Recently developed robust regression methods in many cases provide a more accurate equation and these methods can be performed using available statistical software. Cases where robust methods would provide a more accurate equation are described and the reality of these situations in industrial or laboratory experiments is discussed. The value and practical aspects of using robust regression are illustrated with data from real experiments.