Publisher Summary This chapter focuses on response surface methods. By means of a response surface model, it is not only possible to locate the optimum conditions, but also to analyze how sensitive the optimum conditions are to variations in the settings of experimental variables. Another advantage of response surface models is that it is possible to make different projections, which provide graphic illustrations of the shape of the surfaces, thus, allowing a visual interpretation of functional relations between the response and the experimental variables. This may give an intuitive feeling of what is going on in the system studied. Interdependencies among the experimental variables will influence the actual shape of the response surface. Such relations can be examined by canonical analysis of the response surface model. This may give valuable clues to mechanistic details of the reaction. The technique to establish the response surface models is also simple: experiments are laid out by a design that spreads the settings of the experimental variables over the experimental domain of interest. The model parameters are then estimated by a least squares fitting of the model to the experimental results obtained in the design points.