Use the Custom Design platform to create response surface experiments. Response surface experiments typically involve a small number (generally 2 to 8) of continuous factors that have been identified as active. The main goal of a response surface experiment is to develop a predictive model of the relationship between the factors and the response. Often, you use the predictive model to find better operating settings for your process. For this reason, your assumed model for a response surface experiment is usually quadratic.
Custom Design uses the I-optimality criterion as the recommended criterion whenever you add quadratic effects using the RSM button in the Model section. In response surface experiments, the prediction variance over the range of the factors is important. I-optimal designs minimize the average variance of prediction over the design space. For more information about optimality, see “Optimality Criteria”.
The following examples illustrate constructing response surface designs using the Custom Design platform.
• Response Surface Design with Flexible Blocking
• Comparison of a D-Optimal and an I-Optimal Response Surface Design
• Response Surface Design With Constraints and Categorical Factor