ABSTRACT
Response surface (RS) models (cf., Myers and Montgomery, 1995) based on optimizations are now commonly used in engineering design. The availability of many optimization runs and the RS approximation provide an opportunity to estimate errors. The first part of this paper shows how we can use the runs to estimate the average optimization error, and the second part shows how we can estimate the error associated with fitting the data with low-order polynomials.
