We continue in this chapter the discussion of methods for dealing with sampling variability in experimental optimization techniques. This chapter considers the effect of statistical sampling error in RSM techniques that are based on second order (quadratic) polynomial models. We first discuss finding confidence intervals for the eigenvalues of the Hessian matrix, that is, the effect of sampling variability in canonical analysis. Later sections consider the related and important problem of finding a confidence region for the optimal operating conditions x0. The unconstrained case is discussed first after which methods for the computation and display of confidence regions on constrained optima are discussed. Any traditional (frequentist) RSM optimization analysis should probably always include such regions.
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© 2007 Springer Science+Business Media, LLC
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(2007). Statistical Inference in Second Order RSM Optimization. In: Process Optimization. International Series in Operations Research & Management Science, vol 105. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-71435-6_7
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DOI: https://doi.org/10.1007/978-0-387-71435-6_7
Publisher Name: Springer, Boston, MA
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Online ISBN: 978-0-387-71435-6
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