Prediction properties of optimum response surface designs

TL;DR

This paper extends response surface design criteria to enhance prediction accuracy for responses and differences, introducing new graphical tools and illustrating their effectiveness through examples.

Contribution

It introduces an extended design criterion incorporating prediction properties and new graphical tools for assessing prediction performance across the experimental region.

Findings

Enhanced prediction accuracy in response surface designs

Effective graphical tools for prediction performance assessment

Illustrative examples demonstrating the methods' utility

Abstract

Prediction capability is considered an important issue in response surface methodology. Following the line of argument that a design should have several desirable properties we have extended an existing compound design criterion to include prediction properties. Prediction of responses and of differences in response are considered. Point and interval predictions are allowed for. Extensions of existing graphical tools for inspecting prediction performances of the designs in the whole region of experimentation are also introduced. The methods are illustrated with two examples.