Using I-Optimal Designs for Narrower Confidence Limits William D. Kappele Introduction Industrial experiments demand high quality predictions of product performance from a small amount of data. Scientists and engineers need experiment designs which provide narrow confidence limits on predictions to meet this demand. The effect of a design on the quality of predictions is seldom considered when selecting a design. This paper shows the advantages of I-optimal designs over conventional designs used in industry. Confidence intervals for predictions will show that I-optimal designs are better.
Classic designs spread the points out in space.
I-optimal designs spread the points out to provide the minimum average variance of predictions over the region of interest. The variance is the square of the standard deviation of the prediction from the fitted model. I-optimal designs can be difficult to make. Dave Doehlert, Neil Sloane and Ron Hardin have developed a means of making I-optimal designs relatively quickly. Doehlert, Hardin and Sloane have made it feasible for industrial experimenters to use I-optimal designs in their work.