Two-level factorial designs with extreme numbers of level changes

Optimal Designs for Two-level Factorial Experiments with Binary Response

We consider the problem of obtaining locally D-optimal designs for factorial experiments with qualitative factors at two levels each with binary response. Our focus is primarily on the 2 experiment. In this paper, we derive analytic results for some special cases and indicate how to handle the general case. The performance of the uniform design in examined and we show that this design is highly...

Fitting Second-order Models to Mixed Two-level and Four-level Factorial Designs: Is There an Easier Procedure?

Fitting response surface models is usually carried out using statistical packages to solve complicated equations in order to produce the estimates of the model coefficients. This paper proposes a new procedure for fitting response surface models to mixed two-level and four-level factorial designs. New and easier formulae are suggested to calculate the linear, quadratic and the interaction coeff...

Optimal Foldover Plans for Two-Level Fractional Factorial Designs

A commonly used follow-up experiment strategy involves the use of a foldover design by reversing the signs of one or more columns of the initial design. DeŽ ning a foldover plan as the collection of columns whose signs are to be reversed in the foldover design, this article answers the following question: Given a 2kp design with k factors and p generators, what is its optimal foldover plan? We ...

A Note on Dominating Fractional Factorial Two-Level Designs With Clear Two-Factor Interactions

This note builds on results from Wu, Mee and Tang‘s (2012) article (henceforth WMT) on admissible fractional factorial two-level designs, specifically concentrating on the “dominating designs” that have been introduced but not further pursued in WMT. WMT’s work has been used for increasing the efficiency of the author’s graph-based algorithm for creation of minimum aberration designs that keep ...

Construction of Efficient Fractional Factorial Mixed-Level Designs