THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

Statistical Process Control of Multivariate Processes

A multivariate extension of the exponentially weighted moving average (EWMA) control chart is presented, and guidelines given for designing this easy-to-implement multivariate procedure. A comparison shows that the average run length (ARL) performance of this chart is similar to that of multivariate cumulative sum (CUSUM) control charts in detecting a shift in the mean vector of a multivariate normal distribution. As with the Hotelling's χ2 and multivariate CUSUM charts, the ARL performance of the multivariate EWMA chart depends on the underlying mean vector and covariance matrix only through the value of the noncentrality parameter. Worst-case scenarios show that Hotelling's χ2 charts should always be used in conjunction with multivariate CUSUM and EWMA charts to avoid potential inertia problems. Examples are given to illustrate the use of the proposed procedure.

A multivariate exponentially weighted moving average control chart

The purpose of this article is to provide a survey of the various stages in the development of response surface methodology RSM. The coverage of these stages is organized in three parts that describe the evolution of RSM since its introduction in the early 1950s. Part I covers the period, 1951-1975, during which the so-called classical RSM was developed. This includes a review of basic experimental designs for fitting linear response surface models, in addition to a description of methods for the determination of optimum operating conditions. Part II, which covers the period, 1976-1999, discusses more recent modeling techniques in RSM, in addition to a coverage of Taguchi's robust parameter design and its response surface alternative approach. Part III provides a coverage of further extensions and research directions in modern RSM. This includes discussions concerning response surface models with random effects, generalized linear models, and graphical techniques for comparing response surface designs. Copyright © 2010 John Wiley & Sons, Inc.

Response Surface Methodology.

Response surface methodology

We consider several distinct approaches for controlling the mean of a multivariate normal process including two new and distinct multivariate CUSUM charts, several multiple univariate CUSUM charts, and a Shewhart x2 control chart. The performances of th..

Comparisons of Multivariate CUSUM Charts

Virtually every manufactured product has more than one characteristic by which its overall quality is determined. Since those quality characteristics may be correlated, some changes in the levels of the controllable product design and process design variables may improve one of the quality characteristics while adversely affecting one or more of the other quality characteristics. In this paper we define a quadratic loss function for use with multiple quality characteristics. We discuss several strategies that could be employed for robust quality engineering of products and processes when there is more than one quality characteristic of interest.

Strategies for robust multiresponse quality engineering

Statistical process control procedures typically entail monitoring the process by selecting rational subgroups of equal size at equal time intervals. A generalization of this standard paradigm removes the restriction of equal waiting times between subgr..

Adaptive Sampling for Process Control

Comparison of multivariate CUSUM charts

A maximum likelihood estimator is proposed for the time of a step-change in a multivariate process mean when the observations follow a multivariate Normal distribution. The estimator can be used to identify the change point when a multivariate x(2) cont..

Joseph J. Pignatiello

Papers

Comparisons of Multivariate CUSUM Charts

Strategies for robust multiresponse quality engineering

Adaptive Sampling for Process Control

Comparison of multivariate CUSUM charts

Identifying the time of a step-change with x 2 control charts