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

Economic Control of Quality of Manufactured Product.

Generalized Linear Models (2nd ed.)

Survival Analysis: Techniques for Censored and Truncated Data (2nd ed.) (Book)

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.

To summarize, Basic Statistics and Data Analysis is not suitable for students majoring in math, science, and engineering, because of the sparse coverage of statistical topics applicable to these fields. As a general audience text, the book is well written in terms of style and readability. However, the instructor would have to be predisposed to include a heavy dose of nonparametric statistics in an introductory course and plan on presenting clearer guidelines on when such tests are appropriate.

Statistics for Research

Sliced Latin hypercube designs (SLHDs) have important applications in designing computer experiments with continuous and categorical factors. However, a randomly generated SLHD can be poor in terms of space-filling, and based on the existing construction method that generates the SLHD column by column using sliced permutation matrices, it is also difficult to search for the optimal SLHD. In this article, we develop a new construction approach that first generates the small Latin hypercube design in each slice and then arranges them together to form the SLHD. The new approach is intuitive and can be easily adapted to generate orthogonal SLHDs and orthogonal array-based SLHDs. More importantly, it enables us to develop general algorithms that can search for the optimal SLHD efficiently.

Optimal Sliced Latin Hypercube Designs

Robust parameter design (RPD) as used in Industrial experiments is usually applied assuming a linear model under standard assumptions, but has recently also been considered in a generalized linear model (GLM) setting. The application of a general dual-r..

A dual-response approach to robust Parameter Design for a generalized linear model

There has been considerable interest recently in the use of statistically designed experiments to identify both location and dispersion effects for quality improvement. Analysis of dispersion effects usually requires replications that can be expensive or time consuming. Several recent articles have considered identification of both location and dispersion effects from unreplicated fractional factorial experiments. In this article, we provide a systematic study of various methods that are commonly used or have been proposed recently. Both theoretical and simulation results are used to characterize the properties. Although all methods suffer from some degree of bias, some have serious problems when the bias remains large even as the design run size increases to infinity. Based on these analyses, we propose some iterative strategies for model selection and estimation of the dispersion effects. A real example and simulations as well are used to illustrate the results.

Methods for identifying dispersion effects in unreplicated factorial experiments: A critical analysis and proposed strategies

Robust parameter design has been extensively studied and applied in industrial experiments over the past twenty years. The purpose of robust parameter design is to design a process or product that ...

William A. Brenneman

Papers

Statistics for Research

Optimal Sliced Latin Hypercube Designs

A dual-response approach to robust Parameter Design for a generalized linear model

Methods for identifying dispersion effects in unreplicated factorial experiments: A critical analysis and proposed strategies

Robust Parameter Design with Categorical Noise Variables