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"Dr Russell has produced an accessible and informative text that provides useful methodology for applied researchers and practitioners, and a good introduction for postgraduate students wishing to start researching in the area. Design of experiments is fundamental to the scientific method but, when non-normal data is anticipated, too often either little thought is given to the design, or inappropriate designs tailored to linear models are applied. This book provides the background and methods, including R code, required to start designing better experiments in such situations. The coverage ranges from relatively simple, single factor experiments to multi-factor studies and Bayesian designs using recent research results, making is a valuable addition to many different bookshelves."
—Professor David Woods, University of Southampton

"…this is the first book written specifically on the design of experiments for generalized linear models (GLMs). Code (in R) for handling all the calculations described is available online…This text is helpful as a careful overview of both linear models and GLMs, with some articulation of design implications."
-John H. Maindonald, ISR 2019

"This book fills an important gap in the existing literature on the Design of Experiments. Existing books cover extensively the general linear model, describing topics of Generalized Linear Models (GLMs) for the Design of Experiments (DoE) in only a chapter or so. This book consists of seven chapters. A dedicated website, mainly containing R code for the implementation of the described methods, is maintained by the author (https://doeforglm.com). Known errata can also be found there... particularly useful aspect of the book is the exposition of small sample size effects in the modelling process and ways to cope with this in practice. Small sample sizes are encountered very often in practice in the Design of Experiments, both in the industrial and the agricultural sectors. Similarly, in Chapter 5, the Poisson distribution case is presented, including how to model such data, and how to find relevant D-optimal designs. Useful numerical examples are also given... The book should be particularly useful for researchers working in industry, interested in designing their own experiments when the outcome variable can be modelled by the GLM family of distributions. It could also be very useful for both theoretical and applied researchers in academia, interested in developing skills (each for their own reasons) in this particular area that finds useful applications in different fields of applied research such as -omics and Big Data problems."
- Christos T. Nakas, University of Thessaly, Appeared in ISCB News, January 2020

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"Dr Russell has produced an accessible and informative text that provides useful methodology for applied researchers and practitioners, and a good introduction for postgraduate students wishing to start researching in the area. Design of experiments is fundamental to the scientific method but, when non-normal data is anticipated, too often either little thought is given to the design, or inappropriate designs tailored to linear models are applied. This book provides the background and methods, including R code, required to start designing better experiments in such situations. The coverage ranges from relatively simple, single factor experiments to multi-factor studies and Bayesian designs using recent research results, making is a valuable addition to many different bookshelves."
—Professor David Woods, University of Southampton

"…this is the first book written specifically on the design of experiments for generalized linear models (GLMs). Code (in R) for handling all the calculations described is available online…This text is helpful as a careful overview of both linear models and GLMs, with some articulation of design implications."
-John H. Maindonald, ISR 2019

"This book fills an important gap in the existing literature on the Design of Experiments. Existing books cover extensively the general linear model, describing topics of Generalized Linear Models (GLMs) for the Design of Experiments (DoE) in only a chapter or so. This book consists of seven chapters. A dedicated website, mainly containing R code for the implementation of the described methods, is maintained by the author (https://doeforglm.com). Known errata can also be found there... particularly useful aspect of the book is the exposition of small sample size effects in the modelling process and ways to cope with this in practice. Small sample sizes are encountered very often in practice in the Design of Experiments, both in the industrial and the agricultural sectors. Similarly, in Chapter 5, the Poisson distribution case is presented, including how to model such data, and how to find relevant D-optimal designs. Useful numerical examples are also given... The book should be particularly useful for researchers working in industry, interested in designing their own experiments when the outcome variable can be modelled by the GLM family of distributions. It could also be very useful for both theoretical and applied researchers in academia, interested in developing skills (each for their own reasons) in this particular area that finds useful applications in different fields of applied research such as -omics and Big Data problems."
- Christos T. Nakas, University of Thessaly, Appeared in ISCB News, January 2020

"This book is a legacy of the late author Dr. Kenneth G. Russell (1950–2019), who passed away just months after its publication. Readers of this book will benefit from his career-long knowledge of the topic and passion for passing on the knowledge to others...This book’s style is primarily expository. While the language is concise, the author spares no effort to illustrate important concepts and ensure they are understandable by the intended readership. Especially, the book’s examples are easy to follow (as if the author is speaking directly to you to walk you through each step). The author’s use of language is precise, minimizing the chance of confusing readers...this book cites relevant references for those who want to learn more, instead of providing peripheral details that may hinder the readability. This approach serves well the intended readership who might solely rely on this book to understand the subject in a limited time without external guidance. Thus, I would strongly recommend anyone who collects data for GLMs not hesitate to get this book."
- Youngjun Choe, JASA, April 2021

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