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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

Design of Experiments for Generalized Linear Models

Vorgehensmodell zur Einführung eines strategischen Management-Informationssystems

Peru's Indian Peoples and the Challenge of Spanish Conquest

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