This book is well-written and attractively laid out. It provides an in-depth but practical look at Analysis of Means, written by experts on the topic. It seems sad that ANOM is treated as the ugly stepsister of ANOVA, especially as the graphical analyses provided are powerful, and intuitive to the non-statistician. These authors redress this sorry state of affairs, and provide worked examples of many variants of ANOM, useful in settings of industry and health-care.
The worked examples are most useful, and the book is fully referenced for those who wish to delve into the intricacies of the underlying theory.
I have just two criticisms:
1. The typesetting is excellent, but in the font chosen, a 'subscripted' capital I looks somewhat like a 1, which can be slightly confusing, although the context always makes the distinction clear;
2. The authors do not explore what to do where a normal assumption is not justified in the case of Binomial data, in Sections 2.3 and 3.3. According to their criteria for a "sufficient sample size", 'sufficiency' here depends on the numerator (although this is obscure at first glance), so even with a large denominator the criterion may not be met. (This makes sense, but what do you then do?)
I'd recommend you buy this book, if ANOM intrigues you (as it should). The key value is perhaps in the tables at the back, which allow you to specify precisely the confidence intervals for comparison of up to 20 means. This is particularly useful if you don't have SAS or Minitab, as the R programming environment doesn't yet (2011) seem to have substantial ANOM capabilities. You would be hard put to create these tables yourself, as they require multiple integrations in the complex plane.
ISBN-13:
978-0898715927
ISBN-10:
089871592X
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