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on 30 August 2009
This book has really saved my bacon! I have a difficult dataset to work with, and were it not for this book I would have been completely lost. It's great whether or not you are good at statistics, as it provides basic descriptions as well as more detailed mathematical sections. R code is provided throughout, as well as explanations for what the code does - great for beginners and more advanced users! Real-world examples are used to illustrate the uses for different types of analysis, and some of the issues that can be overcome. Well worth the investment, in my opinion.
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on 5 September 2012
This book is fantastic. Zuur et al. lay out generalized protocols for the analysis of many kinds of data, and provides all the resources necessary to implement these procedures in the R statistical language. Despite the seemingly specific title, this book covers analysis from basic ANOVA and linear regression through Poisson or binomial regression with random effects (GLMM). I was near-instantly able to apply the content of this book to my data. Furthermore, while it uses numerous ecological examples with great detail, the content should easily be transferable to any disciplines that implement linear regression, such as psychology or the social sciences.

I might as well gripe a little bit. While the authors are careful to provide protocols for most types of analysis, some things appear to be missing, such as model validation techniques for some forms of GLMM. The authors point out, however, that these advanced analyses are relatively new and on the forefront of statistical research, so it's quite possible that these techniques don't exist.

In any case, it's a great book. Highly recommended.
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on 28 March 2014
Together with the previous volume this book represent very useful cookbook for all, who find any appropriate model for own data and projects. Written based on the published papers the text show the model philosophy, processing and also what should be presented / published. Extremely valuable book with R scripts and results of many ecological fields from botany to zoology, spatial and temporal level of autocorrelation, etc. I can recommend this book to everybody who needs good multivariate statistics with partial tests for papers with high impact, esp. for university students, postdocs, lecturers, etc. This is a really good choice.
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on 24 May 2013
I highly recommend this book. It manages to make the most complex statistics fathomable to the non-mathematical, which let's face it, most of us are. For years I've struggled with the fact that most ecological data cannot be analysed correctly using simple statistical tests. Here, in one easy-to-read book, is the answer to most of the statistical problems I've struggled with for the last 15 years of my academic career. I now make it essential reading for members of my research group.
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on 16 January 2010
This book is simply superb! Very accessible, lots of applied examples complete with R code and well worth the money. Fairly comprehensive bibliography as well. You don't need to be a mathematical/statistical genius to benefit from its content. Highly recommended.
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on 23 September 2013
This book has some maths in it but is aimed at biologists without being too heavy on the maths. A very good explanation of mixed effects models
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