Overview
Written in simple language with relevant examples, this illustrative introductory book presents best practices in experimental design and simple data analysis. Taking a practical and intuitive approach, it only uses mathematical formulae to formalize the methods where necessary and appropriate. The text features extended discussions of examples that include real data sets arising from research. The authors analyze data in detail to illustrate the use of basic formulae for simple examples while using the GenStat(R) statistical package for more complex examples. Each chapter offers instructions on how to obtain the example analyses in GenStat and R.
Full Product Details
Author: S.J. Welham ,
S.A. Gezan ,
S.J. Clark ,
A. Mead
Publisher: Taylor & Francis Inc
Imprint: Chapman & Hall/CRC
Dimensions:
Width: 17.80cm
, Height: 3.30cm
, Length: 25.40cm
Weight: 1.270kg
ISBN: 9781439808788
ISBN 10: 1439808783
Pages: 602
Publication Date: 22 August 2014
Audience:
College/higher education
,
General/trade
,
Tertiary & Higher Education
,
General
Format: Hardback
Publisher's Status: Active
Availability: In Print
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Reviews
This book is the first serious and successful attempt to teach the general principles underlying sound experimental design and analysis to an audience of students and researchers in biology. The book is written from a strongly applied perspective with lots of real-life examples, but enough mathematical details are given to allow the reader to tailor design and analysis principles to new problems. The leading principle for analysis of experimental data is the multi-stratum analysis of variance. This powerful principle is part of the Rothamsted tradition of applied statistics to which the authors belong. This book makes statistical highlights from that tradition accessible to life scientists without demanding excessive mathematical skills. -Fred van Eeuwijk, Wageningen University and Research Centre This book is easy to read. I am very happy to recommend this book to both scientists as a reference, and to students in the area of agricultural and biological plant sciences as a textbook. It covers most aspects of planning experiments and the steps necessary for analyzing the resulting data, bringing the authors' experience to the reader with real examples. It is obvious that the authors have taken great care in framing their conclusions and the possible interpretations of the results. -Clarice G.B. Demetrio, Professor of Experimental Statistics, Escola Superior de Agricultura Luiz de Queiroz, University of Sao Paulo This book connects the underlying principles of design and statistics to good practice in data analysis. It also gives a solid account of the most commonly used and needed statistical methods in experimental biology. It explains concepts in clear, practical, and accessible ways, using real data to illustrate throughout. Mathematical notation is used when necessary but the explanations of key points are in common language. I would particularly recommend this book for research students. It provides a no-nonsense, gimmick-free account of the key statistical concepts and practices that will allow you to do valid and useful analyses and understand the results from statistical software. -Richard Coe, Principal Scientist-Research Methods, World Agroforestry Centre (ICRAF) and Statistical Services Centre This book will be invaluable to plant scientists who want to develop their knowledge of statistics. Based on courses developed by the authors, the aim is to provide a deep understanding of the most commonly used experimental designs and their analysis, along with linear regression, and to emphasize the connections between these areas. A background in basic statistics, to the level of t-tests, would be helpful, though a revision chapter is provided. Additional chapters introduce linear mixed models and generalised linear models for counts and proportions. The authors' extensive practical experience is apparent throughout, particularly in the many interesting examples that pervade the book. A companion website provides associated data and code in GenStat, R, and (soon) SAS. This in itself is a terrific resource, with 50 well-commented programs covering basic analyses and extensions. Each chapter has exercises; these are often challenging and solutions will be provided on the website. Most of the examples stem from Rothamsted, where all of the authors work or have worked, and the book has something of a Rothamsted 'feel' to it, for example with a strong emphasis on multi-stratum ANOVA to incorporate blocking and other experimental structure even when this is not strictly essential; the benefits of this approach for clarifying more complex designs become apparent as the book progresses. Everything is here for the plant scientist to develop a really solid understanding of the subject and it is difficult to see how the authors could have done a better job of delivering the book that they set out to write. -Martin Ridout, Professor of Applied Statistics, University of Kent
This book connects the underlying principles of design and statistics to good practice in data analysis. It also gives a solid account of the most commonly used and needed statistical methods in experimental biology. It explains concepts in clear, practical, and accessible ways, using real data to illustrate throughout. Mathematical notation is used when necessary but the explanations of key points are in common language. I would particularly recommend this book for research students. It provides a no-nonsense, gimmick-free account of the key statistical concepts and practices that will allow you to do valid and useful analyses and understand the results from statistical software. -Richard Coe, Principal Scientist-Research Methods, World Agroforestry Centre (ICRAF) and Statistical Services Centre This book will be invaluable to plant scientists who want to develop their knowledge of statistics. Based on courses developed by the authors, the aim is to provide a deep understanding of the most commonly used experimental designs and their analysis, along with linear regression, and to emphasize the connections between these areas. A background in basic statistics, to the level of t-tests, would be helpful, though a revision chapter is provided. Additional chapters introduce linear mixed models and generalised linear models for counts and proportions. The authors' extensive practical experience is apparent throughout, particularly in the many interesting examples that pervade the book. A companion website provides associated data and code in GenStat, R, and (soon) SAS. This in itself is a terrific resource, with 50 well-commented programs covering basic analyses and extensions. Each chapter has exercises; these are often challenging and solutions will be provided on the website. Most of the examples stem from Rothamsted, where all of the authors work or have worked, and the book has something of a Rothamsted 'feel' to it, for example with a strong emphasis on multi-stratum ANOVA to incorporate blocking and other experimental structure even when this is not strictly essential; the benefits of this approach for clarifying more complex designs become apparent as the book progresses. Everything is here for the plant scientist to develop a really solid understanding of the subject and it is difficult to see how the authors could have done a better job of delivering the book that they set out to write. -Martin Ridout, Professor of Applied Statistics, University of Kent, UK
Author Information
Salvador Alejandro Gezan, Suzanne Jane Clark, and Suzanne Jane Welham work in the biomathematics and bioinformatics department at Rothamsted Research. Andrew Mead is a senior research scientist in the Horticultural Research Institute at the University of Warwick.
