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Statistical methods in biology : design and analysis of experiments and regression / S. J. Welham...[et al.].

By: Contributor(s): Material type: TextTextPublication details: Boca Raton : CRC Press, ©2015.Description: xx, 582 p. : illustrations ; 27 cmISBN:
  • 9781439808788 (hardback : acidfree paper)
Subject(s): DDC classification:
  • 000SB:570 23 W446
Contents:
1. Introduction -- 2. A review of basic statistics -- 3. Principles for designing experiments -- 4. Models for a single factor -- 5. Checking model assumptions -- 6. Transformations of the response -- 7. Models with a simple blocking structure -- 8. Extracting information about treatments -- 9. Models with more complex blocking structure -- 10. Replication and power -- 11. Dealing with non-orthogonality -- 12. Models for a single variate : simple linear regression -- 13. Checking model fit -- 14. Models for several variates : multiple linear regression -- 15. Models for variates and factors -- 16. Incorporating structure : linear mixed models -- 17. Models for curved relationships -- 18. Models for non-normal responses : generalized linear models -- 19. Practical design and data analysis for real studies.
Summary: "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® statistical package for more complex examples. Each chapter offers instructions on how to obtain the example analyses in GenStat and R"--Summary: "This book provides an introductory, practical and illustrative guide to design of experiments and data analysis in the biological and agricultural plant sciences. It is aimed both at research scientists and at students (from final year undergraduate level through taught masters to PhD students) who either need to design their own experiments and perform their own analyses or can consult with a professional applied statistician, and want to have a clear understanding of the methods that they are using. The material is based on courses developed at two British research institutes (Rothamsted Research and Horticulture Research International (HRI)--then Warwick HRI, and now the School of Life Science, University of Warwick) to train research scientists and post-graduate students in these key areas of statistics. Our overall approach is intended to be practical and intuitive rather than overly theoretical, with mathematical formulae presented only to formalise the methods where appropriate and necessary. Our intention is to present statistical ideas in the context of the biological and agricultural sciences to which they are being applied, drawing on relevant examples from our own experiences as consultant applied statisticians at research institutes, to encourage best practice in design and data analysis. The first two chapters of this book provide introductory, review and background material. In Chapter 1, we introduce types of data and statistical models, together with an overview of the basic statistical concepts and terminology used throughout"--
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Includes bibliographical references and index.

1. Introduction --
2. A review of basic statistics --
3. Principles for designing experiments --
4. Models for a single factor --
5. Checking model assumptions --
6. Transformations of the response --
7. Models with a simple blocking structure --
8. Extracting information about treatments --
9. Models with more complex blocking structure --
10. Replication and power --
11. Dealing with non-orthogonality --
12. Models for a single variate : simple linear regression --
13. Checking model fit --
14. Models for several variates : multiple linear regression --
15. Models for variates and factors --
16. Incorporating structure : linear mixed models --
17. Models for curved relationships --
18. Models for non-normal responses : generalized linear models --
19. Practical design and data analysis for real studies.

"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® statistical package for more complex examples. Each chapter offers instructions on how to obtain the example analyses in GenStat and R"--

"This book provides an introductory, practical and illustrative guide to design of experiments and data analysis in the biological and agricultural plant sciences. It is aimed both at research scientists and at students (from final year undergraduate level through taught masters to PhD students) who either need to design their own experiments and perform their own analyses or can consult with a professional applied statistician, and want to have a clear understanding of the methods that they are using. The material is based on courses developed at two British research institutes (Rothamsted Research and Horticulture Research International (HRI)--then Warwick HRI, and now the School of Life Science, University of Warwick) to train research scientists and post-graduate students in these key areas of statistics. Our overall approach is intended to be practical and intuitive rather than overly theoretical, with mathematical formulae presented only to formalise the methods where appropriate and necessary. Our intention is to present statistical ideas in the context of the biological and agricultural sciences to which they are being applied, drawing on relevant examples from our own experiences as consultant applied statisticians at research institutes, to encourage best practice in design and data analysis. The first two chapters of this book provide introductory, review and background material. In Chapter 1, we introduce types of data and statistical models, together with an overview of the basic statistical concepts and terminology used throughout"--

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