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E-raamat: Analysis of Variance and Covariance: How to Choose and Construct Models for the Life Sciences

(University of Southampton),
  • Formaat: PDF+DRM
  • Ilmumisaeg: 30-Aug-2007
  • Kirjastus: Cambridge University Press
  • Keel: eng
  • ISBN-13: 9780511353192
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  • Formaat: PDF+DRM
  • Ilmumisaeg: 30-Aug-2007
  • Kirjastus: Cambridge University Press
  • Keel: eng
  • ISBN-13: 9780511353192
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A concise, systematic introduction to the principles of analysis of variance for post-graduates and professionals.

Analysis of variance (ANOVA) is a core technique for analysing data in the Life Sciences. This reference book bridges the gap between statistical theory and practical data analysis by presenting a comprehensive set of tables for all standard models of analysis of variance and covariance with up to three treatment factors. The book will serve as a tool to help post-graduates and professionals define their hypotheses, design appropriate experiments, translate them into a statistical model, validate the output from statistics packages and verify results. The systematic layout makes it easy for readers to identify which types of model best fit the themes they are investigating, and to evaluate the strengths and weaknesses of alternative experimental designs. In addition, a concise introduction to the principles of analysis of variance and covariance is provided, alongside worked examples illustrating issues and decisions faced by analysts.

Arvustused

'This is an authoritatively written book aimed at people who already have a good grasp of analysis of (co)variance using fixed factor an(c)ova, who are not afraid of algebraic notation and who wish to understand the background to the comprehensive range of study designs described which incorporate covariates and random factors.' Psychological Medicine 'This book presents details of the analysis of variance for a compendium of designs with up to three treatment factors. The book has a good discussion of practical situations where each design may be useful, ranging from the authors' interests in ecology to more conventional examples from agricultural and medical research. the book has many strengths and I am happy to recommend it.' Experimental Agriculture 'My overall impression is that this text can provide a useful reference for researchers needing a quick refresher on typical design and analysis issues and/or a check on the use of an appropriate design and/or analysis. It does a good job reminding the reader of the complicated issues that can arise and where to be especially cautious. It simplifies some aspects of design and ANOVA but does not attempt to sidestep around or ignore potentially difficult issues, such as unbalanced designs and post hoc pooling of error terms. Will I happily keep this book on my shelf? Yes, most definitely. Although not a stand-alone text on experimental design, it is a useful and usable reference tool.' The American Statistician

Muu info

A concise, systematic introduction to the principles of analysis of variance for post-graduates and professionals.
Preface ix
Introduction to analysis of variance
1(41)
What is analysis of variance?
1(1)
How to read and write statistical models
2(5)
General principles of ANOVA
7(7)
Assumptions of ANOVA
14(2)
How to distinguish between fixed and random factors
16(5)
Nested and crossed factors, and the concept of replication
21(4)
Uses of blocking, split plots and repeated measures
25(4)
Uses of covariates
29(6)
How F-ratios are constructed
35(3)
Use of post hoc pooling
38(2)
Use of quasi F-ratios
40(2)
Introduction to model structures
42(195)
Notation
43(1)
Allocation tables
43(3)
Examples
46(1)
Worked example 1: Nested analysis of variance
47(2)
Worked example 2: Cross-factored analysis of variance
49(2)
Worked example 3: Split-plot, pooling and covariate analysis
51(6)
Key to types of statistical models
57(1)
How to describe a given design with a statistical model
58(3)
One-factor designs
61(6)
One-factor model
62(5)
Nested designs
67(9)
Two-factor nested model
68(4)
Three-factor nested model
72(4)
Fully replicated factorial designs
76(39)
Two-factor fully cross-factored model
78(8)
Three-factor fully cross-factored model
86(12)
Cross-factored with nesting model
98(11)
Nested cross-factored model
109(6)
Randomised-block designs
115(26)
One-factor randomiscd-block model
121(7)
Two-factor randomised-block model
128(6)
Three-factor randomised-block model
134(7)
Split-plot designs
141(38)
Two-factor split-plot model (i)
146(4)
Three-factor split-plot model (i)
150(4)
Three-factor split-plot model (ii)
154(4)
Split-split-plot mode! (i)
158(5)
Split-split-plot model (ii)
163(4)
Two-factor split-plot model (ii)
167(3)
Three-factor split-plot model (iii)
170(3)
Split-plot model with nesting
173(3)
Three-factor split-plot model (iv)
176(3)
Repeated-measures designs
179(50)
One-factor repeated-measures model
187(3)
Two-factor repeated-measures model
190(5)
Two-factor model with repeated measures on one cross factor
195(5)
Three-factor model with repeated measures on nested cross factors
200(5)
Three-factor model with repeated measures on two cross factors
205(9)
Nested model with repeated measures on a cross factor
214(6)
Three-factor model with repeated measures on one factor
220(9)
Unreplicated designs
229(8)
Two-factor cross factored unreplicated model
230(2)
Three-factor cross factored unreplicated model
232(5)
Further Topics
237(11)
Balanced and unbalanced designs
237(5)
Restricted and unrestricted mixed models
242(2)
Magnitude of effect
244(1)
A priori planned contrasts and post hoc unplanned comparisons
245(3)
Choosing experimental designs
248(10)
Statistical power
248(2)
Evaluating alternative designs
250(8)
How to request models in a statistics package
258(2)
Hest practice in presentation of the design
260(4)
Troubleshooting problems during analysis
264(7)
Glossary 271(10)
References 281(3)
Index of all ANOVA models with up to three factors 284(2)
Index 286(2)
Categories of model 288


C. PATRICK DONCASTER is a Reader in Ecology in the School of Biological Sciences at the University of Southampton.