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Analysis of Variance and Covariance How to Choose and Construct Models for the Life Sciences 1st Edition by C Patrick Doncaster, Andrew JH Davey ISBN 052186562X 9780521865623

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Analysis of Variance and Covariance How to Choose and Construct Models for the Life Sciences 1st Edition C. Patrick Doncaster Digital Instant Download

Author(s): C. Patrick Doncaster, Andrew J. H. Davey
ISBN(s): 9780521865623, 052186562X
Edition: 1
File Details: PDF, 1.74 MB
Year: 2007
Language: english
SKU: EB-2013932 Category: Tags: ,

Analysis of Variance and Covariance How to Choose and Construct Models for the Life Sciences 1st Edition by C Patrick Doncaster, Andrew JH Davey – Ebook PDF Instant Download/Delivery: 052186562X, 9780521865623
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Product details:

ISBN 10: 052186562X 
ISBN 13: 9780521865623
Author: C Patrick Doncaster, Andrew JH Davey

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.

Analysis of Variance and Covariance How to Choose and Construct Models for the Life Sciences 1st Table of contents:

1 One-factor designs
1.1 One-factor model

2 Nested designs
2.1 Two-factor nested model
2.2 Three-factor nested model

3 Fully replicated factorial designs
3.1 Two-factor fully cross-factored model
3.2 Three-factor fully cross-factored model
3.3 Cross-factored with nesting model
3.4 Nested cross-factored model

4 Randomised-block designs
4.1 One-factor randomised-block model
4.2 Two-factor randomised-block model
4.3 Three-factor randomised-block model

5 Split-plot designs
5.1 Two-factor split-plot model (i)
5.2 Three-factor split-plot model (i)
5.3 Three-factor split-plot model (ii)
5.4 Split-split-plot model (i)
5.5 Split-split-plot model (ii)
5.6 Two-factor split-plot model (ii)
5.7 Three-factor split-plot model (iii)
5.8 Split-plot model with nesting
5.9 Three-factor split-plot model (iv)

6 Repeated-measures designs
6.1 One-factor repeated-measures model
6.2 Two-factor repeated-measures model
6.3 Two-factor model with repeated measures on one cross factor
6.4 Three-factor model with repeated measures on nested cross factors
6.5 Three-factor model with repeated measures on two cross factors
6.6 Nested model with repeated measures on a cross factor
6.7 Three-factor model with repeated measures on one factor

7 Unreplicated designs
7.1 Two-factor cross-factored unreplicated model
7.2 Three-factor cross-factored unreplicated model

Further topics
Balanced and unbalanced designs
Sums of squares in non-orthogonal factorial models
Restricted and unrestricted mixed models
What are restricted and unrestricted models?
Magnitude of effect
A priori planned contrasts and post hoc unplanned comparisons
Choosing experimental designs
Statistical power
Evaluating alternative designs
How to request models in a statistics package
Best practice in presentation of the design
Troubleshooting problems during analysis

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Tags: C Patrick Doncaster, Andrew JH Davey, Variance