Sequential Data in Biological Experiments: An introduction for research workers by Ellis A. RobertsSequential Data in Biological Experiments: An introduction for research workers by Ellis A. Roberts

Sequential Data in Biological Experiments: An introduction for research workers

byEllis A. Roberts

Paperback | October 3, 2013

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There are many excellent books on general statistical methods in agricul­ tural and biological research. These books cover a broad range of methods without going into detail on specialized topics. A number of topics including regression analysis, design of experiments, biological assay and categorical analysis have received in-depth treatment in specialized texts. Little appears in standard textbooks on experiments in which observations form sequences. The live weights of animals during a long-term experiment provide a familiar example of data forming a sequence, but many others occur: for example, moisture content of segments of soil cores, successive counts of insects in an orchard and hormone levels in blood over a period. Correla­ tions are likely to be found among the observations in all these examples. The book by Goldstein (1979) provided the first systematic coverage of the principles involved in longitudinal studies, but is mainly concerned with observational studies on humans. The main aims of this book are to provide research workers with methods of analysing data from comparative experiments with sequential obser­ vations and to demonstrate special features of the design of such experi­ ments. These aims are achieved by working through sets of data.
Title:Sequential Data in Biological Experiments: An introduction for research workersFormat:PaperbackDimensions:240 pages, 23.5 × 15.5 × 0.02 inPublished:October 3, 2013Publisher:Springer-Verlag/Sci-Tech/TradeLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:9401053812

ISBN - 13:9789401053815

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Table of Contents

1 Introduction.- 2 A simple factorial grazing experiment measured on 27 occasions.- 3 A 3 × 3 factorial with quantitative levels.- 4 Definable within-individual comparisons.- 5 Covariance.- 6 Pre-treatment observations in the design of experiments.- 7 Weighted regression, goodness-of-fit and related topics.- 8 Environmental variables.- 9 Correlation between series of random variables.- 10 Response (reaction) times.- Bibliographical note.