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Experimental design and data analysis for biologists /

"Requiring only introductory statistics and basic mathematics, this textbook avoids jargon and provides worked examples, data sets and R code, and review exercises. Designed for advanced undergraduates and postgraduates studying biostatistics and experiment design in biology-related fields, it...

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Bibliographic Details
Main Authors: Quinn, G. P. (Gerald Peter), 1956- (Author)
Group Author: Keough, Michael J.
Published: Cambridge University Press,
Publisher Address: Cambridge, United Kingdom :
Publication Dates: 2024.
Literature type: Book
Language: English
Edition: Second edition.
Subjects:
Biology > Experiments.
Biometry.
Summary: "Requiring only introductory statistics and basic mathematics, this textbook avoids jargon and provides worked examples, data sets and R code, and review exercises. Designed for advanced undergraduates and postgraduates studying biostatistics and experiment design in biology-related fields, it applies statistical concepts to biological scenarios"--
Item Description: Revised edition of: Experimental design and data analysis for biologists / Gerry P. Quinn, Michael J. Keough. 2002.
Carrier Form: xix, 387 pages : illustrations ; 26 cm
Bibliography: Includes bibliographical references and index.
ISBN: 9781107036710
1107036712
9781107687677
1107687675
Index Number: QH323
CLC: Q-332
Call Number: Q-332/Q74/2nd ed.
Contents: Introduction -- Things to know before proceeding -- Sampling and experimental design -- Introduction to linear models -- Exploratory data analysis -- Simple linear models with one predictor -- Linear models for crossed (factorial) designs -- Multiple regression models -- Predictor importance and model selection in multiple regression models -- Random factors in factorial and nested designs -- Split-plot (split-unit) designs: Partly nested models -- Repeated measures designs -- Generalized linear models for categorical responses -- Introduction to multivariate analyses -- Multivariate analyses based on Eigenanalyses -- Multivariate analyses based on (dis)similarities or distances -- Telling stories with data.

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