Applying statistical concepts to biological scenarios, this established textbook continues to be the go-to tool for advanced undergraduates and postgraduates studying biostatistics or experimental design in biology-related areas. Chapters cover linear models, common regression and ANOVA methods, mixed effects models, model selection, and multivariate methods used by biologists, requiring only introductory statistics and basic mathematics. Demystifying statistical concepts with clear, jargon-free explanations, this new edition takes a holistic approach to help students understand the relationship between statistics and experimental design. Each chapter contains further-reading recommendations, and worked examples from today's biological literature. All examples reflect modern settings, methodology and equipment, representing a wide range of biological research areas. These are supported by hands-on online resources including real-world data sets, full R code to help repeat analyses for all worked examples, and additional review questions and exercises for each chapter.
Rezensionen / Stimmen
'The new edition of this 'go-to' text, has been revised to take a more holistic, informative, and up-to-date approach to the subject. This remarkably accessible, practical and - at times - entertaining guide to how we can best translate our questions and ideas into informative experiments and analyses is highly recommended for everyone wanting to investigate, visualise, and analyse biological phenomena.' Jonathon Havenhand, University of Gothenburg 'The new book is an excellent resource for researchers, analysts and teachers. The text clearly outlines the important concepts of the tests and models. The examples are all based on published data which makes it easy to source the full manuscript, datasets and replicate the analyses which is incredibly important and assists with interpretation.' Victoria Goodall, VLG Statistical Services 'At last, a book for undergraduates and graduates that distills the complexities of biological data analysis into an easy-to-understand generalized linear model approach - with examples in R! The authors challenge the reader to think critically about data by providing important details on design, summary statistics, power analysis/effect size, model fit, and data visualization. This book will be used in my classes for years to come.' Greg Moyer, Mansfield University 'I have been using Experimental Design and Data Analysis for teaching and research for 20 years and have been hoping for a second edition for 10. It was worth the wait. The new edition shares many attributes with the original. It is quite easy to read, the examples are varied and interesting, there are ample and revealing box examples and there remains an attitude of Statistics as a tool that will be used by many. There are a number of changes that reflect these attributes - two key ones are the emphasis on Generalized Models as a framework for a broad array of approaches and the conversion of examples to R, with code and additional material available online.' Peter Raimondi, University of California, Santa Cruz 'I have taught in and coordinated a third-year design/statistics paper for zoology and ecology students for ten years - an enjoyable and sometimes challenging task. The first edition of Quinn and Keough has been immensely helpful for me in teaching this course. The second edition has been updated and expanded considerably. I'm sure it will continue to inspire my future teaching.' Christoph Matthaei, University of Otago 'I was excited to see Quinn and Keough have updated their classic guide to experimental design and data analysis. I read the earlier edition of this book as a graduate student, and the advice it provides on experimental design is the foundation of my own studies, as well as my approach to training graduate students ... This book is foundational reading for aspiring scientists. Not only does it teach you how to analyse your data, it also provides invaluable advice on how to communicate analyses and write up scientific studies. The book's advice will help give early career scientists the confidence they need to write-up and publish their first studies.' Chris Brown, University of Tasmania 'I love how one of my favorite stats books has been updated after so long! I always considered the original a must-have for the bookshelf of anyone who uses statistics, and this one seems to improve on the original greatly.' Drew Talley, University of San Diego
Auflage
Sprache
Verlagsort
Editions-Typ
Produkt-Hinweis
Illustrationen
140 Halftones, black and white
Maße
Höhe: 252 mm
Breite: 201 mm
Dicke: 19 mm
Gewicht
ISBN-13
978-1-107-68767-7 (9781107687677)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
Gerry Quinn is an Honorary Professor in the School of Life and Environmental Sciences at Deakin University, having served as Chair in Marine Biology and Head of Warrnambool Campus during his academic career. He has extensive experience in teaching biostatistics at Deakin University and the University of Gothenburg. Michael J. Keough is an ecologist, environmental scientist, and honorary Professor in the School of Biosciences at University of Melbourne.
Autor*in
Deakin University, Victoria
University of Melbourne
Contents: List of Acronyms; Preface; 1. Introduction; 2. Things to Know Before Proceeding; 3. Sampling and Experimental Design; 4. Introduction to Linear Models; 5. Exploratory Data Analysis; 6. Simple Linear Models with One Predictor; 7. Linear Models for Crossed (Factorial) Designs; 8. Multiple Regression Models; 9. Predictor Importance and Model Selection in Multiple Regression Models; 10. Random Factors in Factorial and Nested Designs; 11. Split-plot (Split-unit) Designs: Partly Nested Models; 12. Repeated Measures Designs; 13. Generalized Linear Models for Categorical Responses; 14. Introduction to Multivariate Analyses; 15. Multivariate Analyses Based on Eigenanalyses; 16. Multivariate Analyses Based on (dis)similarities or Distances; 17. Telling Stories with Data; References; Glossary; Index.
