Experimental Design and Data Analysis for Biologists

A biostatistics textbook for upper undergraduate and graduate students, covering analyses used by biologists and now including R code.

Gerry P. Quinn (Author), Michael J. Keough (Author)

9781107687677, Cambridge University Press

Paperback / softback, published 7 September 2023

405 pages, 140 b/w illus.
27 x 18 x 2.6 cm, 0.67 kg

'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

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.

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.

Subject Areas: Maths for scientists [PDE]