Statistical Methods in Biology

Suzanne Jane Welham obtained an MSc in statistical sciences from University College London in 1987 and worked as an applied statistician at Rothamsted Research from 1987 to 2000, collaborating with scientists and developing statistical software. She pursued a PhD from 2000 to 2003 at the London School of Hygiene and Tropical Medicine and then returned to Rothamsted, during which time she coauthored the in-house statistics courses that motivated the writing of this book. She is a coauthor of about 60 published papers and currently works for VSN International Ltd on the development of statistical software for analysis of linear mixed models and presents training courses on their use in R and GenStat. Salvador Alejandro Gezan, PhD, is an assistant professor at the School of Forest Resources and Conservation at the University of Florida since 2011. Salvador obtained his bachelor’s from the Universidad of Chile in forestry and his PhD from the University of Florida in statistics-genetics. He then worked as an applied statistician at Rothamsted Research, collaborating on the production and development of the in-house courses that formed the basis for this book. Currently, he teaches courses in linear and mixed model effects, quantitative genetics and forest mensuration. He carries out research and consulting in statistical application to biological sciences with emphasis on genetic improvement of plants and animals. Salvador is a long-time user of SAS, which he combines with GenStat, R and MATLAB as required. Suzanne Jane Clark has worked at Rothamsted Research as an applied statistician since 1981. She primarily collaborates with ecologists and entomologists at Rothamsted, providing and implementing advice on statistical issues ranging from planning and design of experiments through to data analysis and presentation of results, and has coauthored over 130 scientific papers. Suzanne coauthored and presents several of the in-house statistics courses for scientists and research students, which inspired the writing of this book. An experienced and long-term GenStat user, Suzanne has also written several procedures for the GenStat Procedure Library and uses GenStat daily for the analyses of biological data using a wide range of statistical techniques, including those covered in this book. Andrew Mead obtained a BSc in statistics at the University of Bath and an MSc in biometry at the University of Reading, where he spent over 16 years working as a consultant and research biometrician at the Institute of Horticultural Research and Horticulture Research International at Wellesbourne, Warwickshire, UK. During this time, he developed and taught a series of statistics training courses for staff and students at the institute, producing some of the material on which this book is based. For 10 years from 2004 he worked as a research biometrician and teaching fellow at the University of Warwick, developing and leading the teaching of statistics for both postgraduate and undergraduate students across a range of life sciences. In 2014 he was appointed as Head of Applied Statistics at Rothamsted Research. Throughout his career he has had a strong association with the International Biometric Society, serving as International President and Vice President from 2007 to 2010 inclusive, having been the first recipient of the ‘Award for Outstanding Contribution to the Development of the International Biometric Society’ in 2006, serving as a Regional Secretary of the British and Irish Region from 2000 to 2007 and on the International Council from 2002 to 2010. He is a (co)author of over 80 papers, and coauthor of Statistical Principles for the Design of Experiments: Applications to Real Experiments published in 2012.

Introduction. A Review of Basic Statistics. Principles for Designing Experiments. Models for a Single Factor. Checking Model Assumptions. Transformations of the Response. Models with Simple Blocking Structure. Extracting Information about Treatments. Models with Complex Blocking Structure. Replication and Power. Dealing with Non-Orthogonality. Models for a Single Variate: Simple Linear Regression. Checking Model Fit. Models for Several Variates: Multiple Linear Regression. Models for Variates and Factors. Incorporating Structure: Mixed Models. Models for Curved Relationships. Models for Non-Normal Responses: Generalized Linear Models. Practical Design and Data Analysis for Real Studies. References. Appendices.