Statistical Methods in Biology

Written in simple language with relevant examples, Statistical Methods in Biology: Design and Analysis of Experiments and Regression is a practical and illustrative guide to the design of experiments and data analysis in the biological and agricultural sciences. The book presents statistical ideas in the context of biological and agricultural sciences to which they are being applied, drawing on relevant examples from the authors’ experience.

Taking a practical and intuitive approach, the book only uses mathematical formulae to formalize the methods where necessary and appropriate. The text features extended discussions of examples that include real data sets arising from research. The authors analyze data in detail to illustrate the use of basic formulae for simple examples while using the GenStat® statistical package for more complex examples. Each chapter offers instructions on how to obtain the example analyses in GenStat and R.

By the time you reach the end of the book (and online material) you will have gained:

 

 

A clear appreciation of the importance of a statistical approach to the design of your experiments, A sound understanding of the statistical methods used to analyse data obtained from designed experiments and of the regression approaches used to construct simple models to describe the observed response as a function of explanatory variables, Sufficient knowledge of how to use one or more statistical packages to analyse data using the approaches described, and most importantly, An appreciation of how to interpret the results of these statistical analyses in the context of the biological or agricultural science within which you are working.

The book concludes with a guide to practical design and data analysis. It gives you the understanding to better interact with consultant statisticians and to identify statistical approaches to add value to your scientific research.

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