All students and researchers in environmental and biological sciences require statistical methods at some stage of their work. Many have a preconception that statistics are difficult and unpleasant and find that the textbooks available are difficult to understand. Practical Statistics for Environmental and Biological Scientists provides a concise, user-friendly, non-technical introduction to statistics. The book covers planning and designing an experiment, how to analyse and present data, and the limitations and assumptions of each statistical method. The text does not refer to a specific computer package but descriptions of how to carry out the tests and interpret the results are based on the approaches used by most of the commonly used packages, e.g. Excel, MINITAB and SPSS. Formulae are kept to a minimum and relevant examples are included throughout the text.

Provides an excellent introductory text for students on the principles and methods of statistical analysis in the life sciences, helping them choose and analyse statistical tests for their own problems and present their findings. An understanding of statistical principles and methods is essential for any scientist but is particularly important for those in the life sciences. The field biologist faces very particular problems and challenges with statistics as "real-life" situations such as collecting insects with a sweep net or counting seagulls on a cliff face can hardly be expected to be as reliable or controllable as a laboratory-based experiment. Acknowledging the peculiarites of field-based data and its interpretation, this book provides a superb introduction to statistical analysis helping students relate to their particular and often diverse data with confidence and ease. To enhance the usefulness of this book, the new edition incorporates the more advanced method of multivariate analysis, introducing the nature of multivariate problems and describing the the techniques of principal components analysis, cluster analysis and discriminant analysis which are all applied to biological examples. An appendix detailing the statistical computing packages available has also been included. It will be extremely useful to undergraduates studying ecology, biology, and earth and environmental sciences and of interest to postgraduates who are not familiar with the application of multiavirate techniques and practising field biologists working in these areas.

Practical Statistics for Environmental and Biological Scientists is a concise, user-friendly, non-technical introduction to statistics. Starting from basics, this book carefully introduces those statistical methods and techniques that all students and researchers need to know. Written in an accessible style, the book divides into two parts. The first part covers statistical principles, how to plan and design experiments and surveys, and the presentation of data. The second part introduces a range of statistical tests and methods commonly used in environmental and biological sciences. The limitations and assumptions of each statistical method are clearly described along with numerous relevant examples for the applications of the techniques. Practical Statistics for Environmental and Biological Scientists is an accessible introduction to key statistical techniques used in the environmental and biological sciences; includes relevant examples throughout the text with references for further reading; illustrates concepts and methods and the presentation of data through numerous tables and figures; and provides an appendix describing how many of the tests can be carried out using Excel and Minitab. Written for undergraduate students studying within the environmental and biological sciences. Researchers and professionals will also find this an invaluable reference.

Better experimental design and statistical analysis make for more robust science. A thorough understanding of modern statistical methods can mean the difference between discovering and missing crucial results and conclusions in your research, and can shape the course of your entire research career. With Applied Statistics, Barry Glaz and Kathleen M. Yeater have worked with a team of expert authors to create a comprehensive text for graduate students and practicing scientists in the agricultural, biological, and environmental sciences. The contributors cover fundamental concepts and methodologies of experimental design and analysis, and also delve into advanced statistical topics, all explored by analyzing real agronomic data with practical and creative approaches using available software tools. IN PRESS! This book is being published according to the “Just Published” model, with more chapters to be published online as they are completed.

Analytical chemists must use a range of statistical tools in their treatment of experimental data to obtain reliable results. Practical Statistics for the Analytical Scientist is a manual designed to help them negotiate the daunting specialist terminology and symbols. Prepared in conjunction with the Department of Trade and Industry's Valid Analytical Measurement (VAM) programme, this volume covers the basic statistics needed in the laboratory. It describes the statistical procedures that are most likely to be required including summary and descriptive statistics, calibration, outlier testing, analysis of variance and basic quality control procedures. To improve understanding, many examples provide the user with material for consolidation and practice. The fully worked answers are given both to check the correct application of the procedures and to provide a template for future problems. Practical Statistics for the Analytical Scientist will be welcomed by practising analytical chemists as an important reference for day to day statistics in analytical chemistry.

This book provides a practical introduction to analyzing ecological data using real data sets. The first part gives a largely non-mathematical introduction to data exploration, univariate methods (including GAM and mixed modeling techniques), multivariate analysis, time series analysis, and spatial statistics. The second part provides 17 case studies. The case studies include topics ranging from terrestrial ecology to marine biology and can be used as a template for a reader’s own data analysis. Data from all case studies are available from www.highstat.com. Guidance on software is provided in the book.