Statistical methods are a key tool for all scientists working with data, but learning the basics continues to challenge successive generations of students. This accessible textbook provides an up-to-date introduction to the classical techniques and modern extensions of linear model analysis-one of the most useful approaches for investigating scientific data in the life and environmental sciences. While some of the foundational analyses (e.g. t tests, regression, ANOVA) are as useful now as ever, best practice moves on and there are many new general developments that offer great potential. The book emphasizes an estimation-based approach that takes account of recent criticisms of over-use of probability values and introduces the alternative approach that uses information criteria.

This new edition includes the latest advances in R and related software and has been thoroughly “road-tested” over the last decade to create a proven textbook that teaches linear and generalized linear model analysis to students of ecology, evolution, and environmental studies (including worked analyses of data sets relevant to all three disciplines). While R is used throughout, the focus remains firmly on statistical analysis.

The New Statistics with R is suitable for senior undergraduate and graduate students, professional researchers, and practitioners in the fields of ecology, evolution and environmental studies.

About the Author

Andy Hector, Professor of Ecology, Department of Plant Sciences, University of Oxford, UK

Andy Hector is Professor of Ecology at the Department of Plant Sciences, Linacre College, University of Oxford, UK. He is Co-Director of the Plants for the Twenty-First Century Institute. He has convened and taught statistics on the Quantitative Methods for Biologists course for undergraduates. He is a community ecologist interested in biodiversity loss and its consequences for ecosystem functioning, stability and services and scientific PI of the Sabah Biodiversity Experiment. He has contributed to several publications on ecological analysis.

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