Multivariate Statistical Methods: A Primer
offers an introduction to multivariate statistical methods in a rigorous yet intuitive way, without an excess of mathematical details. In this fifth edition, all chapters have been revised and updated, with clearer and more direct language than in previous editions, and with more up-to-date examples, exercises, and references, in areas as diverse as biology, environmental sciences, economics, social medicine, and politics.
Features
• A concise and accessible conceptual approach that requires minimal mathematical background.
• Suitable for a wide range of applied statisticians and professionals from the natural and social sciences.
• Presents all the key topics for a multivariate statistics course.
• The R code in the appendices has been updated, and there is a new appendix introducing programming basics for R.
• The data from examples and exercises are available on a companion website.
This book continues to be a great starting point for readers looking to become proficient in multivariate statistical methods, but who might not be deeply versed in the language of mathematics. In this edition, we provide readers with conceptual introductions to methods, practical suggestions, new references, and a more extensive collection of R functions and code that will help them to deepen their toolkit of multivariate statistical methods.
Bryan F. J. Manly, PhD, was born in London, UK on May 27, 1944, and he is practically retired from academic work. His areas of interest are in statistical ecology, environmental statistics, computer intensive statistics, and general applied statistics. He is the author of over two hundred papers and seven books that have been both fundamental statistical research, and applications to several related disciplines. Bryan's academic career began in 1966 as a statistician and one of the first computer programmers, at the British multinational manufacturer Fisons, marking the start of a brilliant career as a researcher and statistical consultant in several countries around the world: University of Salford (UK), University of Papua New Guinea, University of Otago (New Zealand), Louisiana State University, University of Wyoming, and WEST, Inc. (USA). Among other distinctions, he is an Elected Fellow of the Royal Society of New Zealand, and he was awarded as Distinguished Statistical Ecologist in the International Ecology Congress, held in Manchester, 1994. Bryan is an excellent connoisseur of home brewing and homemade wine; everybody praises his good hand in making peerless wine!
Jorge A. Navarro Alberto, PhD, is a professor emeritus at the Autonomous University of Yucatán, México, where he specialized in ecological and environmental statistics research. Dr. Navarro Alberto earned his PhD degree in Statistics at the University of Otago, New Zealand. His academic career spanned more than 36 years teaching statistics for biologists, marine biologists, and natural resource managers in Mexico, and as a visiting professor at the University of Wyoming, with a vast experience in teaching multivariate analysis courses for life scientists. He is the co-author of the last edition of the book Randomization, Bootstrap and Monte Carlo Methods in Biology, and the co-editor of Introduction to Ecological Sampling, published by CRC Press. After retirement, Jorge is still active in the professional and academic arenas, working as a (more relaxed) part-time statistical consultant, and as one of the associate editors of the international journal, Environmental and Ecological Statistics. He also member of the Mexican representation at the International Statistical Literacy Project, Finland.
Ken Gerow, PhD, recently retired from the University of Wyoming, where, as a professor of statistics for over thirty years, he taught statistics to quantitative scientists from many disciplines. Dr. Gerow earned his PhD degree in Statistics at Cornell University. He is the author or a coauthor of over ninety research articles, books, and book chapters, in topics ranging from the molecular and cellular world to the visible world around us (plant, animal, and human systems). Ken considers himself to be a parasitic biologist because he only publishes with other people's data.
