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Sinopsis
This second edition textbook teaches modern methods of statistics through the use of fascinating biological and medical case studies. The clear and engaging writing and practical perspective allows students to understand the analytical process behind biological data. Through the use of real world biological examples, biologists and health professionals can learn statistics in an essential manner.
Authors Whitlock and Schulter have over 40 years’ experience between the two of them and therefore able to understand that students learn best through interesting examples and not overcomplicated formulas. This edition includes several unusual features that they have discovered to be helpful for effectively reaching their readers.
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Michael Whitlock is an evolutionary biologist and population geneticist. He is a professor of zoology at the University of British Columbia, where he has taught statistics to biology students since 1995. Whitlock is known for his work on the spatial structure of biological populations, genetic drift, and the genetics of adaptation. He has worked with fungus beetles, rhinos, and fruit flies; mathematical theory; and statistical genetics. He is a fellow of the American Academy of Arts and Sciences and a fellow of the American Association for the Advancement of Science. He is also the former editor-in-chief of The American Naturalist.
Dolph Schluter is professor and Canada Research Chair in the zoology department and Biodiversity Research Center at the University of British Columbia. He currently teaches evolutionary ecology as well as a graduate course in biological data analysis. He is a fellow of the Royal Society of London and the Royal Society of Canada and is a Foreign Member of the American Academy of Arts and Sciences. He has received multiple honors including the Guggenheim Fellowship, The Sewall Wright Award from the American Society of Naturalists, the Killam Fellowship from the Canada Council, and the Darwin-Wallace Medal from the Linnean Society of London.
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The Analysis of Biological Dat
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