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Randomization, Bootstrap and Monte Carlo Methods in Biology

Download or Read eBook Randomization, Bootstrap and Monte Carlo Methods in Biology PDF written by Bryan F.J. Manly and published by CRC Press. This book was released on 2020-07-22 with total page 338 pages. Available in PDF, EPUB and Kindle.
Randomization, Bootstrap and Monte Carlo Methods in Biology
Author :
Publisher : CRC Press
Total Pages : 338
Release :
ISBN-10 : 9781000080506
ISBN-13 : 1000080501
Rating : 4/5 (06 Downloads)

Book Synopsis Randomization, Bootstrap and Monte Carlo Methods in Biology by : Bryan F.J. Manly

Book excerpt: Modern computer-intensive statistical methods play a key role in solving many problems across a wide range of scientific disciplines. Like its bestselling predecessors, the fourth edition of Randomization, Bootstrap and Monte Carlo Methods in Biology illustrates a large number of statistical methods with an emphasis on biological applications. The focus is now on the use of randomization, bootstrapping, and Monte Carlo methods in constructing confidence intervals and doing tests of significance. The text provides comprehensive coverage of computer-intensive applications, with data sets available online. Features Presents an overview of computer-intensive statistical methods and applications in biology Covers a wide range of methods including bootstrap, Monte Carlo, ANOVA, regression, and Bayesian methods Makes it easy for biologists, researchers, and students to understand the methods used Provides information about computer programs and packages to implement calculations, particularly using R code Includes a large number of real examples from a range of biological disciplines Written in an accessible style, with minimal coverage of theoretical details, this book provides an excellent introduction to computer-intensive statistical methods for biological researchers. It can be used as a course text for graduate students, as well as a reference for researchers from a range of disciplines. The detailed, worked examples of real applications will enable practitioners to apply the methods to their own biological data.


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