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.
Bryan F.J. Manly is an international expert on the analysis of data from environmental and ecological studies and also data from studies in other subject areas. He is the author of seven books on statistical methods, and is one of the two Chief Editors of the international journal, Environmental and Ecological Statistics.
Jorge A. Navarro Alberto is in the Department of Tropical Ecology at the Autonomous University of Yucatan, Mexico, with research interests in ecological and environmental statistics and computer-intensive methods. In particular, he has contributed to the development of randomization algorithms for the analysis of ecological data. He has more than thirty years of experience teaching statistics for biologists, marine biologists, and natural resource managers in Mexico, and also as a visiting professor at the Department of Mathematics and Statistics in the University of Wyoming.
