ABSTRACT

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.

chapter 1|14 pages

Randomization

chapter 2|32 pages

The Bootstrap

chapter 3|8 pages

Monte Carlo Methods

chapter 4|12 pages

Some General Considerations

chapter 5|22 pages

One- and Two-Sample Tests

chapter 6|30 pages

Analysis of Variance

chapter 7|28 pages

Regression Analysis

chapter 8|32 pages

Distance Matrices and Spatial Data

chapter 9|18 pages

Other Analyses on Spatial Data

chapter 10|32 pages

Time Series

chapter 11|14 pages

Survival and Growth Data

chapter 12|28 pages

Non-Standard Situations

chapter 13|8 pages

Bayesian Methods

chapter 14|4 pages

Conclusion and Final Comments