Description
Book SynopsisModern 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
Trade Review
"...This book deals with statistical data simulations in biology...It should be noted that the presentation of the book contains a lot of explanations and justifications that are not limited by mathematical formula. Thus, a biologist can easily understand the basic idea and approach of any statistical method discussed in the book...The book...is very well structured; the presentation of the material is clear and consistent. There are many illustrative examples and exercises. I enjoyed reading this book, and it is clearly included in the list of books that I highly recommend for study in the training of specialists in the field of biostatistics."
- Taras Lukashiv, ISCB News, June 2021
Table of Contents
1.Randomization
2.The Bootstrap
3.Monte Carlo Methods
4.Some General Considerations
5.One- and Two-Sample Tests
6.Analysis of Variance
7.Regression Analysis
8.Distance Matrices and Spatial Data
9.Other Analyses on Spatial Data
10.Time Series
11.Survival and Growth Data
12.Non-Standard Situations
13.Bayesian Methods
14.Conclusion and Final Comments
15.Appendix: Software for Computer-Intensive Statistics
