Randomization, Bootstrap and Monte Carlo Methods in Biology e-bog
403,64 DKK
(inkl. moms 504,55 DKK)
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, boots...
E-bog
403,64 DKK
Forlag
Chapman and Hall/CRC
Udgivet
21 juli 2020
Længde
338 sider
Genrer
Probability and statistics
Sprog
English
Format
epub
Beskyttelse
LCP
ISBN
9781000080544
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.FeaturesPresents an overview of computer-intensive statistical methods and applications in biologyCovers a wide range of methods including bootstrap, Monte Carlo, ANOVA, regression, and Bayesian methodsMakes it easy for biologists, researchers, and students to understand the methods usedProvides information about computer programs and packages to implement calculations, particularly using R codeIncludes a large number of real examples from a range of biological disciplinesWritten 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.