Randomization, Bootstrap and Monte Carlo Methods in Biology (Chapman & Hall/CRC Texts in Statistical Science)

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 Biologyillustrates 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. It provides comprehensive coverage of computer-intensive applications, with datasets available online. Features <ul> <li>Presents an overview of computer-intensive statistical methods and applications in biology</li> <li>Covers a wide range of methods including bootstrap, Monte Carlo, ANOVA, regression and Bayesian methods</li> <li>Makes it easy for biologists, researchers, and students to understand the methods used</li> <li>Provides information about computer programs and packages to implement calculations, particularly using R code</li> <li>Includes a large number of real examples from a range of biological disciplines</li> </ul> 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.

Science & Math

Manly, Bryan F.J.

Navarro Alberto, Jorge A.

Mathematics

Routledge

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.

Chapman and Hall/CRC

This book provides a review of methods for obtaining and analysing data from stage-structured biological populations. The topics covered are sam pling designs (Chapter 2), the estimation of parameters by maximum likelihood (Chapter 3), the analysis of sample counts of the numbers cif individuals in different stages at different times (Chapters 4 and 5), the analysis of data using Leslie matrix types of model (Chapter 6) and key factor analysis (Chapter 7). There is also some discussion of the approaches to modelling and estimation that have been used in five studies of particular populations (Chapter 8). There is a large literature on the modelling of biological populations, and a multitude of different approaches have been used in this area. The various approaches can be classified in different ways (Southwood, 1978, ch. 12), but for the purposes of this book it is convenient to think of the three categories mathematical, statistical and predictive modelling. Mathematical modelling is concerned largely with developing models that capture the most important qualitative features of population dynamics. In this case, the models that are developed do not have to be compared with data from natural populations. As representations of idealized systems, they can be quite informative in showing the effects of changing parameters, indicating what factors are most important in promoting stability, and so on.

Stage-Structured Populations: Sampling, analysis and simulation (Molecular Biology Intelligence Unit)

Politics & Social Sciences

Social Sciences

Chapman & Hall

This book aims to illustrate the value of modern computer intensive methods for a wide range of statistical problems. There is coverage of randomization methods of inference that were first proposed by R.A. Fisher in 1935, but are only now becoming practical because of the ready availability of computer power. Applications to analysis of variance and regression multivariate analysis are covered. Monte Carlo methods, jacknifing and bootstrapping are also discussed as being alternatives to randomization in some circumstances. The examples used have been chosen to attract interest and include the controversial topics of whether there is periodicity in fossil extinction records and whether there is evidence for a relationship between species co-occurences on islands. The first chapter introduces the ideas of randomization methods through some examples and the second introduces Monte Carlo tests and also the computer intensive techniques of jacknifing and bootstrapping since these are sometimes used as alternatives to randomization. The following chapters discuss some general theoretical matters concerning randomization and Monte Carlo methods and the analysis of various standard types of data. The final chapter gives some examples of non-standard situations where computer intensive methods seem to provide the best method for statistical analysis. In all chapters the emphasis is on applications in biology.

Randomization and Monte Carlo Method

The purpose of this book is to introduce multivariate statistical methods to non-mathematicians. It is not intended to be comprehensive. Rather, the intention is to keep the details to a minimum while still conveying a good idea of what can be done. In other words, it is a book to 'get you going' in a particular area of statistical methods. This second edition has retained all of Professor Manly's crystal clear style. It is based on a course that has been taught successfully at the University of Otago for a number of years but has increased coverage on measuring distances between cases based on presence-absence data, a new selection on logistic regression, new exercises and two completely new chapters on graphical methods and ordination. The author has taken into account the major shift in the way in which computer software is used, but the emphasis is on the underlying principles rather than the use of particular programs.

Multivariate Statistical Methods: A Primer, Second Edition

Randomization, Bootstrap and Monte Carlo Methods in Biology, Second Edition features new material on on bootstrap confidence intervals and significance testing, and incorporates new developments on the treatments of randomization methods for regression and analysis variation, including descriptions of applications of these methods in spreadsheet programs such as Lotus and other commercial packages. This second edition illustrates the value of modern computer intensive methods in the solution of a wide range of problems, with particular emphasis on biological applications. Examples given in the text include the controversial topic of whether there is periodicity between co-occurrences of species on islands.

Randomization, Bootstrap and Monte Carlo Methods in Biology, Second Edition

Biological Sciences

This book provides students and research workers in the biological, medical and social sciences with the statistical background needed to collect and analyse data in an intelligent and critical manner. Key examples and case studies are used to illustrate commonly encountered research problems and to explain how they may be solved or even avoided altogether. Professor Manly also presents a clear understanding of the opportunities and limitations of different research designs, as well as an introduction to some new methods of analysis that are proving increasingly popular. Topics covered include the differences between observational and experimental studies; the design of sample surveys; multiple regression and its generalizations to log-linear and logistic models; experimental and quasi-experimental designs; interrupted time series; computer intensive methods of statistical inference; and the ethical considerations of research. In the final chapter, there is a useful discussion of how the various components of a research study (including deciding on the objectives, planning, designing, and the collection and analysis of data) come together. This practical and well-structured book will be essential reading for graduate students and researchers in a wide range of disciplines, including biology, anthropology, medicine and the social sciences.

The Design and Analysis of Research Studies

Cambridge University Press

This book provides research workers with the statistical background needed in order to collect and analyze data in an intelligent and critical manner. Key examples and case studies are used to illustrate commonly encountered research problems and to explain how they may be solved or even avoided altogether. Professor Manly also presents a clear understanding of the opportunities and limitations of different research designs, as well as an introduction to some new methods of analysis that are proving increasingly popular. Topics covered include: the differences between observational and experimental studies, the design of sample surveys, multiple regression, interrupted time series, computer intensive statistics, and the ethical considerations of research. In the final chapter, there is a discussion of how the various components of a research study come together.

This book provides research workers with the statistical background needed in order to collect and analyze data in an intelligent and critical manner. Key examples and case studies are used to illustrate commonly encountered research problems and to explain how they may be solved or even avoided altogether. Professor Manly also presents a clear understanding of the opportunities and limitations of different research designs, as well as an introduction to some new methods of analysis that are proving increasingly popular. Topics covered include: the differences between observational and experimental studies, the design of sample surveys, multiple regression, interrupted time series, computer intensive statistics, and the ethical considerations of research. In the final chapter, there is a discussion of how the various components of a research study come together.

Multivariate Statistical Methods

Gerow, Ken

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Books by Manly, Bryan F.J.

See the best price to sell, buy, or rent books by Manly, Bryan F.J.. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Randomization, Bootstrap and Monte Carlo Methods in Biology (Chapman & Hall/CRC Texts in Statistical Science)

Randomization, Bootstrap and Monte Carlo Methods in Biology (Chapman & Hall/CRC Texts in Statistical Science)

Stage-Structured Populations: Sampling, analysis and simulation (Molecular Biology Intelligence Unit)

Randomization and Monte Carlo Method

Multivariate Statistical Methods: A Primer, Second Edition

Randomization, Bootstrap and Monte Carlo Methods in Biology, Second Edition

The Design and Analysis of Research Studies

The Design and Analysis of Research Studies

The Design and Analysis of Research Studies

Multivariate Statistical Methods