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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.
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.
Multivariate Statistical Methods: A Primer provides an introductory
overview of multivariate methods without getting too deep into the
mathematical details. This fourth edition is a revised and updated
version of this bestselling introductory textbook. It retains the
clear and concise style of the previous editions of the book and
focuses on examples from biological and environmental sciences. The
major update with this edition is that R code has been included for
each of the analyses described, although in practice any standard
statistical package can be used. The original idea with this book
still applies. This was to make it as short as possible and enable
readers to begin using multivariate methods in an intelligent
manner. With updated information on multivariate analyses, new
references, and R code included, this book continues to provide a
timely introduction to useful tools for multivariate statistical
analysis.
Multivariate Statistical Methods: A Primer provides an introductory
overview of multivariate methods without getting too deep into the
mathematical details. This fourth edition is a revised and updated
version of this bestselling introductory textbook. It retains the
clear and concise style of the previous editions of the book and
focuses on examples from biological and environmental sciences. The
major update with this edition is that R code has been included for
each of the analyses described, although in practice any standard
statistical package can be used. The original idea with this book
still applies. This was to make it as short as possible and enable
readers to begin using multivariate methods in an intelligent
manner. With updated information on multivariate analyses, new
references, and R code included, this book continues to provide a
timely introduction to useful tools for multivariate statistical
analysis.
An Easy-to-Understand Treatment of Ecological Sampling Methods and
Data Analysis Including only the necessary mathematical
derivations, Introduction to Ecological Sampling shows how to use
sampling procedures for ecological and environmental studies. It
incorporates both traditional sampling methods and recent
developments in environmental and ecological sampling methods.
After an introduction, the book presents standard sampling methods
and analyses. Subsequent chapters delve into specialized topics
written by well-known researchers. These chapters cover adaptive
sampling methods, line transect sampling, removal and
change-in-ratio methods, plotless sampling, mark-recapture sampling
of closed and open populations, occupancy models, sampling designs
for environmental modeling, and trend analysis. The book explains
the methods as simply as possible, keeping equations and their
derivations to a minimum. It provides references to important, more
advanced sampling methods and analyses. It also directs readers to
computer programs that can be used to perform the analyses.
Accessible to biologists, the text only assumes a basic knowledge
of statistical methods. It is suitable for an introductory course
on methods for collecting and analyzing ecological and
environmental data.
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