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This book contains a rich set of tools for nonparametric analyses,
and the purpose of this text is to provide guidance to students and
professional researchers on how R is used for nonparametric data
analysis in the biological sciences: To introduce when
nonparametric approaches to data analysis are appropriate To
introduce the leading nonparametric tests commonly used in
biostatistics and how R is used to generate appropriate statistics
for each test To introduce common figures typically associated with
nonparametric data analysis and how R is used to generate
appropriate figures in support of each data set The book focuses on
how R is used to distinguish between data that could be classified
as nonparametric as opposed to data that could be classified as
parametric, with both approaches to data classification covered
extensively. Following an introductory lesson on nonparametric
statistics for the biological sciences, the book is organized into
eight self-contained lessons on various analyses and tests using R
to broadly compare differences between data sets and statistical
approach.
This book introduces the open source R software language that can
be implemented in biostatistics for data organization, statistical
analysis, and graphical presentation. In the years since the
authors' 2014 work Introduction to Data Analysis and Graphical
Presentation in Biostatistics with R, the R user community has
grown exponentially and the R language has increased in maturity
and functionality. This updated volume expands upon skill-sets
useful for students and practitioners in the biological sciences by
describing how to work with data in an efficient manner, how to
engage in meaningful statistical analyses from multiple
perspectives, and how to generate high-quality graphics for
professional publication of their research. A common theme for
research in the diverse biological sciences is that decision-making
depends on the empirical use of data. Beginning with a focus on
data from a parametric perspective, the authors address topics such
as Student t-Tests for independent samples and matched pairs;
oneway and twoway analyses of variance; and correlation and linear
regression. The authors also demonstrate the importance of a
nonparametric perspective for quality assurance through chapters on
the Mann-Whitney U Test, Wilcoxon Matched-Pairs Signed-Ranks test,
Kruskal-Wallis H-Test for Oneway Analysis of Variance, and the
Friedman Twoway Analysis of Variance. To address the element of
data presentation, the book also provides an extensive review of
the many graphical functions available with R. There are now
perhaps more than 15,000 external packages available to the R
community. The authors place special emphasis on graphics using the
lattice package and the ggplot2 package, as well as less common,
but equally useful, figures such as bean plots, strip charts, and
violin plots. A robust package of supplementary material, as well
as an introduction of the development of both R and the discipline
of biostatistics, makes this ideal for novice learners as well as
more experienced practitioners.
This book introduces the open source R software language that can
be implemented in biostatistics for data organization, statistical
analysis, and graphical presentation. In the years since the
authors' 2014 work Introduction to Data Analysis and Graphical
Presentation in Biostatistics with R, the R user community has
grown exponentially and the R language has increased in maturity
and functionality. This updated volume expands upon skill-sets
useful for students and practitioners in the biological sciences by
describing how to work with data in an efficient manner, how to
engage in meaningful statistical analyses from multiple
perspectives, and how to generate high-quality graphics for
professional publication of their research. A common theme for
research in the diverse biological sciences is that decision-making
depends on the empirical use of data. Beginning with a focus on
data from a parametric perspective, the authors address topics such
as Student t-Tests for independent samples and matched pairs;
oneway and twoway analyses of variance; and correlation and linear
regression. The authors also demonstrate the importance of a
nonparametric perspective for quality assurance through chapters on
the Mann-Whitney U Test, Wilcoxon Matched-Pairs Signed-Ranks test,
Kruskal-Wallis H-Test for Oneway Analysis of Variance, and the
Friedman Twoway Analysis of Variance. To address the element of
data presentation, the book also provides an extensive review of
the many graphical functions available with R. There are now
perhaps more than 15,000 external packages available to the R
community. The authors place special emphasis on graphics using the
lattice package and the ggplot2 package, as well as less common,
but equally useful, figures such as bean plots, strip charts, and
violin plots. A robust package of supplementary material, as well
as an introduction of the development of both R and the discipline
of biostatistics, makes this ideal for novice learners as well as
more experienced practitioners.
This book contains a rich set of tools for nonparametric analyses,
and the purpose of this text is to provide guidance to students and
professional researchers on how R is used for nonparametric data
analysis in the biological sciences: To introduce when
nonparametric approaches to data analysis are appropriate To
introduce the leading nonparametric tests commonly used in
biostatistics and how R is used to generate appropriate statistics
for each test To introduce common figures typically associated with
nonparametric data analysis and how R is used to generate
appropriate figures in support of each data set The book focuses on
how R is used to distinguish between data that could be classified
as nonparametric as opposed to data that could be classified as
parametric, with both approaches to data classification covered
extensively. Following an introductory lesson on nonparametric
statistics for the biological sciences, the book is organized into
eight self-contained lessons on various analyses and tests using R
to broadly compare differences between data sets and statistical
approach.
Through real-world datasets, this book shows the reader how to work
with material in biostatistics using the open source software R.
These include tools that are critical to dealing with missing data,
which is a pressing scientific issue for those engaged in
biostatistics. Readers will be equipped to run analyses and make
graphical presentations based on the sample dataset and their own
data. The hands-on approach will benefit students and ensure the
accessibility of this book for readers with a basic understanding
of R. Topics include: an introduction to Biostatistics and R, data
exploration, descriptive statistics and measures of central
tendency, t-Test for independent samples, t-Test for matched pairs,
ANOVA, correlation and linear regression, and advice for future
work.
In statistics, analysis of variance (ANOVA) is a collection of
statistical models used to distinguish between an observed variance
in a particular variable and its component parts. In its simplest
form, ANOVA provides a statistical test of whether or not the means
of several groups are all equal, and therefore generalizes a test
between these groups. One test often used by statisticians and
researchers in their work is the Two-Way ANOVA, which determines
the differences--and possible interactions--when variables are
presented from the perspective of two or more categories. When a
Two-Way ANOVA is implemented, it enables one to compare and
contrast variables resulting from independent or joint actions.
This brief provides guidance on how R can be used to facilitate
Two-Way ANOVA for data analysis and graphical presentation. Along
with instruction on the use of R and R syntax associated with
Two-Way ANOVA, this brief will also reinforce the use of
descriptive statistics and graphical figures to complement outcomes
from parametric Two-Way ANOVA.
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