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Books > Science & Mathematics > Biology, life sciences > Life sciences: general issues
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Using R for Biostatistics (Hardcover, 1st ed. 2021)
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Using R for Biostatistics (Hardcover, 1st ed. 2021)
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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.
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