Introduction To Nonparametric Statistics For The Biological Sciences Using R
by Thomas W. MacFarland /
2016 / English / PDF
5.1 MB Download
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:
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 when nonparametric approaches to data analysis
are appropriateTo introduce the leading nonparametric tests commonly used in
biostatistics and how R is used to generate appropriate
statistics for each test
To introduce the leading nonparametric tests commonly used in
biostatistics and how R is used to generate appropriate
statistics for each testTo introduce common figures typically associated with
nonparametric data analysis and how R is used to generate
appropriate figures in support of each data set
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.
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.
