I Basic Concepts
Introduction
A Crash Course on R
Statistical Assumptions
Statistical Inference
II Statistical Modeling
Linear Models
Nonlinear Models
Classi cation and Regression Tree
Generalized Linear Model
III Advanced Statistical Modeling
Simulation for Model Checking and Statistical Inference
Multilevel Regression
Using Simulation for Evaluating Models Based on Statistical Signicance Testing
Bibliography
Song S. Qian, PhD, is an assistant professor in the Department of Environmental Sciences at the University of Toledo, Ohio, USA
"‘Environmental and Ecological Statistics with R, Second Edition’
offers a comprehensive and highly engaging look at modern
statistical modeling. It covers a wide range of topics, including
linear and non-linear regression models, classification and
regression tree structures, and generalized linear models. I
particularly enjoyed the third section of the book covering
interesting areas of advanced statistical modeling, where the
reader can find many didactical examples that are highly relevant
to environmental management such as the problem of Cryptosporidium
in drinking water, the uncertainty in water quality measurements
using the ELISA method as an example, or the threshold indicator
taxa analysis.
The author has the unique ability of being able to clearly explain
difficult statistical concepts whilst still making the book
accessible for researchers of all levels, from undergraduate
students to researchers already conducting serious empirical
research. The emerging philosophical consensus that both the
frequentist and Bayesian way of thinking are important in
statistical practice is nicely articulated throughout the book. R
codes are also provided, enabling researchers to apply statistical
techniques to their own ecological or environmental management
problems. Overall, this book is exceptionally well written and
should prove an invaluable tool either as a classroom text or as an
addition to the research bookshelf. I am very confident that
‘Environmental and Ecological Statistics with R, Second Edition’
will end up being a classic!"
—George Arhonditsis, Professor and Chair of the Department of
Physical & Environmental Sciences, University of Toronto"Shortly
after it was published, the first edition of ‘Environmental and
Ecological Statistics with R’ by Song S. Qian became a go-to book
for environmental scientists interested in the application of
Bayesian methods in R to address a broad range of environmental
issues. The book serves to introduce Bayesian statistical analysis
in an accessible way to ecologists and environmental scientists,
with numerous applications in R. An important aspect of this book
is that it is written primarily for scientists, not statisticians;
thus the author emphasizes the broader context of scientific
inference, within which statistical analysis plays a critical role.
The second edition includes several important additions and
improvements including: an expanded introduction to R code, greater
emphasis on hypothesis testing and p-values, and an iterative
approach to scientific inference through the continued refinement
of a model for a data set as the book’s chapters explore more
advanced statistical methods. The R code included in the book
outlines key computational procedures and provides a workable
foundation upon which researchers can conduct scientific inference
and statistical analysis with their own data."
—Kenneth H. Reckhow, Professor Emeritus, Duke University"Statistics
is a science to interpret, model, and explain variation. It
provides us with a strategy to evaluate potential models rather
than a rule to specify a specific statistical model as most people
expected. However, most of the time, the importance of model
evaluation is largely underestimated in the training of
students…Most students, like mine, are taught about statistics in a
classical way. They are impressed but somehow intimidated by some
technical terms like significance and power effect, thus becoming
afraid of applying statistics to real data and questions. This book
gives us a new way to teach statistics to biological and ecological
students at research level… This book not only teaches us about
statistical methodology but also philosophy and strategy in applied
statistics…With the modification of chapters 1 to 10, with the
addition of a new chapter, this edition features a stronger
emphasis on model evaluation compared to that of the previous
edition."
—Prof. Bo-Ping Han, Department of Ecology, Jinan UniversityPraise
for the First Edition:"...Overall, I liked the book. I expect that
I will be pulling examples from it when I teach methods courses to
students in the sciences. … it does contain many interesting and
intriguing examples, and good examples of R code. So I can and do
recommend it as a helpful resource"
—Jane L. Harvill, The American Statistician, November 2011"Qian
effectively blends fundamentals of scientific methods with
statistical thinking, modeling, computing, and inference. … the
text is well formatted with liberal use of illustrative portions of
R code … It is clear that Qian has taken great care in developing
this book and has succeeded in meeting his stated purpose. The book
reflects Qian’s insights into teaching environmental and ecological
modeling developed over many years in applied statistics and as an
educator in applied sciences"
—Biometrics, June 2011"This book gives a data-oriented introduction
to statistical modeling of environmental and ecological phenomena.
It is a beautiful scientific guideline for a computer-based model
building and evaluation process. … This introductory book gives a
diversified overview of modern applied statistics while always
following an inductive, data-based approach...Meaningful graphics
and R code/output embedded in the text support the conclusions
drawn and facilitate the application to own data sets. … Students
and researchers of environmental sciences with basic knowledge in
statistics will find this book valuable as both a work of reference
and an introductory guide to statistical modeling with R"
—Sebastian Engelke and Martin Schlather, Biometrical Journal, 2011
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