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Researchers and students who use empirical investigation in their
work must go through the process of selecting statistical methods
for analyses, and they are often challenged to justify these
selections. This book is designed for readers with limited
background in statistical methodology who seek guidance in
defending their statistical decision-making in the worlds of
research and practice. It is devoted to helping students and
scholars find the information they need to select data analytic
methods, and to speak knowledgeably about their statistical
research processes. Each chapter opens with a conundrum relating to
the selection of an analysis, or to explaining the nature of an
analysis. Throughout the chapter, the analysis is described, along
with some guidance in justifying the choices of that particular
method. Designed to offer statistical knowledge to the
non-specialist, this volume can be used in courses on research
methods, or for courses on statistical applications to biological,
medical, life, social, or physical sciences. It will also be useful
to academic and industrial researchers in engineering and in the
physical sciences who will benefit from a stronger understanding of
how to analyze empirical data. The book is written for those with
foundational education in calculus. However, a brief review of
fundamental concepts of probability and statistics, together with a
primer on some concepts in elementary calculus and matrix algebra,
is included. R code and sample datasets are provided.
Statistical Methods for Field and Laboratory Studies in Behavioral
Ecology focuses on how statistical methods may be used to make
sense of behavioral ecology and other data. It presents fundamental
concepts in statistical inference and intermediate topics such as
multiple least squares regression and ANOVA. The objective is to
teach students to recognize situations where various statistical
methods should be used, understand the strengths and limitations of
the methods, and to show how they are implemented in R code.
Examples are based on research described in the literature of
behavioral ecology, with data sets and analysis code provided.
Features: This intermediate to advanced statistical methods text
was written with the behavioral ecologist in mind Computer programs
are provided, written in the R language. Datasets are also
provided, mostly based, at least to some degree, on real studies.
Methods and ideas discussed include multiple regression and ANOVA,
logistic and Poisson regression, machine learning and model
identification, time-to-event modeling, time series and stochastic
modeling, game-theoretic modeling, multivariate methods, study
design/sample size, and what to do when things go wrong. It is
assumed that the reader has already had exposure to statistics
through a first introductory course at least, and also has
sufficient knowledge of R. However, some introductory material is
included to aid the less initiated reader. Scott Pardo, Ph.D., is
an accredited professional statistician (PStat (R)) by the American
Statistical Association. Michael Pardo is a Ph.D. is a candidate in
behavioral ecology at Cornell University, specializing in animal
communication and social behavior.
In engineering and quality control, various situations, including
process validation and design verification, require equivalence and
noninferiority tests. Equivalence and Noninferiority Tests for
Quality, Manufacturing and Test Engineers presents methods for
using validation and verification test data to demonstrate
equivalence and noninferiority in engineering and applied science.
The book covers numerous tests drawn from the author's more than 30
years of work in a range of industrial settings. It provides
computational formulas for the tests, methods to determine or
justify sample sizes, and formulas to calculate power and operating
characteristic curves. The methods are accessible using standard
statistical software and do not require complicated programming.
The book also includes computer code and screen shots for SAS, R,
and JMP. This book provides you with a guide to performing
validation and verification tests that demonstrate the adequacy of
your process, system, or product. It will help you choose the best
test for your application.
In engineering and quality control, various situations, including
process validation and design verification, require equivalence and
noninferiority tests. Equivalence and Noninferiority Tests for
Quality, Manufacturing and Test Engineers presents methods for
using validation and verification test data to demonstrate
equivalence and noninferiority in engineering and applied science.
The book covers numerous tests drawn from the author's more than 30
years of work in a range of industrial settings. It provides
computational formulas for the tests, methods to determine or
justify sample sizes, and formulas to calculate power and operating
characteristic curves. The methods are accessible using standard
statistical software and do not require complicated programming.
The book also includes computer code and screen shots for SAS, R,
and JMP. This book provides you with a guide to performing
validation and verification tests that demonstrate the adequacy of
your process, system, or product. It will help you choose the best
test for your application.
Researchers and students who use empirical investigation in their
work must go through the process of selecting statistical methods
for analyses, and they are often challenged to justify these
selections. This book is designed for readers with limited
background in statistical methodology who seek guidance in
defending their statistical decision-making in the worlds of
research and practice. It is devoted to helping students and
scholars find the information they need to select data analytic
methods, and to speak knowledgeably about their statistical
research processes. Each chapter opens with a conundrum relating to
the selection of an analysis, or to explaining the nature of an
analysis. Throughout the chapter, the analysis is described, along
with some guidance in justifying the choices of that particular
method. Designed to offer statistical knowledge to the
non-specialist, this volume can be used in courses on research
methods, or for courses on statistical applications to biological,
medical, life, social, or physical sciences. It will also be useful
to academic and industrial researchers in engineering and in the
physical sciences who will benefit from a stronger understanding of
how to analyze empirical data. The book is written for those with
foundational education in calculus. However, a brief review of
fundamental concepts of probability and statistics, together with a
primer on some concepts in elementary calculus and matrix algebra,
is included. R code and sample datasets are provided.
Statistical Methods for Field and Laboratory Studies in Behavioral
Ecology focuses on how statistical methods may be used to make
sense of behavioral ecology and other data. It presents fundamental
concepts in statistical inference and intermediate topics such as
multiple least squares regression and ANOVA. The objective is to
teach students to recognize situations where various statistical
methods should be used, understand the strengths and limitations of
the methods, and to show how they are implemented in R code.
Examples are based on research described in the literature of
behavioral ecology, with data sets and analysis code provided.
Features: This intermediate to advanced statistical methods text
was written with the behavioral ecologist in mind Computer programs
are provided, written in the R language. Datasets are also
provided, mostly based, at least to some degree, on real studies.
Methods and ideas discussed include multiple regression and ANOVA,
logistic and Poisson regression, machine learning and model
identification, time-to-event modeling, time series and stochastic
modeling, game-theoretic modeling, multivariate methods, study
design/sample size, and what to do when things go wrong. It is
assumed that the reader has already had exposure to statistics
through a first introductory course at least, and also has
sufficient knowledge of R. However, some introductory material is
included to aid the less initiated reader. Scott Pardo, Ph.D., is
an accredited professional statistician (PStat (R)) by the American
Statistical Association. Michael Pardo is a Ph.D. is a candidate in
behavioral ecology at Cornell University, specializing in animal
communication and social behavior.
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