Analysis of Categorical Data with R (eBook, ePUB)

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Analysis of Categorical Data with R, Second Edition presents a modern account of categorical data analysis using the R software environment. It covers recent techniques of model building and assessment for binary, multicategory, and count response variables and discusses fundamentals, such as odds ratio and probability estimation. The authors give detailed advice and guidelines on which procedures to use and why to use them.The second edition is a substantial update of the first based on the authors' experiences of teaching from the book for nearly a decade. The book is organized as before, bu...

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Analysis of Categorical Data with R, Second Edition presents a modern account of categorical data analysis using the R software environment. It covers recent techniques of model building and assessment for binary, multicategory, and count response variables and discusses fundamentals, such as odds ratio and probability estimation. The authors give detailed advice and guidelines on which procedures to use and why to use them.

The second edition is a substantial update of the first based on the authors' experiences of teaching from the book for nearly a decade. The book is organized as before, but with new content throughout, and there are two new substantive topics in the advanced topics chapter-group testing and splines. The computing has been completely updated, with the "emmeans" package now integrated into the book. The examples have also been updated, notably to include new examples based on COVID-19, and there are more than 90 new exercises in the book. The solutions manual and teaching videos have also been updated.

Features:

Requires no prior experience with R, and offers an introduction to the essential features and functions of R
Includes numerous examples from medicine, psychology, sports, ecology, and many other areas
Integrates extensive R code and output
Graphically demonstrates many of the features and properties of various analysis methods
Offers a substantial number of exercises in all chapters, enabling use as a course text or for self-study
Supplemented by a website with data sets, code, and teaching videos

Analysis of Categorical Data with R, Second Edition is primarily designed for a course on categorical data analysis taught at the advanced undergraduate or graduate level. Such a course could be taught in a statistics or biostatistics department, or within mathematics, psychology, social science, ecology, or another quantitative discipline. It could also be used by a self-learner and would make an ideal reference for a researcher from any discipline where categorical data arise.

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Christopher R. Bilder is a Professor in the Department of Statistics at the University of Nebraska-Lincoln. Bilder has been the Principal Investigator for grants from the National Science Foundation and the National Institutes of Health involving research into categorical data analysis problems. His research has been published in a diverse set of outlets ranging from the Journal of the American Statistical Association to Chance. For his categorical data research, Bilder was awarded the Best Paper in Biometrics by an International Biometric Society Member Award and the American Statistical Association's Outstanding Statistical Application Award (twice). Bilder is a Fellow of the American Statistical Association.

For more than 20 years, Bilder has taught a course on categorical data analysis to students majoring in statistics and to students majoring in a wide variety of other fields of study. He also has been a pioneer in using technology in and outside the classroom through the use of class video capturing, course websites, distance learning, blended learning, and tablets during his career. Bilder's YouTube Channel at https://www.youtube.com/ChrisBilder hosts his videos from courses, workshops, and presentations.

Thomas M. Loughin is a Professor in the Department of Statistics and Actuarial Science at Simon Fraser University in Burnaby, BC, Canada. He was Chair of the department from 2014-2019 before coming to his senses. He previously held a faculty position at Kansas State University for 13 years. At K-State he was partly funded by the College of Agriculture to provide statistical collaboration and consulting for faculty and students there. As a result, he has been active in statistical consulting, particularly the design and analysis of experiments, for most of his career. As a consultant and a teacher, he specializes in communication with subject-matter experts and students, re-expressing complex statistical concepts into language that is easy to understand.

Tom's research interests include categorical data analysis, statistical learning techniques, particularly tree-based ensembles, and sports analytics. He is a Fellow of the American Statistical Association and an accredited professional statistician, maintaining both P.Stat. (SSC), PStat® (ASA). He has served on numerous committee positions within SSC and ASA and has held positions on the editorial boards of Biometrics, Technometrics, The American Statistician, and Developmental Medicine and Child Neurology.

Tom is an avid curler, playing in two leagues at the Cloverdale Curling Club. He is a craft beer aficionado and a member of the world's longest-running fantasy baseball league (*), the Snedecor Baseball Mail League.
(*) Claim unconfirmed but unrefuted.

"This book is particularly useful as a book that focuses on the analysis of discrete data rather than the theory of discrete data analysis. It is suitable for a large variety of readers: undergraduates, graduates, as well as scientific researchers. Some key features of the book separate it from other discrete data analysis textbooks. One is the use of powerful statistical software R as the learning bridge between applications and theory of discrete data analysis. For easily understood methods, the book provides multiple examples of R codes that showcase them being carried out. For methods with challenging statistical backgrounds, the R codes and the author's personal website show their working principles. Another one is the selection of

topics which considers both lecture time (one semester) and major analytic techniques in discrete data analysis. The videos and lecture notes in the author's personal website provide great benefits to college students, instructors, as well as self-learners."
~Xianggui (Harvey) Qu, Professor of Statistics, Oakland University

"I suspect that I'm not alone in singing high praises for the first (2015) edition of Bilder and Loughin's Analysis of Categorical Data with R by CRC Press; so when the opportunity came along to peruse the second (2024) edition, I was delighted. The new edition is equally superb, and the changes from the first edition are in the much-appreciated subtle refinements, honing, and added clarity. The intended audience is the advanced undergraduate or graduate data analytics-focused student or the practitioner using these methods. Notable features of the book are its readability, thorough illustrations with real data (with supplied code so one can easily follow along), excellent exercises, and the authors' delving just deep enough (but not too deep) into the important methodological details."
~Tim O'Brien, Loyola University Chicago

"Analysis of Categorical Data with R (2^nd ed.)" by Christopher R. Bilder and Thomas M. Loughin is an essential resource for students, researchers, and professionals in the fields of statistical or data sciences. This a comprehensive book which demystifies the complexities of analyzing categorical data using R, a powerful and widely used statistical software. With its clear explanations, contemporary and practical examples, and step-by-step instructions, the book enables readers to apply statistical theories and models to real-world problems efficiently. Key features include in-depth coverage of logistic regression, contingency tables, and multinomial logistic regression, along with insightful case studies that illustrate the application of techniques in various disciplines. Whether you're a novice looking to grasp the basics of categorical data analysis or a seasoned analyst seeking to enhance your skills, this book offers the tools and knowledge needed to master the analysis of categorical data with R."
~Abdus Sattar, Case Western Reserve University

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