This book will equip practitioners with the necessary background in testing hypotheses and decision theory to enable practical applications. Real-world problems of missing and censored data, multiple comparisons, non-responders, after-the-fact covariates, and outliers are dealt with at length. The third edition includes many more real-world illustrations from biology, business, clinical trials, economics, geology, law, medicine, social science, and engineering along with twice the number of exercises. New sections are added on sequential analysis, multivariate analysis, and exact analysis of multi-factor designs.

"a ]the author has packaged an excellent and modern set of topics around the development and use of quantitative models.... If you need to learn about resampling, this book would be a good place to start."

a "Technometrics (Review of the Second Edition)

This thoroughly revised and expanded third edition is a practical guide to data analysis using the bootstrap, cross-validation, and permutation tests. Only requiring minimal mathematics beyond algebra, the book provides a table-free introduction to data analysis utilizing numerous exercises, practical data sets, and freely available statistical shareware.

Topics and Features

* Practical presentation covers both the bootstrap and permutations along with the program code necessary to put them to work.

* Includes a systematic guide to selecting the correct procedure for a particular application.

* Detailed coverage of classification, estimation, experimental design, hypothesis testing, and modeling.

* Suitable for both classroom use and individual self-study.

New to the Third Edition

* Procedures are grouped by application; a prefatory chapter guides readers to the appropriate reading matter.

* Program listings and screen shots now accompany each resampling procedure: Whether one programs in C++, CART, Blossom, Box Sampler (an Excel add-in), EViews, MATLAB, R, Resampling Stats, SAS macros, S-PLUS, Stata, or StatXact, readers will find the program listings and screen shots needed to put each resampling procedure into practice.

* To simplify programming, code for readers to download and apply is posted at http: //www.springeronline.com/0-8176-4386-9.

* Notation has beensimplified and, where possible, eliminated.

* A glossary and answers to selected exercises are included.

With its accessible style and intuitive topic development, the book is an excellent basic resource for the power, simplicity, and versatility of resampling methods. It is an essential resource for statisticians, biostatisticians, statistical consultants, students, and research professionals in the biological, physical, and social sciences, engineering, and technology.

A Practical Guide with Step-by-Step Explanations, Numerous Worked Examples, and R CodeThe A-Z of Error-Free Research describes the design, analysis, modeling, and reporting of experiments, clinical trials, and surveys. The book shows you when to use statistics, the best ways to cope with variation, and how to design an experiment, determine optimal sample size, and collect useable data. It also helps you choose the best statistical procedures for your application and takes you step by step through model development and reporting results for publication. Transition from Student to ResearcherHelping you become a confident researcher, the book begins with an overview of when-and when not-to use statistics. It guides you through the planning and data collection phases and presents various data analysis techniques, including methods for sample size determination. The author then covers techniques for developing models that provide a basis for future research. He also discusses reporting techniques to ensure your research efforts get the proper credit. The book concludes with case-control and cohort studies.

A Practical Guide with Step-by-Step Explanations, Numerous Worked Examples, and R CodeThe A-Z of Error-Free Research describes the design, analysis, modeling, and reporting of experiments, clinical trials, and surveys. The book shows you when to use statistics, the best ways to cope with variation, and how to design an experiment, determine optimal sample size, and collect useable data. It also helps you choose the best statistical procedures for your application and takes you step by step through model development and reporting results for publication. Transition from Student to ResearcherHelping you become a confident researcher, the book begins with an overview of when-and when not-to use statistics. It guides you through the planning and data collection phases and presents various data analysis techniques, including methods for sample size determination. The author then covers techniques for developing models that provide a basis for future research. He also discusses reporting techniques to ensure your research efforts get the proper credit. The book concludes with case-control and cohort studies.

Previous edition sold over 1400 copies worldwide.

This new edition includes many more real-world illustrations from biology, business, clinical trials, economics, geology, law, medicine, social science and engineering along with twice the number of exercises.