Thomas W. O’Gorman 
Adaptive Tests of Significance Using Permutations of Residuals with R and SAS 

Provides the tools needed to successfully perform adaptive tests
across a broad range of datasets

Adaptive Tests of Significance Using Permutations of Residuals
with R and SAS illustrates the power of adaptive tests and
showcases their ability to adjust the testing method to suit a
particular set of data. The book utilizes state-of-the-art software
to demonstrate the practicality and benefits for data analysis in
various fields of study.

Beginning with an introduction, the book moves on to explore the
underlying concepts of adaptive tests, including:

* Smoothing methods and normalizing transformations

* Permutation tests with linear methods

* Applications of adaptive tests

* Multicenter and cross-over trials

* Analysis of repeated measures data

* Adaptive confidence intervals and estimates

Throughout the book, numerous figures illustrate the key
differences among traditional tests, nonparametric tests, and
adaptive tests. R and SAS software packages are used to perform the
discussed techniques, and the accompanying datasets are available
on the book’s related website. In addition, exercises at the end of
most chapters enable readers to analyze the presented datasets by
putting new concepts into practice.

Adaptive Tests of Significance Using Permutations of Residuals
with R and SAS is an insightful reference for professionals and
researchers working with statistical methods across a variety of
fields including the biosciences, pharmacology, and business. The
book also serves as a valuable supplement for courses on regression
analysis and adaptive analysis at the upper-undergraduate and
graduate levels.

About the author

Thomas W. O’gorman, Ph D, is Associate Professor in the Department of Mathematical Sciences at Northern Illinois University. Dr. O’Gorman’s current research focuses on the analysis of adaptive methods for performing statistical tests and confidence intervals.