Reliability Engineering Handbook

of the size of a P value that the authors acknowledge are not universally accepted. There is also a section that discusses the relationship between hypothesis tests and conŽ dence intervals and suggests that these two methods should always be used simultaneously. Standard methods for inferences about a population variance or standard deviation are based on the chi-square distribution. Since these techniques are very sensitive to nonnormality, the authors indicate that this approach is no longer recommended. The bootstrap technique is an alternative, but it is not presented in this book. All of the hypothesis tests seem to evolve from an appropriate example, which naturally leads to interpretation of results. The last two chapters involve tables of counts, and regression and correlation. The authors use some of their own research projects for examples and use a detective-style theme to motivate regression. Each section of the book has only a minimal number of exercises, probably not enough to construct an adequate homework assignment. However, there is a quiz at the end of each section, that students might Ž nd useful, and there are plenty of open-ended review problems at the end of each chapter. There are also numerous Minitab and Excel commands and output screens to motivate concepts and to help solve speciŽ c problems. I would have a difŽ cult time using this textbook in an introductory statistics class taught in a mathematics or statistics department. However, the writing style and current, real-world examples might be Ž ne in a general education class where students need and want only a cursory look at statistics.