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Dicing With Death: Chance, Risk, and Health

analyses such as discriminant analysis, principal components, MANOVA, and others. However, the book focuses on analyses that involve several variables, principally a response with several explanatory variables. The author recommends the book for “people who estimate and present multivariate models”; I think it would be especially appropriate for researchers and scientists seeking to summarize their statistical findings, as well as for undergraduate and graduate students of statistics seeking to improve their communication skills with nonstatisticians. The author suggests, and I agree, that this might be a good complementary book to accompany a standard statistical linear regression text at the undergraduate or graduate level. Many of the chapters, most of which are duplicated from Miller (2004), are quite general in nature and address topics like basic principles for effectively presenting numerical results, creating effective tables and charts, writing about data and methods, distributions and associations, and writing introductions, conclusions, and abstracts. For a more detailed assessment of these chapters, see Anderson-Cook (2005). There are five chapters (and a few appendixes) which specifically discuss multiple linear regression and logistic regression: Chapter 9, Quantitative Comparisons for Multivariate Models; Chapter 10, Choosing How to Present Statistical Results; Chapter 14, Writing about Multivariate Models; Chapter 15, Speaking about Multivariable Models; and Chapter 16, Writing for Applied Audiences. Chapters 9 and 10 discuss some of the technical aspects of multiple linear regression and logistic regression analyses and show how to best decide what portions of the statistical software outputs to include and discuss in summaries. A noticeably missing aspect in the discussion of the results is the concept of the quality of the fit of the data with a multivariable measure, which I think should be an important part of any discussion of the model. There are a number of very helpful tables that itemize the information provided, as well as what types of background knowledge are needed by audience members to best appreciate them. Chapters 14–16 give specific suggestions for presenting multiple linear regression and logistic regression results to both the statistically savvy and to nonstatisticians. One section, which I think is very helpful, presents a justification of why it is worthwhile to consider several explanatory variables simultaneously, rather than just look at correlations between the response and individual explanatory variables. This discussion is one that I have frequently had with scientists and researchers who are familiar with only the most basic statistical tools. Overall, this text is well written and contains many helpful suggestions and broader strategies for effectively communicating core statistical tools. If you are considering purchasing a book on this topic, I think that this is a solid choice. It contains all that one needs to build an effective written or oral statistical presentation.