Reviews of Books and Teaching Materials

Brian S. Everitt, now a professor emeritus, is prolific author of statistics books at the introductory or intermediate level that span a range of applications. Some of his books provide the reader with an introduction to the use of statistical packages such as SAS, SPSS, or R; A Whistle-Stop Tour of Statistics does not. A relatively young academic, Statistical Inference: Learning from Data is Simon Vaughan’s first book. His background is physics and astronomy, and it shows in the data that he uses in the book. His computing language of preference is R and he provides a brief introduction to its use for all the methods he covers in his book. Both authors write very well and both books are a pleasure to read. The two books are similar yet quite different. Their similarity lies in the fact that they are both about introductory topics in statistics yet from a slightly advanced point of view. Where do we place these books along the spectrum for the genre of books providing an introduction to statistics? The first dimension to look at is length. Both Everitt’s and Vaughan’s books are about 200 pages long, about one-half to one-quarter the length of a standard introductory statistics book with the same, or similar, range of topics. Consequently, the writing in these books is compact and succinct so that any hand-holding blather to which many introductory books are susceptible cannot be found in either book. On the other hand, the writing is not so compact as to compete with the Very Short Introductions series published by Oxford University Press; Statistics: A Very Short Introduction runs to 125 small-sized pages. Along another dimension, both Everitt and Vaughan assume knowledge of calculus; a few derivatives and/or integrals pop up, not unexpectedly, in both books. Unlike a standard introductory statistics textbook, neither book has chapter-ending exercises for the student to work through. As a supplement to his book, Vaughan does provide a number of exercises in a pdf file on the publisher’s website. Both books are, in a sense, data driven. Everitt provides a number of datasets to illustrate the statistical procedures he covers. They have the feel of having originated in actual situations; however, he provides no source references for his data so that the reader, or at least this reader, cannot be sure. Vaughan provides data, mostly from famous physics experiments or astronomical observations, about half from the late nineteenth century and the other half from the late twentieth century. References to the data sources in Vaughan’s book, their collection, and original analysis, are given, as well as data files that can be downloaded from the publisher’s website. Like Statistics: A Very Short Introduction, Everitt’s A Whistle-Stop Tour of Statistics is meant to introduce the reader to a number of statistical topics and methods without going into great detail, although in more detail than in the Very Short Introductions series. In this regard, I think that Everitt has been quite successful. All the standard topics, such as probability, estimation, hypothesis testing, analysis of variance, and regression are discussed. The discussion is short, to the point, readable, and reliable. In addition, there are the more “advanced” topics of logistic regression, survival analysis, longitudinal data analysis, and multivariate analysis, thus providing the reader with a short introduction to a wide array of the methods in the statistical arsenal without getting bogged down in detail. Since this is a no frills treatment of statistics, there are chapter-ending additional reading lists where each topic can be pursued in greater depth and detail. At first glance, Vaughan’s Scientific Inference: Learning from Data has the appearance of an extended Very Short Introduction, but initial impressions are deceiving. A more careful look at the book shows a well-written compact calculus-based introductory statistics and data analysis textbook with, as the author says, “a bias towards students in physics. ” Despite the fact that the book favors physics examples, I think it would make at least an excellent supplementary reading book for students who do not have such a bias. Would I go beyond supplementary reading and use the book in a oneor two-semester calculusbased introductory statistics course? Here I am torn. I liked the discussion of deductive, inductive, and abductive reasoning at the beginning of the book. It was very nice to have a discussion of how the field of statistics fits more generally into scientific reasoning. I liked the derivation of the least-square estimates in simple linear regression later on—simple, clear and, once again, compact. I felt, however, that the treatments of the one-sample and two-sample t-tests a little too compact for me; I would have to put together some supplementary material if I were to teach from this book. One very nice feature was the introduction and discussion of some physical models such as fluid dynamics due to the civil engineer Osborne Reynolds and radioactive decay model arising from the experiments of the physicists Ernest Rutherford and Hans Geiger. These models were used to introduce the topic of maximum likelihood estimation in a way that was different from the norm of statistics textbooks. I liked the use of R—it is the present and future of statistical computing—but felt there could have been more of it in the book. In summary, I think both these books should be read by anyone with a calculus background wanting a brief, but good, introductions to statistics. They are also excellent “refreshers” for those who have already experienced an introduction to statistics and want a slightly different approach or point of view. In comparing the two books I found Vaughan’s book the more interesting one because it was quite different from the norm; I came across unexpected topics such as fluid dynamics treated in a way that was accessible to me as a statistician.