Brief Reviews of Teaching Materials

By Lenth, Russell V. | The American Statistician, November 2003 | Go to article overview

Brief Reviews of Teaching Materials

Lenth, Russell V., The American Statistician

Editor's Note--This article is the first of perhaps many containing brief reviews of new teaching materials, such as introductory textbooks, new editions of standard textbooks, workbooks, software, and other materials intended for supplemental use in teaching. Many of these materials would otherwise not be reviewed at all, or would instead receive telegraphic reviews in Journal of the American Statistical Association. A small number of them may receive full reviews at a later date.

101 Careers in Mathematics (2nd ed.).

Andrew STERRETT (Ed.). Washington, DC: Mathematical Association of America, 2001, xii + 340 pp., $34.95 (P), ISBN: 0-88385-728-6.

This book contains two-page biographies of 101 individuals who have careers in the mathematical sciences. Their educations vary from Bachelor's to Master's to PhD, in roughly equal numbers. Statistics, biostatistics, actuarial science, quality control, operations research, finance, and other statistics-related fields (what field isn't?) are reasonably well represented. There are even a few people I know. There are also appendixes on how to find a job, how to interview, teamwork, working in industry, and so on. There is no index, but one appendix lists the individuals by degrees held; so it is easy to identify the ten who have statistics degrees. This book is nicely put together, and will be valuable to many teachers in giving career advice to their students.

Introductory Biostatistics.

Chap T. LE. Hoboken, NJ: John Wiley & Sons, Inc., 2003, xvi + 536 pp., $94.95 (H), lSBN: 0-471-41816-1.

This appears to be a fairly standard text aimed at professional students in the health sciences. Chapter topics include descriptive statistics for categorical and continuous data, probability and distributions, estimation of means, proportions, odds ratios, and correlations, concepts of testing, comparisons of proportions and of means, linear and logistic regression, Poisson regression, survival data and matched case-control studies, and clinical study designs. There are lots of exercises, and a strong emphasis on data examples in the health sciences.

Statistical Design and Analysis of Experiments: With Applications to Engineering and Science

Robert L. MASON, Richard F. GUNST, and James L. HESS. Hoboken, NJ; John Wiley & Sons, Inc., 2003, xix + 728 pp., $94.95 (H), ISBN: 0-471-37216-1.

The first edition of this book is well regarded by industrial statisticians; see Murray (1990) for a review. The new edition has new or expanded materials on distinguishing populations and processes, and on modeling issues. The amount of introductory material is reduced so that so that readers get to design topics sooner. It is organized in four parts. Part I, statistical methods (3 chapters) contains introductory material, standard methods for confidence intervals and tests, and sample-size determination. The factorial experiments part (5 chapters) discusses completely randomized designs, multiple comparisons, and unreplicated and fractional two-level designs. The third part (5 chapters) is on designs with random effects, including blocking, nesting, variance-components estimation, split plots, and Taguchi designs. The last part on designs with quantitative predictors (6 chapters) includes materials on linear regression, analysis of covariance, response surfaces, and variable selection. The book maintains a nice applications flavor in its examples and numerous exercises.

Research Design and Statistical Analysis (2nd ed.).

Jerome L. MYERS and Arnold D. WELL. Mahwah, NJ: Lawrence Erlbaum Associates, Inc., 2003, xviii + 760 pp., $79.00 (H + CD), ISBN: 0-8058-4037-0.

This is a fairly typical text on analysis of experimental data, oriented mostly to the social sciences. There are chapters on basic concepts and methods, completely randomized designs, contrasts, multifactor designs, repeated measures, mixed designs, covariance models, hierarchical designs, Latin squares, correlation, regression, and linear models. …

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