Statistical Reasoning for Everyday Life

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<ul><li><p>This article was downloaded by: [West Virginia University]On: 15 October 2014, At: 19:39Publisher: Taylor &amp; FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House,37-41 Mortimer Street, London W1T 3JH, UK</p><p>TechnometricsPublication details, including instructions for authors and subscription information:</p><p>Statistical Reasoning for Everyday LifeMark Baileyaa SAS Institute Inc.Published online: 01 Jan 2012.</p><p>To cite this article: Mark Bailey (2004) Statistical Reasoning for Everyday Life, Technometrics, 46:4, 490-491, DOI: 10.1198/tech.2004.s234</p><p>To link to this article:</p><p>PLEASE SCROLL DOWN FOR ARTICLE</p><p>Taylor &amp; Francis makes every effort to ensure the accuracy of all the information (the Content) containedin the publications on our platform. However, Taylor &amp; Francis, our agents, and our licensors make norepresentations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of theContent. Any opinions and views expressed in this publication are the opinions and views of the authors, andare not the views of or endorsed by Taylor &amp; Francis. The accuracy of the Content should not be relied upon andshould be independently verified with primary sources of information. Taylor and Francis shall not be liable forany losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoeveror howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use ofthe Content.</p><p>This article may be used for research, teaching, and private study purposes. Any substantial or systematicreproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in anyform to anyone is expressly forbidden. Terms &amp; Conditions of access and use can be found at</p><p></p></li><li><p>490 BOOK REVIEWS</p><p>of EVOP, although the relatively wide scaling illustrated in Figure 11.4 servesto undermine the discussions emphasis on slight process changes.</p><p>The author provides a good discussion of the differences, both philosophi-cal and operationally, between Taguchi and classical experimental design. Thediscussion of robust design is highly useful. There are some aspects of contro-versy to the materials covered. Some statisticians have attacked aspects of theTaguchi methodology, asserting that it is either not rigorous enough or makesquestionable assumptions. Taguchi practitioners have attacked dangerouslyrigorous or too-narrow aspects of classical design of experiments applications.This review does not referee such issues; one should use what works best fromeach philosophy. Applied adaptively, either approach will be highly beneficial.The reviewers personal opinion is that classical design is often applied too nar-rowly and inflexibly in practice with respect to the underlying business prob-lem and suffers from some systematic statistical overinterpretation, whereas theTaguchi design methodology is better focused on the underlying business prob-lem but would benefit from more rigor in some aspects.</p><p>Of particular value in the Taguchi material is the concept of quality lossfunction. Quality efforts have often foundered in the longer term when qualityprofessionals have been unable to explicitly document a value for their effortsor to determine when additional effort is likely not to be cost-effective. Theillustrated definition and application of quality loss functions should be con-sidered only as a starting point; there are numerous alternatives (e.g., otherpossible loss functions, multivariate loss functions that are not simple sumsof the corresponding univariate loss functions). Chapter 12, Introduction toTaguchi and Parameter Design, is good but will require several readings bydesign novices. Chapter 16, Taguchi and ANOVA, has a useful discussion ontolerancing but could benefit from more explanation contrasting Taguchi andtraditional ANOVA. Chapter 17, Case Studies, is good, although more suchstudies and examples would have been useful throughout the text. As with mostexperimental design books, the subject of how to deal with multiple and oftenconflicting responses to be optimized simultaneously is developed only spo-radically. The appendixes are fairly extensive and range from the highly useful(Orthogonal Arrays and Linear Graphs) to the not as useful (Computer CodeExamples). The book does not incorporate any software, and there is no re-lated website with additional materials. This book is a worthwhile addition tothe bookshelf of engineers or quality professionals who use or intend to useexperimental design.</p><p>Thomas J. BZIKAir Products and Chemicals, Inc.</p><p>Applying Contemporary Statistical Techniques, byRand R. WILCOX, San Diego, CA: Academic Press, 2003,ISBN 0-12-751541-0, xii + 662 pp., $79.95.</p><p>Increasingly, we call on applied statistics to address practical problemswhere the data may be nonnormal and heteroscedastic, and where there may belittle data available. Wilcox suggests that at the root of many of these problemsin applied research lie two important issues: (1) finding methods for comparinggroups of individuals or things, and (2) studying how two or more variables arerelated. So, using examples from astronomy, engineering, medicine, psychol-ogy, and other disciplines, he cohesively presents a book to equip the modernstatistical experimenter.</p><p>It is important to address the scope of this book, because it is differentthan many others. The main topics are ANOVA and regression. However, youwill not find the traditional presentation of simple linear regression followedby multiple linear regression, general linear regression, and perhaps nonlinearregression. Instead, Chapter 1 presents a brief historical account of how andwhy statistical techniques were developed based on the assumptions of nor-mality and homoscedasticity. As the first part of the book progresses, Wilcoxlays down the fundamental concepts in probability (Chap. 2), summarizing data(Chap. 3), sampling distributions and confidence intervals (Chap. 4), hypothesistesting (Chap. 5), and least squares regression (Chap. 6) while including severalnice discussions that address the potential consequences of violating the nor-mality and homoscedasticity assumptions. I especially liked the discussion as-sociated with the presentation of least squares regression (Sec. 6.3.2 in Chap. 6).These discussions serve to motivate the modern approaches that follow. Thesecond half of the book then gives some basic bootstrap methods, includingthe bootstrap t-interval (Chap. 7), followed by the relative merits of bootstraptechniques with respect to ANOVA and regression. Specifically, these include</p><p>bootstrap methods for comparing means (Chap. 8), one-way ANOVA (Chap. 9),two-way ANOVA (Chap. 10), comparing dependent groups (Chap. 11), andmultiple comparisons (Chap. 12). Within these chapters, some nonbootstrapmethods for these topics are also presented and compared. New estimators andsmoothers for robust regression (Chaps. 13 and 14) and rank-based and non-parametric methods (Chap. 15) round out the book. The book synthesizes manyrecent advances and insights with more than 350 citations in the reference list,more than one-third of which have appeared within the last 10 years.</p><p>A subtheme of the book is to make the techniques accessible by supplyinga rather large library of SPLUS functions that are available via anonymousftp ( and a website ( (According to thewebsite, new updates to the functions since the books publication are underdevelopment.) For each SPLUS function, the syntax, description, and an ex-ample are presented immediately after the relevant theory. There are numer-ous end-of-chapter exercises (with solutions to some provided in an appendix)to reinforce the material. Other than the software, no additional materials areavailable.</p><p>I will confess that I had to overcome some mental hurdles from my trainingin regression based on traditional approaches (Neter et al. 1989 is on my shelf)to appreciate this book. After doing so, what I found most generally appealingabout the book is that it conveys the sense that the field of statistics is evolving,and that this evolution is based on a critical (yet appreciative) examination ofprevious work. From a curriculum standpoint, this books focus makes it appro-priate for a graduate course in regression, particularly those that want to offerstudents the tools for analysis of research data. Wilcoxs previous book (Wilcox2001) may be appropriate for an undergraduate course. It would be nice to seeacademic curricula evolve as well to include these books in coursework.</p><p>Harriet Black NEMBHARDUniversity of Wisconsin</p><p>REFERENCES</p><p>Neter, J., Wasserman, W., and Kutner, M. H. (1989), Applied Linear RegressionModels (2nd ed.), Homewood, IL: Irwin.</p><p>Wilcox, R. R. (2001), Fundamentals of Modern Statistical Methods: Substan-tially Improving Power and Accuracy, New York: Springer-Verlag.</p><p>Statistical Reasoning for Everyday Life (2nd ed.),by Jeffrey O. BENNETT, William L. BRIGGS, andMario F. TRIOLA, Boston: Addison-Wesley, 2003,ISBN 0-201-77128-4, xx + 499 pp., $55.80.</p><p>This textbook is intended for high school students who do not plan additionalcourse work in statistics. It should provide a suitable introduction to studentsmajoring in any field except mathematics or a physical science. The subjects,as indicated by the Table of Contents, are as follows:</p><p>Chapter 1, Speaking of Statistics: What is/are statistics?; sampling; types ofstatistical study; should you believe a statistical study?</p><p>Chapter 2, Measurement in Statistics: Data types and levels of measurement;dealing with errors; uses of percentages in statistics; index numbers</p><p>Chapter 3, Visual Displays of Data: Frequency tables; picturing distributionsof data; graphics in the media; a few cautions about graphics</p><p>Chapter 4, Describing Data: What is average?; shapes of distributions; mea-sures of variation</p><p>Chapter 5, A Normal World: What is normal?; properties of the normal dis-tribution; the central limit theorem</p><p>Chapter 6, Probability in Statistics: The role of probability in statistics: sta-tistical significance; basics of probability; probabilities with large numbers;combining probabilities</p><p>Chapter 7, Correlation and Causality: Seeking correlation; interpreting cor-relations; best-fit lines and prediction; the search for causality</p><p>Chapter 8, From Samples to Populations: Sampling distributions; estimatingpopulation means; estimating population proportions</p><p>Chapter 9, Hypothesis Testing: Fundamentals of hypothesis testing; settingup hypothesis tests; hypothesis tests for population means; hypothesis tests;further considerations; hypothesis tests; population proportions</p><p>Chapter 10, Further Applications of Statistics: Ideas of risk and life ex-pectancy; statistical paradoxes; hypothesis testing with two-way tables.</p><p>Supplements include the Addison-Wesley complete on-line course throughtheir website. This site includes a fully integrated version of</p><p>TECHNOMETRICS, NOVEMBER 2004, VOL. 46, NO. 4</p><p>Dow</p><p>nloa</p><p>ded </p><p>by [</p><p>Wes</p><p>t Vir</p><p>gini</p><p>a U</p><p>nive</p><p>rsity</p><p>] at</p><p> 19:</p><p>39 1</p><p>5 O</p><p>ctob</p><p>er 2</p><p>014 </p></li><li><p>BOOK REVIEWS 491</p><p>the textbook, online testing, diagnosis, tutorials, and grade books. A separateTechnology Manual and Workbook provides examples requiring technology,such as the TI-83+ graphing calculator, Excel, MINITAB, and STATDISK. Theworkbook comes with STATDISK and DDXL software. Supplements for in-structors include an Instructors Guide and Solution Manual by David Lund;a computerized test generator, TestGen-Eq, for Windows and Macintosh; andthree sets of tests in the Test Bank. Supplements for students include a StudentSolutions Manual by David Lund and tutoring through the AWL Math TutorCenter.</p><p>An introductory book must carefully consider its scope and make difficultdecisions to be successful with its intended audience. The authors have donewell in this regard. The material provides sufficient material to develop a sta-tistically literate citizen in the modern world. It achieves this result largely byaddressing the statistical information and concepts, such as margin of error,that are encountered everywhere in our normal surroundings and maintainingtheir context, instead of presenting an isolated statistical concept as abstractlessons. A common form of statistics that is not commonly found in other textbooks is index numbers, which this book addresses at length.</p><p>The book is written in a clear and compelling style. It uses ample visual aidsand graphic designs to make the lessons engaging and to help organize new in-formation for the student. Its style should appeal to its intended audience, butit might be criticized by instructors or students who appreciate a more tradi-tional mathematics book. There is an overall rewarding sense of completenessand clarity at the conclusion of each presentation. I found no glaring mistakesto detract from the flow of the presentation.</p><p>Every topic is introduced in the context of at least one real-life example orcase from history or the present to learn from and practice. Each section in-cludes questions and extra problems in the form of Projects for the Web andBeyond. Each chapter ends with a strong example in the Focus on . . . high-light. Topics include medicine, education, ethics, politics, and the environment.More theoretical or advanced information is called out into the book margin asa Technical Note.</p><p>There is a good deal of material about important statistical plots, what kindof plot to use with particular data, and specific pitfalls and misuse. Othergood points include a strong section on the distinction between correlation andcausality. Just a couple of minor bad points include a rule of thumb for comput-ing the sample standard deviation from the sample range and computing lowand high values (sample mean plus or minus two sample standard deviations)without interpretation or reference to a distribution.</p><p>Statistical Reasoning for Everyday Life should be appreciated by its intendedaudience and popular. It could also be used by business and technical profes-sionals with little previous statistics training for self-study or refresher.</p><p>Mark BAILEYSAS Institute Inc.</p><p>Probability and Statistics for Computer Science, byJames L. JOHNSON, Hoboken, NJ: Wiley, 2003, ISBN0-471-32672-0, xvi + 744 pp., $98.00.</p><p>This textbook is not simply an introductory statistics book adapted for un-dergraduate computer science majors. This is really a statistics textbook writtenexplicitly f...</p></li></ul>