Teaching Statistics, Resources for Undergraduate Instructors

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<ul><li><p>This article was downloaded by: [Laurentian University]On: 05 October 2014, At: 22:00Publisher: 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>The American StatisticianPublication details, including instructions for authors and subscription information:http://www.tandfonline.com/loi/utas20</p><p>Teaching Statistics, Resources for UndergraduateInstructorsThomas H Shortaa Villanova UniversityPublished online: 01 Jan 2012.</p><p>To cite this article: Thomas H Short (2002) Teaching Statistics, Resources for Undergraduate Instructors, The AmericanStatistician, 56:2, 157-158, DOI: 10.1198/tas.2002.s136</p><p>To link to this article: http://dx.doi.org/10.1198/tas.2002.s136</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 ofthe Content. Any opinions and views expressed in this publication are the opinions and views of the authors,and are not the views of or endorsed by Taylor &amp; Francis. The accuracy of the Content should not be reliedupon and should be independently verified with primary sources of information. Taylor and Francis shall not beliable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilitieswhatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out ofthe use of the 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 http://www.tandfonline.com/page/terms-and-conditions</p><p>http://www.tandfonline.com/loi/utas20http://www.tandfonline.com/action/showCitFormats?doi=10.1198/tas.2002.s136http://dx.doi.org/10.1198/tas.2002.s136http://www.tandfonline.com/page/terms-and-conditionshttp://www.tandfonline.com/page/terms-and-conditions</p></li><li><p>Statistical Thinking: Improving Business Performance.RogerHOERL andRonaldSNEE. Paci cGrove,CA:Brooks/ColeThomsonLearning, 2001, xvii + 526 pp., ISBN: 0-534-38158-8.</p><p>The business of this book is business. If you are looking for a book to teachgeneral introductory statistics, or even introductory statistics for economics stu-dents, then this is not your book.Many business statistics books, in my opinion,differ from the standard introductory texts only in that they are garnished withbusiness-related examples. Hoerl and Snee, on the other hand, offer a book com-pletely immersed in the business paradigm. So much so, in fact, that perhapsthey teach more business than statistics. The chief fault of this immersion, ifindeed it is a fault, is that the book seems more intent on convincing businesspeople to use statistics than it does on convincing statistics students to considerbusiness as a viable eld of application.</p><p>The rst four chapters provide an introductory overview and illustration ofbasic terms. Even though these chapters might seem lengthy, they do providedetailed and interesting case studies of real business problems, in the processvividly illustrating the application of statistical thinking. Useful techniquesstatistical and otherwiseare named andexamples of their applicationare given,but no details are provided. Acronyms abound. (Is Opportunity for Improve-ment really worthyof an acronym?) The result is somewhat akin to lookingovera statisticians shoulder as shewalks a manager throughher analysis. Themiddlechapters segue from consideration of speci c applications to more general con-cepts. Readers are introduced to problem solving tools (graphical summariesand organizational tools), statistical model building strategies, and experimen-tal design. The nal chapters discuss statistical theory and concepts, althoughalways these concepts are approached from the perspective of a business ana-lyst looking to solve a problem, not a student needing to understand a generalprinciple.</p><p>The strongbusiness slant is botha strength and a weakness. Business studentswill no doubt appreciate the lack of algebra and urn-problems. They might alsoappreciate the level of detail put into the case studies, which makes for trulyinteresting reading for the motivated student. The weakness of this approachis that those wishing to teach statistics as a cohesive and uni ed disciplineas opposed to a collection of toolswill be disappointed. Statistical tools andtests (and other problem solving tools) are treated in catalog fashion. Chapter 5presents each tool with brief sections titled purpose, bene ts, limitations,examples, procedure, variations,and tips.After seven pages of sketchingthe hypothesis-testingparadigm in Chapter 8, tests are presented in catalog form,broken down into two-paragraph chunks; the rst a description (This test is: : : used to compare a sample average to a hypothesized value. p. 355) and theseconda few sentences on theoretical assumptions.Formulas are not given, norare they described. Instead, readers are told that statistical software packagescalculate p-values based on the relevant statistical distributions (p. 355).</p><p>A conference held at theUniversity ofChicagosGraduate SchoolofBusiness,Making StatisticsMore Effective in Schools ofBusiness (Easton,Roberts, andTiao 1988),agreed with reform advocates (Roberts 1987)and recommended thatbusiness statistics courses show examples of real applications, reduce the em-phasis on formal theory and formal testing, and increase attention paid to topicssuch as time series, quality and productivity, sampling, and report writing. Inaddition, they reported a resounding recommendation: a need for statisticalcase studies. To their credit, Hoerl and Snees book takes these recommenda-tions very seriously. But I fear the de-emphasis on theory has gone too far. Forexample, nowhere is there a discussion of statistical independence. Without anintroduction to this most fundamental concept, how well can managers com-municate with their statisticians? Even worse, how much damage could theydo in an analysis without understanding that they have violated a fundamen-tal assumption? There are other places where a statistically na ve reader mightbe led astray. For example, the discussion on hypothesis tests says, In manycases : : : . we do not decide to formally test a hypothesis until after the dataare collected. In such cases, analysis of the data is what led us to consider thishypothesis(p. 352). True, but after that, I hoped for a warning about shing ex-peditions or post-hoc tests. Instead readers are merely warned that in such casesthe quality of the data is an important consideration. There are other places inwhich the book strays from statistical convention. For example, the average isvaguely de ned as the central value around which the process varies. (p. 12).A subsequent example illustrates how to calculate the sample average, but thisde nition, besides begging the question of what is meant by central, makes itdif cult for the student to place the sample mean in an inferential context.</p><p>This book makes heavy use of software, which I see as a strength. Shortintroductions are provided to Minitab, JMP, and Excel. I willingly concede that</p><p>Excel is in the business community to stay, but I dont like it. I would like tothink that my job as a statistics educator is to convince students that they willbe more successful if they do not use Excel. The authors use Excel for mostof their examples of basic statistics, but steer students away from Excel andrecommend Minitab or JMP for the more formal analyses. I wish they were alittle more emphatic; rather than present Excel as one of three possible choices,I would like to see it pointed out that Excel is not a statistical analysis packageand should not be used as one.Whether or not you think this is a goodbookwill dependonwho your students</p><p>are andwhat youd like them to get out of yourstatistics course. Studentswho arealready working as managers or who aspire to managerial positions will comeaway with an appreciation of the usefulness of statistics. But I fear that studentswho wish to function as statisticians in a business setting will be short-changed.</p><p>Robert GOULDUCLA</p><p>REFERENCES</p><p>Easton,G., Roberts, H. V., and Tiao,G. C. (1988),Conference Report,Journalof Business and Economic Statistics, 6, 247260.</p><p>Roberts, H. V. (1987),Data Analysis forManagers,TheAmerican Statistician,41, 270278.</p><p>Teaching Statistics, Resources for Undergraduate Instructors.Thomas J. MOORE (ed.). Washington,DC: TheMathematical Associationof America and the American Statistical Association, 2001, xii + 222 pp.,$31.95(P), ISBN: 0-88385-162-8.</p><p>As I started to read Teaching Statistics I jotted down two questions: Howwill this collection of essays change the way I teach my undergraduate statisticscourses? How will the essays in Teaching Statistics change the way nonstatisti-cians teach undergraduate statistics? I am pleased to report that the answers toboth questions are for the better!Teaching Statistics is published jointly by the MAA and the ASA within the</p><p>MAANotes series. MAA Notes volumes are not intended to be used as primarytexts, but they support undergraduate instruction in mathematics and, in thiscase, statistics.The essays and commentary collected and edited by Tom Moore in Teach-</p><p>ing Statistics are organized into six sections: Hortatory Imperatives, Teachingwith Data, Established Projects in Active Learning, Textbooks,Technology, andAssessment.Moores preface to the volume and the Section 1 (reprinted) essay titled</p><p>Teaching Statistics: More Data, Less Lecturing by George Cobb are worthreading, but they dont carry the speci c practical advice present in the middlesections of the volume. Moores goal for the volume is explicitly stated in thepreface: Teaching Statistics aims to be an instructors manual for statisticseducational reform for teachers of statistics at the undergraduate and secondaryschool levels. Sections 2 through5 of the volumeare lled with practical advicethat really does read like a user-friendly instructors manual.Notice that Moores objective does not distinguish between nonstatisticians</p><p>and statisticianswho teach statistics. In fact, nonstatisticianswho nd themselvesteaching statistics are the primary audience for this volume.Section 2 offers guidance and suggestions for incorporating real datasets,</p><p>projects, and case studies into statistics courses, and Section 4 provides essayscontaining advice on the selection of textbooksfor introductorystatistics coursesand also for mathematical statistics courses. The tone of the essays in Teach-ing Statistics is not dogmatic but is supportive, and it appears that great efforthas been made to avoid singling out speci c products and textbooks as beingexceptionally good or bad.What is remarkable aboutTeaching Statistics is the balance between overview</p><p>and philosophyversus advice on practical implementation. In Section 3 Estab-lishedProjects inActiveLearningand Section 5Technology,overviewessaysor excerpts from publishedmaterials are followed by commentary written by un-dergraduate statistics instructorswho are not the authors of the primary material.In fact, many of the commentators are not statisticians by training. For example,Michael Seyfried, a geometer from Shippensburg University of Pennsylvania,wrote the companion piece for Allan Rossmans Excerpts from WorkshopStatistics: Discovery with Data. The essay Using Graphing Calculators forData Analysis in Teaching was contributed to the Technology section by PatHopfensperger, a high school teacher from Wisconsin.</p><p>The American Statistician, May 2002, Vol. 56, No. 2 157</p><p>Dow</p><p>nloa</p><p>ded </p><p>by [</p><p>Lau</p><p>rent</p><p>ian </p><p>Uni</p><p>vers</p><p>ity] </p><p>at 2</p><p>2:00</p><p> 05 </p><p>Oct</p><p>ober</p><p> 201</p><p>4 </p></li><li><p>The commentaries provide genuine critique of the resources that are availableto statistics instructors. Naturally most of the comments are positive, but theexperiences are real, and obstacles and challenges are described along withglowing reports of successful use of resources and tools.I had hoped that the essay-commentary structure would extend through Sec-</p><p>tion 6 on Assessment, but it does not. The Assessment section consists of pre-viously published essays by Joan Gar eld and Beth Chance, two acknowledgedleaders in the eld of statistics education research. Their essays are insightful,but they lack the refreshing practical viewpoint of a non-statistician that accom-panies some of the essays in the earlier sections of Teaching Statistics. I wouldhave liked to read about the experiences of a nonstatistician or statistician butnot statistics education researcher trying to incorporate some of the assessmenttechniques and strategies proposed by Gar eld and Chance, because I mighthave learned more about what pitfalls to avoid and what successes to expect asI try to strengthen the assessment component of my own teaching.Teaching Statistics represents many voices in statistics education: leaders</p><p>of the reform movement, statisticians who are now established and respectedinstructors and education researchers, and nonstatisticians who are enjoyingtheir explorationsof the differences between statistics and mathematics. Thoughnot technical, the volume is educational for anyone who teaches introductoryand undergraduate statistics.</p><p>Thomas H. SHORTVillanova University</p><p>XploRe: Applications Guide.Wolfgang H ARDLE, Zdenek HLAVKA, and Sigbert KLINKE. Berlin:Springer-Verlag, 2000, xv + 525 pp., $79.00(P), ISBN: 3-540-67545-0.</p><p>XploRe: Application Guide is not just for XploRe users. Hardle, Hlavka, andKlinke have prepared this guide for advanced applied statisticians. There aredetailed discussions and many good references on both traditional and newlydeveloped data analysis methodologies. Hence readers can implement thesemethodologies in whatever statistical software or computer language that theychoose.There is an accompanying CD-ROM, which includes a (color) electronic</p><p>version of this guide in both HTML and PDF format, the XploRe statisticalsoftware, and all example code. Using these resources, beginningapplied statis-ticians can easily repeat analyses included in this guide, and modify the code toanalyze their own data. However, to get full bene t of this guide, readers shouldhave a strong background in general mathematics and mathematical statistics,some experience in general computing, and access to general guides for usingXploRe, such as Hardle et al. (1995, 2000a, 2000b, 2001). (One of these guideswas reviewed by Hilbe (2001).)There are 18 chapters, contributed by 27 writers. These chapters are grouped</p><p>into three parts:</p><p>Part 1: (Chapters 17) Regression Models. This part includes many gene...</p></li></ul>