Newsletter for the Section on Statistical Education
Volume 4, Number 1 (Winter 1998)

As I begin my term as chair of the section, I'd like to take this opportunity to thank the people who have worked so hard to create this newsletter for us. Carol Blumberg, Joan Garfield, and Tom Moore have been the joint editors of the newsletter now for three years. They have seen it off to a great start, and have taken reader comments to heart to make changes so that the newsletter better serves our diverse readership. My thanks to all of you for this service to our section. As of this issue we welcome Terry King to the editorial staff as our new "lead" editor. Terry will be working with Carol, Tom, and Joan to keep you informed of section news.

Newsletter for the Section on Statistical Education
Volume 4, Number 1 (Winter 1998)

Newsletter for the Section on Statistical Education
Volume 4, Number 1 (Winter 1998)

Newsletter for the Section on Statistical Education
Volume 4, Number 1 (Winter 1998)

For the past three years, Carol Joyce Blumberg has served as the "lead" editor of this newsletter. She, along with the other editors, have solicited articles, and helped prepare them for publication. But in addition, she has also assumed the responsibility as the lead editor, including the responsibility for the production and mailing of the newsletters. Much of the success of this newsletter is due to the hard work that Carol has exerted on behalf of the Section on Statistical Education. Although she has decided to step aside from the "lead" editor position, Carol will continue to serve as one of the editors of this publication. Her knowledge and expert attention to this newsletter will continue to be a benefit to this section.

Newsletter for the Section on Statistical Education
Volume 4, Number 1 (Winter 1998)

Newsletter for the Section on Statistical Education
Volume 4, Number 1 (Winter 1998)

Newsletter for the Section on Statistical Education
Volume 4, Number 1 (Winter 1998)

Our section has three invited sessions allotted to us for the JSM in Dallas 1998.

Carol Blumberg, Winona State University, has organized one of our invited sessions addressing statistics needs for students with disabilities. She has invited two excellent speakers to her session. Carol Preston is with the National Oceanic and Atmospheric Administration. She will speak on ACCESS THROUGH SCIENCE: OPENING THE PROFESSION TO STUDENTS WITH DISABILITIES. For many reasons, disabled students are actively discouraged from entering into careers that offer challenges and opportunities for personal growth. Several strategies, including mentoring and recruitment that can lead to optimizing opportunities for disabled students will be presented. Sue Ann Kroeger, University of Minnesota, will present MAKING STATISTICS COURSES MORE ACCESSIBLE TO STUDENTS WITH DISABILITIES, from the legal requirements to classroom technological and non-technological issues.

PLUGGING IN -- CONNECTING TECHNOLOGY TO THE CLASSROOM has been organized by Tom Short, Villanova University. Bill Finzer, Key Curriculum Press, will discuss visualization techniques, the importance of dragging in Dynamic StatisticsO software, and on the role of user-constructed measures in the learning of statistics, including results of research from the DataSpace project. VIRTUAL LABORATORIES IN PROBABILITY AND STATISTICS is the topic of Kyle Siegrist, University of Alabama in Huntsville. Kyle's project consists of interweaving hypertext, graphics, interactive Java applets, and data sets in web-based modules that cover the core topics at the undergraduate level. Tim Hesterberg, MathSoft/Statistical Sciences, will talk about how computer simulation and bootstrapping let students gain experience with and intuition for key ideas in statistics and probability. In particular, bootstrapping frees us from the requirement to teach inference only for statistics for which simple formulas are available - we can bootstrap robust statistics like the median as easily as the mean. Discussant responsibilities are in the very able hands of Robin Lock, St. Lawrence University.

With the advanced placement program in statistics finishing its second year by the time JSM Dallas occurs, Dex Whittinghill, Rowan University, has asked leaders in the program to speak on THE ADVANCED PLACEMENT STATISTICS EXAMS: THE FIRST TWO YEARS. Rosemary Roberts, Bowdoin College, will briefly describe the content of AP statistics, and then discuss examples of questions from the first two exams. Chris Olsen from George Washington High School in Cedar Rapids, Iowa, will present WHAT'S A RUBRIC? SCORING THE OPEN ENDED QUESTIONS. Since student responses are many and varied, the process is not without some surprises and perplexities; examples will be shared, and sympathy elicited! Dick Scheaffer, University of Florida, will follow with WHY DID PAT GET A 3? MAKING THE GRADE IN AP STATISTICS. What happens at a reading of free-response questions will be discussed, along with what happens after the reading to turn raw scores into the grades received by the students. An interesting part of the process involves a comparability study to see how AP scores relate to college grades in a similar course. Paul Velleman, Cornell University, has agreed to be the discussant.

Special thanks to Carol, Tom, and Dex for their work in organizing three very interesting sessions.

Newsletter for the Section on Statistical Education
Volume 4, Number 1 (Winter 1998)

As organizer of the roundtable luncheons for the Section on Statistical Education at the 1998 JSM in Dallas I am writing to solicit your ideas and ask for volunteers to lead the roundtable discussions. A typical roundtable luncheon consists of about ten people at a table with a leader who has been chosen and announced in advance. If you were that leader, you would lead a discussion on your topic with those at your table. Some handouts are usually helpful. Your basic responsibility would be to get the discussion started, let all the members at your table who want to contribute comments have the chance to do so, keep the discussion moving and on target, and summarize the discussion at closing, if appropriate. By the way, lunch is free for discussion leaders!

The list of roundtable discussion topics for the JSM in Dallas will include:

1. What Should We Do Instead of a Math Stat Course? David Hull, Ohio Wesleyan U.
2. An Intuitive Approach to Including More Nonparametric Methods in the Introductory Statistics Course. Douglas A. Wolfe, Ohio State University.
3. Activity-Based Mathematical Statistics. Karla Ballman, Macalester.
4. AP Statistics (subtopic to be determined). Katherine Halverson, Smith College

We would also like to include discussions on the following topics: assessment, technology, course projects, QL (Quantitative Literacy) and the K-12 curriculum, and university-wide data analysis or quantitative literacy requirements. If you are interested in being the luncheon discussion leader for any of these topics, or any other topic that you think is interesting in the area of statistical education, please contact me.

Newsletter for the Section on Statistical Education
Volume 4, Number 1 (Winter 1998)

Academic journal publishing is changing dramatically. Publishers are beginning to exploit the Internet as a medium for delivering journals. Before, only scattered journals were offered electronically, usually with only the recent issues online. Now electronic publishing seems to be settling into a pattern: a vendor offers a World Wide Web interface to a collection of journals, then the campus library arranges for an institutional-wide license for the entire campus network. A user on campus then simply accesses the vendor's Web site, and downloads the article for reading or printing. This allows publishers to generate revenue despite the electronic medium.

One vendor that looks particularly promising is called JSTOR. It offers the complete run of a journal, not just the last few years. It offers the articles in Portable Document Format (PDF) which is created by the Adobe Acrobat application and can be read by the freeware application called Acroread. The articles are full image, including illustrations and the original page numbers for citing references. JSTOR charges libraries a large initial payment, and then a yearly maintenance fee. For that, the campus has access to all of the journals housed on their server.

Newsletter for the Section on Statistical Education
Volume 4, Number 1 (Winter 1998)

Every university professor has experienced the frustration that Dr. Richard Scheaffer felt. While teaching at the University of Florida he became convinced that a professor cannot start from scratch in teaching a three-hour introductory statistics course and expect the student to end up with a very good understanding of the subject. There were simply too many things to overcome, including a fear produced, in part, by ignorance of data collection and analysis.

But rather than throw up his hands and "just make the best of it," Dr. Scheaffer began on a mission, of sorts. And the mission went beyond the walls of the universities--it went all the way to kindergarten classrooms and to local industries in cities. He became one of the leaders of the movement known as quantitative literacy.

Scheaffer began his involvement rather by accident. In 1980, he was appointed to a joint committee of the American Statistical Association and the National Council of Teachers of Mathematics. Under the leadership of Jim Swift, Dr. Scheaffer became involved in changing the way the system treated statistics.

While most mathematics textbooks rarely even talked about statistics, the group began writing material that could be introduced at the junior high and high school levels, emphasizing data analysis rather than probability or theory. Simultaneously, they began workshops for teachers in the field. Diann Resnick, a high school teacher in Houston, was one of the workshop attendees. Her praises for the projects and Dr. Scheaffer are enthusiastic.

"It used to be that statistics was never mentioned in the textbook," she said. "Now it is regarded as an integral part of the program. Quantitative literacy is now into elementary schools and high schools. And he is very, very much an integral part of it. He is a well-known statistician, a highly respected professor, and he is really interested in statistical education. And what is so extraordinary about him is that you could ask him a question and no matter how elementary it was, he would always take time to answer it."

The recipient of the Teaching Improvement Program Award in 1995, Dr. Scheaffer has been involved with a series of projects that included workshops, papers, committees and lectures to help schools better prepare students for statistics. The newest such program has been a successful push to get statistics into the Advanced Placement (AP) Program in high schools. The AP program gives students the opportunity while still in high school to take courses with content outlined and approved by a committee of high school teachers. They then may opt to test over the course and gain college credit. Last year, after years of preparation, the AP Statistics Course was offered. During the first year, 7600 students took the exam. Although not every student who takes the course finishes with the exam, some 10,000 students are expected to participate in the exam stage this year.

"The schools are in the through of change," Dr. Scheaffer said. "It is making math more realistic and something they can sink their teeth into."

Dr. Scheaffer noted that while the good side of the coin was that more and more high schools are participating in the effort to make students at an earlier age more quantitatively literate, the bad side might be that perhaps colleges are not convinced that it is an idea worth supporting with more time and money.

Several of the goals for quantitative literacy have been met, he believes. Part of the aim was to help ease the fear college students had about statistics courses they would be required to meet for their major. Earlier introduction to the subject allows them a greater chance to succeed. But another concern Dr. Scheaffer had was for high school students who would never go to universities. He felt that the elementary introduction of data analysis would eventually enhance their knowledge, producing people who would reason correctly with data and see its usefulness in their everyday life.

And while he is very excited about the results of these efforts, Dr. Scheaffer still has a vision for more. He is concerned, for example, that the workshops and papers only reach a limited number of teachers. Almost all the work has involved supplemental education and in-service with teachers who have already been in the field for awhile. To really meet the goals of quantitative literacy, Dr. Scheaffer believes that an effort should be made at colleges of education to train teachers there how to make the students more aware and less afraid of statistics.

Ms. Resnick agreed. "One of the problems is a funding problem," she explained. "And another is that teachers are trained in older methods. They are fine mathematics teachers, but they are not trained (in statistics) and are, thus, unprepared."

Another solution Dr. Scheaffer sees is the involvement of local industries. "Unless teachers and students in the schools see a payoff for the courses," he said, "the programs won't last. Part of the payoff is to see that it is actually used in the industry...something that will help them on the job.

Scheaffer added, "school districts are demanding more and more of their schools. They want to know what the payoff is. If a program in AP Statistics doesn't look like it is paying off for students, it won't last.

"So we need to encourage universities to look at their colleges of education and we need to encourage local industrial people to run courses and to keep this alive and expanding in schools. They need to see that it is something useful. The marketplace seems to have clout."

As a teacher himself, Dr. Scheaffer noted that active learning that involves students' own experiences was the very spirit that was tried in the AP program. That type of learning, he said, helps more than anything in teaching students and in motivating their attitudes.

He noted, as an aside, that some of his own students have come to him complaining that "you didn't teach me anything. I learned it all on my own." His teaching evaluations have even dipped as he gave more laboratory experiences. But his modesty and his great desire to help others are reflected in his response to those comments.

"I've given them the opportunity to learn and they learned it on their own," he said. "And that's fine with me."

Newsletter for the Section on Statistical Education
Volume 4, Number 1 (Winter 1998)

When I first walk into my Introductory Statistics courses, I have two main goals: give the students a flavor for the course and introduce them to the practice of statistics through examples. I don't feel giving a simple definition of "statistics" is meaningful, and instead want them to discover what the subject involves through recent examples of uses and misuses. I also want them to know from the beginning that this course won't be only about number crunching, but will also emphasize reading numerical discussions, writing technical arguments, and reasoning statistically. To accomplish these goals, first I present a series of myths to the students and then we discuss some more realistic viewpoints of the course. Then I present a series of misuses, mostly from the news. By having students identify the errors in the arguments, they begin to develop their own statistical intuition and understanding of statistical practice. Finally, to ensure they believe that not all statistics are lies I present examples of a few effective uses.

Examples of Myths Students Have Entering the Course

To give students an idea of what I will expect from them in the course, I discuss a series of myths they may hold entering the course:

1. Statistics is a math course.
2. It's completely impossible for me to get a good grade for this course.
3. This course will be a cakewalk.
4. I can continue to cram for an exam the night before.
5. Statistics is memorizing formulas.
6. There is usually only one right answer.
7. The teacher is going to physically harm me if I ask a stupid question.
8. Statistics is not interesting and I will never use it.

I try to emphasize to the students that since computers can now do the numerical calculations so well, our role in studying statistics has changed to being able to tell the computer the right thing to do and then interpreting the output. I also stress that students will need to be able to justify their answers since multiple interpretations are quite possible. (A good example here is two different newspaper headlines based on the same study with opposite implications.) I also try to convey that statistics will not only be important no matter what career they choose, but also just for evaluating information in the newspaper. To study the material, I encourage them to approach it like a foreign language: immersing themselves in the use of the terms, and constantly practicing "talking statistics" with other students. Also, since the students taking my course are so diverse, I encourage them to work with others to share their distinct perspectives. Finally, I tell them my first goal of the course is for them to examine statistics with a critical eye (instead of accepting whatever numbers they hear) and to become intelligent consumers of statistical arguments.

Examples of (Mis)uses of Statistics

I present the following points on overheads and have the class explain to me how they feel about the statements - if they feel they are effective uses of information and convincing arguments. Most of these examples are borrowed from Chapter 3, Section 4 of Statistics: Concepts and Controversies by David Moore, 3rd edition. Moore has an excellent discussion of how to "look at data intelligently". I also try to accompany these points with recent newspaper headlines illustrating the same misuses.

1. True cigarettes have 5 milligrams less tar.
2. Anacin contains more of the ingredient doctors recommend most./Doctors specify Bufferin the most over other leading brands.
3. Dr. Bragg claims his patients have 50% fewer cavities.
4. GM advertises that JD Power picks the Lumina Coupe as the most trouble-free car in its class.
5. Science (1976): People over 65, now numbering 10 million, will number 30 million by the year 2000, and will constitute an unprecedented 25 percent of the population.
6. Dr. Fudge took measurements for 20 animals, recording the number of successful trials. He reported percentages of 53, 58, 63, 46, 48, and 67.
7. The Alabama Development Office reports that the state has attracted 422,657 new industrial jobs in the past 25 years.
8. The unemployment rate is 7.1%.
9. In 1989, 5326 drivers 65 years of age and over were involved in fatal accidents. In contrast, only 2900 drivers aged 16 and 17 had fatal accidents. Thus, young people are safer drivers.
10. South Dakota has the highest average SAT score.
11. The Investment Company Institute claims a $10,000 investment in 1950 in an average common stock mutual fund would have increased to $113,500 by the end of 1972.
12. Schick Super chromium razor blades commercial: A group of barbers shave with the same blade, one after the other. The 12th, 13th, 15th, and 17th men to use the blade were interviewed and said the shave was satisfactory.

Discussing this list enables students to develop a list of questions to ask when evaluating any numerical argument: What comparisons are being made? Is the information complete? Are the numbers plausible? Are the definitions clear? Are the right numbers being looked at? What is the source of the information? How was the sample selected? Does the conclusion follow? Are the generalizations valid? I remind them how even the experts have made some serious mistakes (Challenger Accident, Dewey Defeats Truman) and such misuses of statistics should not be taken lightly. Good Uses of Statistics To reassure the students that there are plenty of good uses of statistics, I cite some recent studies that are quite informative. I also share some projects I've been involved with (motivated by Bentley, D. (1994), "My First Day's Lectures: Past and Present" presented at Joint Statistical Meetings) to show them how accessible the questions are to them and the broad variability of disciplines asking the questions. For example, I tell students about my recent work with projects involving

1. A NCAA survey on UOP student interests
2. Availability of pharmacists, with and without principal care providers (with a Pharmacy professor)
3. How are housing prices affected by proximity to toxic waste sites (with a Business School professor)?
4. Does a visual, interactive explanation of statistical concepts improve student learning? (with R. delMas, J. Garfield, U Minn.)
5. How to decide which books should be ordered for the university library (with Library Sciences faculty)
6. Identifying age and motivation when students choose their college major (with Engineering faculty)

I have found these ideas effective at motivating student interest in the course from day one, as well as aligning their expectations of the course with my own. These ideas are reinforced by their first homework, to identify and evaluate uses of statistics in "the news", and throughout the course.

Newsletter for the Section on Statistical Education
Volume 4, Number 1 (Winter 1998)

How might statisticians help high school science fair participants? It is suggested that statisticians should: (a) assist high school students with statistical design and data analysis techniques that will enhance the quality of their research projects; and (b) provide students with information about the relationship between winning statistics special awards and winning specific science-category awards. These two activities are essential for "selling" science fair students on the value of using quality statistics in their research projects.

Each year Amstat News includes descriptions of the statistics special award winners at the International Science and Engineering Fair (ISEF).

These projects use statistical design and analysis techniques that are beyond the scope of most first high school non-calculus-based statistics courses. Topics mentioned include two-factor ANOVA, multiple comparisons, Newman-Keuls pairwise comparisons, factor analysis, response surface models, and many uses of multiple regression. At the 1997 ISEF the statistics special awards winners used two-factor ANOVA, Scheffe Multiple Comparisons, response surface models, and multiple regression models.

Those who have judged in local and state fairs can observe that those projects that use the combined power of statistics and computer software frequently receive high recognition. Statisticians can use these examples to motivate science fair students to learn to use advanced analysis techniques to increase their chances of recognition for a statistics special award.

Casual observation of the ISEF statistics winners' names and their recognition in specific science-fair categories, indicates possible association between statistics "winners" and other-category "winners". Statisticians, above all, should use their own data analysis competence to study the association between the use of "quality" statistics in projects and receipt of specific science-category awards. This should be done for not only the ISEF but for each local and state science fair for which there are statistics special awards. While "causation" may not be possible it is appropriate to study "association". Consider two extreme scenarios for a science fair:

(a) Each statistics special awards winner wins FIRST PRIZE in their own specific science category. In this situation it might be argued that "being a statistics winner is 'HIGHLY associated' with winning in a specific science category". (b) Each statistics special awards winner receives no recognition in their own specific science category. In this situation it might be argued that "being a statistics winner has a NEGATIVE relationship with winning in a specific science category".

The actual situation is somewhere between these two extremes and might even indicate NO relationship. Statisticians should be able to present actual science fair results in some form, graphically or numerically, that exhibits the extent of association.

In the 1997 International Science and Engineering Fair at Louisville, KY, the FIRST AWARD winner in Statistics, appearing for her fourth straight year, received many awards in non-statistics categories. The project used a well-designed two-factor ANOVA. Her project received the FIRST Grand Award and Best of Category in the Behavioral and Social Science category, a special award from the American Psychology Association, an INTEL Achievement award, the Weissman Institute award and several offers of college scholarships. The direct cash awards totaled over $10,000. While it may not be possible to claim that the use of statistics "caused" the other recognitions, it seems reasonable to say that her experimental design, analysis and presentation of the statistical results "enhanced" the quality of her project.

The SECOND award winner in Statistics received FOURTH Grand Award in the Behavioral and Social Science category. The project used response surface models. The THIRD winner in statistics, which involved the use of multiple regression models, received Fourth Grand Award in the Mathematics category.

At the Alamo Regional Science Fair in San Antonio TWO of the top four statistics projects were among the SIX overall projects that qualified to attend the International Fair in Louisville.

It is important for ASA chapters to continue their efforts toward the Adopt-a-School program, QL activities, and AP-Statistics. However, those statisticians who serve as science fair mentors and judges should direct the highly-talented science-fair students toward statistical design and analysis procedures that can take their research projects to a higher level.

Joe Ward may be contacted at:

Newsletter for the Section on Statistical Education
Volume 4, Number 1 (Winter 1998)

Origin and Early History of SPAIG. The last several years have seen a growing interest in strengthening partnerships between academe, industry, and government.

In August 1992, Ron Iman suggested joint meetings to the representatives of statistics programs in academics (Academic Program Representatives) and the representatives of statistical groups in industry and government (Corporate Member Representatives), at the annual American Statistical Association meeting.

In August 1993, the two groups began holding joint meetings to define areas of mutual interest. A beginning was identification of industry and government internship opportunities, which are now published in Amstat News by Jim Rosenberger of Penn State University.

In August 1994, Ron Iman, then President of the American Statistical Association, challenged the Corporate Member Representatives and the Academic Program Representatives to form a strategic partnership, which was named the Statistics Partnership among Academe, Industry, and Government (SPAIG). Each group was charged with development of vision statements and recommendations for achieving those visions, and reporting on the results at the American Statistical Association meetings in Orlando, in August 1995. Leadership for these two groups was provided by Bill Parr of the University of Tennessee and Bruce Rodda of Schering-Plough Research Institute

In July 1995, Roxy Peck organized and hosted a three-day conference to develop cases via joint academic - industry/government work. This conference was held at Cal-Poly, San Luis Obispo. This conference was sponsored by the National Science Foundation and brought together representatives from academe, industry, and government to form 22 case-writing partnerships between individuals in academe and industry/government. Faculty for this conference were Ron Iman of Southwest Technology Consultants, Bill Parr of the University of Tennessee, Dick Gunst of Southern Methodist University, and Bob Mason of Southwest Research Institute.

In August 1995, at the joint meeting of the Academic Program Representatives and Corporate Member Representatives, both groups presented their draft vision statements, recommendations for achieving those visions, and related issues and concerns. The two sets of vision statements and recommendations were remarkably similar. To accelerate the progress of harmonizing the visions and recommended action items, a joint subcommittee of three Academic Program Representatives and three Corporate Member Representatives was formed to:

1. Further evaluate and refine the vision statements and recommended action items.
2. Identify action items that could be implemented quickly and define a strategy to accomplish them.
3. Organize a two-day symposium in the first half of 1996 focused on these vision statements and action items.
4. Organize an invited session annually at the American Statistical Association meetings on the SPAIG initiative.

The members of this joint committee were: Ron Bosecker, U.S. Department of Agriculture; Thomas Gerig, North Carolina State University; David Marx, University of Nebraska; William Parr, University of Tennessee; Bruce Rodda, Schering-Plough Research Institute; and Susan Schall, DuPont Engineering.

A planning meeting was held, involving twelve representatives from academe, industry and government, on August 23 and 24, 1996, in Gatlinburg, Tennessee. The purpose of this planning meeting was to plan a larger conference, to which representatives of many major organizations employing statisticians in academe, industry and government would be invited, for the purpose of furthering partnership efforts. The SPAIG Conference. Based on the work done at the Gatlinburg planning meeting, the Statistics Partnerships among Academe, Industry, & Government (SPAIG) Workshop was held in Raleigh, NC on May 30-31, 1997. Eighty senior-level statisticians from academe, industry, and government attended. Participation by statisticians from academe and industry/government organizations was roughly equal.

Bob Starbuck (Wyeth-Ayerst) opened the workshop by discussing the opportunities that could be achieved by better and more widespread partnering relationships, including expanding and improving the use of statistics; increasing the value of statistics to society; improving the educational experience of students; improving the career decision process and outcomes; increasing communications among all statisticians; enabling greater awareness of each other's needs, issues, and concerns; improving the self-image of statisticians; making statistics a more rewarding profession; and ensuring that statistics is a growth field.

Ron Iman (Southwest Technology Consultants) provided an overview of partnering models and the great success that partnering has had and is having in the semiconductor industry. Ron presented several partnering models and also provided data that show that well over 90% of new Ph.D. statisticians are employed in industry and government rather than in academe, thereby emphasizing the importance of training statisticians to achieve the skills required to be successful in industry and government.

G. Rex Bryce (Brigham Young University), Dean Isaacson (Iowa State University), John Spurrier (University of South Carolina), and Bob Hogg (University of Iowa) followed with success stories of partnering with industry through internships, partnering with other departments on campus, a capstone course in statistics, and partnering with engineering, respec tively. The needs of industry, government, and academe were then expressed by Gene Meieran (Intel Corporation), Cynthia Clark (US Bureau of Census), and Dan Solomon (North Carolina State University).

Ron Snee (NYNEX) presented the keynote speech. He pointed out that global competition and advances in computer technology are forcing changes in how US corporations are managed. The result is an expanding role for statistics and statisticians. This expansion also results in changing roles for statisticians because the needs of industry have changed. In short, the needs are managerial as well as technical. The managerial needs are less well defined and are challenging to satisfy.

Ron concluded by saying that partnering with industry is needed to effectively and efficiently identify how to align statistical education and research with these new needs. Personal change is required to take advantage of the expanding role. The group was reminded that those who do not respond to their changing world will have decreasing influence in their world. As George Bernard Shaw noted, "If you can't change your mind, you can't change anything."

Workshop participants were asked to identify the consequences of maintaining the status quo; i.e., doing nothing to improve the partnering relationships between academe and industry/government. A Pareto analysis of their conclusions led to these conclusions:

There was clearly a strong belief among those present that the role of statistics and statisticians will diminish if the status quo is maintained. Negative consequences would include fewer students choosing statistics as a career, decline or elimination of statistics departments, and fewer employment opportunities for statisticians.

Participants were then divided up into four groups to address the following four topics:

1. Short-term visits between statisticians in academe and industry/government,
2. Long-term collaboration between academe & industry/government statisticians,
3. Partnering with other disciplines, and
4. Industry/government input into academic statistical programs & curricula.

Andy Kirsch (3M Co.), Lynne Hare (National. Inst. of Standards & Technology), Roger Hoerl (General Electric), Dean Isaacson (Iowa State University), and Susan Schall (Dupont Engineering Polymers) assisted individual groups in the use of affinity mapping, interrelationship digraphs, and multi-voting tools to:

1. Identify why partnering was not occurring or not occurring as much as it could be
2. Group identified causes into related clusters (root causes)
3. Determine which root causes exerted the most influence on other root causes
4. Identify solutions for addressing the most influential root causes, and
5. Determine which solutions would be focused on to develop specific action plans.

This process led to the development of the following specific action plans.

Specific Action Plans

1. Create and disseminate contact lists to facilitate partnering between academe, industry and government
2. Conduct I/G salary survey
3. Design and implement regional SPAIG Workshops
4. Include I/G statisticians on academic advisory boards and review teams
5. Create "How To" manual on short-term visits between A/I/G statisticians
6. Incorporate collaboration into A/I/G job descriptions and job goals
7. Establish an ASA Award for Partnership
8. Assess past and current long-term collaboration between A/I/G and develop a process for long-term collaboration
9. Survey collaboration with other disciplines
10. Collect and edit partnering success stories
11. Manage the SPAIG web site.

Volunteers participating in these initiatives have developed detailed action plans with milestones, responsibilities, and measures of success.

As action plans are implemented, progress reports will be provided at sessions on SPAIG at the Joint Statistical Meetings and in Amstat News articles.

Newsletter for the Section on Statistical Education
Volume 4, Number 1 (Winter 1998)

The Boston Chapter of the American Statistical Association is planning a one-day conference on Technology in Statistics Education. Speakers include Paul Velleman from Cornell University who will give the keynote address; Robin Lock from St. Lawrence University who will talk about Internet and WWW resources for statistics courses; and Joseph Aieta and William Rybolt from Babson College who will speak about innovative uses of technology in statistics courses. In addition, participants will be able to attend up to four sessions on software packages (or the TI-83 calculator) for statistics courses. In each half-hour session an expert user will show how the package (or calculator) he or she is demonstrating can be used to solve the same preassigned problem, will highlight the strengths and weaknesses of the package, and will answer questions. Demonstrations will include the TI-83 calculator; computational software such as MINITAB, JMP, Data Desk, Systat, Stata, SAS, Splus, and SPSS; conceptual software such as ActivStats, Constats, ExplorStats, and StatConcepts; and more general software such as Excel and Mathematica. (The final list of demonstrations will depend on the interests of participants based on a pre-conference questionnaire and the availability of software experts.)

If you would like to receive a registration packet, please contact

Newsletter for the Section on Statistical Education
Volume 4, Number 1 (Winter 1998)

The Department of Statistics at the University of South Carolina will host an NSF sponsored workshop to train college faculty on the use of "hands-on" laboratory exercises in elementary statistics. The workshop will be held on June 16-20, 1998 and is limited to 24 participants. John Spurrier and Lori Thombs will lead the workshop.

Funding is available to support local expenses of all selected participants. In addition, eight stipends of up to $250 are available to help defray travel expenses for participants from resource poor colleges.

Participants will take part in nine laboratory experiences which illustrate important concepts of applied statistics. In addition, participants will discuss strategies for leading a laboratory session, using student teams, having students produce written reports, obtaining equipment, scheduling, constructing new laboratory exercises, generating enrollments, and training lab assistants.

Participants are expected to use at least two experiment-based exercises in an elementary statistics class during the 1998-99 academic year.

Newsletter for the Section on Statistical Education
Volume 4, Number 1 (Winter 1998)

Newsletter for the Section on Statistical Education
Volume 4, Number 1 (Winter 1998)

A Chance Workshop will be held at Dartmouth College July 7-11, 1998. Chance is an innovative introductory quantitative literacy course which teaches basic concepts from probability and statistics in the context of current issues in the news such as medical trials, opinion polls, weather prediction, and the use of DNA fingerprinting in the courts. The aim of the course is to make students better able to understand and critically analyze chance news. The Chance course makes significant use of group learning and activities. The workshop will allow college teachers to experience a brief version of the Chance course and to learn how it is taught. The workshop is supported by the NSF.