Cal Poly, San Luis Obispo

October 1, 2000

Newsletter for the Section on Statistical Education

Volume 6, Number 3 (Special Issue, 2000)

Members of the ASA Section on Statistical Education

This is a special mailing to members of the Section to bring you up to date on outcomes of the Undergraduate Statistics Education Initiative and to solicit your comments on proposed Curriculum Guidelines for Undergraduate Programs in Statistical Science that may be forwarded to the ASA Board of Directors for consideration at their December meeting.

As many of you know, a Symposium on Undergraduate Education preceded JSM this year. The Symposium was well attended and included a thoughtful discussion of several position papers that were developed for the symposium. These papers have now been revised to reflect the discussion of issues raised at the Symposium. These revised papers form the foundation of the enclosed curriculum recommendations.

The Section on Statistical Education has been asked to endorse the enclosed recommendations and forward them to the ASA Board of Directors. At our Section business meeting in Indianapolis, the Executive Committee was authorized to conduct this special mailing, collect responses from the Section membership, and then decide whether or not to forward the curriculum guidelines to the ASA Board of Directors.

Time is of the essence -- should we make the decision to endorse and forward the guidelines, we would like to be able to have it considered at the December Board meeting. **Please read the enclosed curriculum guidelines and send your comments and concerns to me by October 23 if you would like to have them considered by the Section Executive Committee when making its decision.**

To send a comment or concern, you may send email to me at rpeck@calpoly.edu (please put "USEI comment" as the subject line), fax to me at 805-756-1670, or send by mail to Roxy Peck, College of Science and Mathematics, Cal Poly, San Luis Obispo, CA 93407.

I enjoyed seeing many of you at the Stat Ed sessions and meetings at JSM in Indianapolis this year, and look forward to hearing from you on this important issue.

Thanks

Roxy Peck, Chair

Section on Statistical Education

**The supporting papers can be found on the ASA website at ****www.amstat.org/meetings/jsm/2000/ symposium.html**

Curriculum Guidelines for Undergraduate Programs in Statistical Science

The American Statistical Association endorses the value of undergraduate programs in statistical science, both for statistical science majors and for students in other majors seeking a minor or concentration. This document provides guidelines for development of curricula for such programs. The ASA Center for Statistical Education can provide more detailed recommendations and examples of existing programs in a variety of institutions.

Principles

Undergraduate programs in statistics are intended to equip students with quantitative skills that they can employ and build on in flexible ways. Some students may plan graduate work in statistics or other fields, while others will seek employment after their first degree. Programs should be sufficiently flexible to accommodate varying goals. Undergraduate programs are not intended to train professional statisticians, though some graduates may reach this level through work experience and/or further study.

Institutions vary greatly in the type and intensity of programs they are able to offer. The ASA believes that almost all institutions can provide a level of statistical education that is useful to both students and employers. We encourage flexibility in adapting these guidelines to institutional constraints. In many cases, statistics minors or concentrations for quantitatively oriented students in fields such as biology, business, and behavioral and social science may be more feasible than a full statistics major.

Undergraduate statistics programs should emphasize concepts and tools for working with data and provide experience in designing data collection and in analyzing real data that go beyond the content of a first course in statistical methods. The detailed statistical content may vary, and may be accompanied by varying levels of study in computing, mathematics, and a field of application.

Though statistics requires mathematics for the development of its underlying theory, statistics is distinct from mathematics and uses many non-mathematical skills; thus, the curriculum *must* be more than a sequence of mathematics courses. It is essential that faculty trained in statistics and experienced in working with data be involved in developing statistics programs and in teaching or supervising courses required by the programs.

Skills Needed

Effective statisticians at any level display a combination of skills that are not exclusively mathematical. Programs should provide some background in these areas:

**Statistical**-- Graduates should have training and experience in designing studies (including practical aspects), in exploratory analysis of data by graphical and other means, and in a variety of formal inference procedures.**Mathematical**-- Undergraduate major programs should include study of probability and statistical theory along with the prerequisite mathematics, especially calculus and linear algebra. Programs for non-majors may require less study of mathematics. Programs preparing for graduate work may require additional mathematics.**Non-mathematical**-- Graduates should be expected to write clearly and speak fluently and to have developed skills in collaboration and teamwork and in organizing and managing projects. Academic programs often fail to offer adequate preparation in these areas.**Computational**-- Working with data requires more than basic computing skills. Programs should require familiarity with a standard statistical software package and should encourage study of data management and algorithmic problem-solving.**Substantive area**-- Because statistics is a methodological discipline, statistics programs should include some depth in an area of application.

The approach to teaching the following topics should:

- emphasize
**real data**(not merely realistic data) and authentic applications. - include experience with statistical
**computing**. - encourage
**synthesis**of theory, methods, and applications. - offer frequent opportunities to develop
**communication**skills.

Statistical Topics

- Statistical theory (distributions of random variables, point and interval estimation, hypothesis testing).
- Graphical data analysis methods.
- Statistical modeling (simple, multiple, and logistic regression; categorical data; diagnostics).
- Design of studies (e.g., random assignment, replication, blocking, analysis of variance, fixed and random effects, diagnostics in experiments; random sampling, stratification in sample surveys; data exploration in observational studies).
- Statistical software appropriate to a variety of tasks.

Non-Mathematical Topics

- Effective technical writing and presentations.
- Organizing for data collection and management.
- Teamwork and collaboration.

Mathematical Topics

- Calculus (integration and differentiation) through multivariable calculus.
- Probability (emphasis on connections between concepts and their applications in statistics).
- Applied linear algebra (emphasis on matrix manipulations, linear transformations, projections in Euclidean space, and eigenvalue/eigenvector decomposition).

Computational Topics

- Programming concepts, data management.

**Electives** -- There are many electives that might be included in a statistics major. Since resources will vary among institutions the identification of what will be offered is left to the discretion of individual units.

**Practice** -- When possible the undergraduate experience should include an internship or a senior-level "capstone" course or a consulting experience of some kind or a combination of these. These and other opportunities to practice should be included in a variety of venues in an undergraduate program.

The core of a minor or concentration in statistics should consist of:

- General statistical methodology (statistical thinking, descriptive, estimation, testing, etc.);
- Statistical modeling (simple and multiple regression, diagnostics, etc.);
- Exposure to professional statistical software.

The number of credit hours for minors or concentrations will depend on the policies set by the academic units involved. Additional topics to complete the required number of credit hours could be chosen from some non-exhaustive list (e.g., mathematical statistics, design of experiments, categorical data analysis, time series, Bayesian methods, probability, database management, a capstone experience). Courses from other departments with significant statistical content might be allowed to count toward a statistics minor or concentration, though the content of such courses must differ substantially from the others.

Additional Information

The ASA's Center for Statistical Education (see http://www.amstat.org) has available more detailed recommendations on statistics programs, along with a list of model programs. These materials have been developed and are maintained by the Section on Statistical Education, in conjunction with other sections and committees of ASA. Those considering new or revised undergraduate statistics programs may contact the Center for Statistical Education for further information.

Comments and suggestions for the improvement of the newsletter are most welcome, and should be sent to a member of the editorial board.

**Terry King**

Department of Mathematics & Statistics

Northwest Missouri State University

Maryville, Missouri 64468-6001

(660) 562-1805

Fax: (660) 562-1188

tlking@mail.nwmissouri.edu

**Joan Garfield**

Department of Educational Psychology

University of Minnesota

332 Burton Hall

128 Pillsbury Dr., S.E.

Minneapolis MN 55455

(612) 625-0337

Fax: (612) 624-8241

jbg@maroon.tc.umn.edu

**Tom Moore**

Department of Mathematics and Computer Science

Grinnell College

Grinnell IA 50112

(515) 269-4206

Fax: (515) 269-4984

mooret@grinnell.edu

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