ISSN 1069-1898

 An International Journal on the Teaching and Learning of Statistics

## JSE Volume 16, Number 3 Abstracts

#### Nicolas Christou Enhancing the Teaching of Statistics: Portfolio Theory, an Application of Statistics in Finance

In this paper we present an application of statistics using real stock market data. Most, if not all, students have some familiarity with the stock market (or at least they have heard about it) and therefore can understand the problem easily. It is the real data analysis that students find interesting. Here we explore the building of efficient portfolios through optimization using examples of two and three stocks, and how covariance and correlation can help the investor to diversify his or her risk. We discuss why diversification works, but also the problems that arise in portfolio management. Stock market data can be incorporated at any level of statistics, from lower division, to upper division, to graduate courses of Mathematics and Statistics. From our experience, students find this topic very interesting and often they want to enroll in other courses related to this area.

Key Words: Efficient frontier; Covariance; Portfolio risk and return; Stock market.

#### Owen P. Hall, Jr., and Ken Ko Customized Content Delivery for Graduate Management Education: Application to Business Statistics

Globalization is bringing about a radical "rethink" regarding the delivery of graduate management education. Today, manystudents entering a residential MBA program do not possess an undergraduate degree in business. As a result, many business schools are increasingly turning to the Internet to provide "customized" instructional content to ensure that students can remain competitive throughout the program. The purpose of this paper is threefold: 1) to estimate student performance in a residential MBA program; 2) to outline a process for identifying specific learning support resources based on student backgrounds and capabilities; and 3) to illustrate the screening process in providing business statistics support content to students requiring additional preparation. The results show that neural net based classification techniques can effectively identify students for the purpose of providing additional learning resources. Business statistics is one area in which this screening process has been used to deliver specialized content to students with a variety of backgrounds enrolled in a MBA residential program.

Key Words: Residential MBA programs; Learning support systems; Intelligent agents; Neural nets; Business statistics.

#### Lawrence M. Lesser and Dennis K. Pearl Functional Fun in Statistics Teaching: Resources, Research and Recommendations

This paper presents an overview of modalities that can be used to make learning statistics fun. Representative examples or points of departure in the literature are provided for no less than 20 modalities. Empirical evidence of effectiveness specific to statistics education is starting to emerge for some of these modalities - namely, humor, song, and cartoons. To reinforce their effectiveness as an intentional teaching tool, the authors offer practical implementation tips.

Key Words: Anxiety; Cartoons; Humor; Fun; Motivation; Song; Statistics Education.

#### David Martin A Spreadsheet Tool for Learning the Multiple Regression F-test, t-tests, and Multicollinearity

This note presents a spreadsheet tool that allows teachers the opportunity to guide students towards answering on their own questions related to the multiple regression F-test, the t-tests, and multicollinearity. The note demonstrates approaches for using the spreadsheet that might be appropriate for three different levels of statistics classes, so teachers can select the context that is most appropriate for their particular needs. The spreadsheet tool is linked to this article, and materials are provided in the appendices for teachers to use as handouts, homework questions, and answer keys.

Key Words: Joint influence; Multicollinearity; F-test confidence region; T-test confidence interval.

#### Amy L. Phelps and Lina Dostilio Studying Student Benefits of Assigning a Service-Learning Project Compared to a Traditional Final Project in a Business Statistics Class

The present study addresses the efficacy of using service-learning methods to meet the GAISE guidelines (http://www.amstat.org/education/gaise/GAISECollege.htm) in a second business statistics course and further explores potential advantages of assigning a service-learning (SL) project as compared to the traditional statistics project assignment. Second semester business students were given the choice of participating in a SL project or doing a traditional project assignment.

When the projects were completed, students reflected on their experiences via survey. Both groups responded equally (agree or strongly agree) to the Likert scale questions: 96.15% reinforced learning objectives, 98.08% applied to real world, 84.62% positive experience. Responses to the open ended questions revealed that more students in the SL group (p = 0.019) wrote about the benefits of dealing with real world data, more SL students felt their work benefited others (65% felt their statistical expertise was valuable) and more (p=0.005) SL students felt that the experience will help them in future classes. These results suggest that while both groups were able to effectively support the GAISE guidelines, participation in the SL option offered an enhanced learning experience that included elements of social responsibility and personal growth. The experience was perceived more enjoyable and relevant to the real world adding elements of student empowerment while assisting a local agency in need of statistical expertise suggesting one can reap positive learning benefits by introducing service-learning pedagogy into a non-majors statistics course.

Key Words: Comparative study; Student project assignments; Community engagement; Authentic assessment.

#### Kady Schneiter Two Applets for Teaching Hypothesis Testing

Interactive applets have the ability to enhance statistics teaching by providing multiple representations of new concepts and by facilitating experimentation. I introduce two applets that have been developed as aids in illustrating ideas relevant to hypothesis testing and describe how I have used these in my classes.

Key Words: Technology; p-value; Chi-square.

#### Laura E. Schulte The Development and Validation of a Teacher Preparation Program Follow-Up Survey

Students in my applied advanced statistics course for educational administration doctoral students developed a follow-up survey for teacher preparation programs, using the following scale development processes: adopting a framework; developing items; providing evidence of content validity; conducting a pilot test; and analyzing data. The students developed the surveyitems by using the Interstate New Teacher Assessment and Support Consortium (INTASC) principles as the framework to operationally define the knowledge and skills that highly qualified teachers should possess. The students analyzed the data from the pilot study for their final exam in the course. The follow-up survey currently is being used by our university for program evaluation, improvement, and accreditation.

Key Words: Scale development; Applied statistics; Service learning.

Teaching Bits

#### Audbjorg Bjornsdottir and Joan Garfield Statistics Education Articles from 2007

Over 150 articles and book chapters were published in 2007 that pertained to statistics education. In this column, we will highlight a few of these articles that represent a variety of different journals that include statistics education in their focus. We also provide information about the journal and a link to their website so that abstracts of additional articles may be accessed and viewed.

#### Deborah J. Rumsey Random Thoughts: Letting Go to Grow: Independent vs. Mutually Exclusive

Teachers often get caught up in the discussion of how to teach this concept or that concept, or how to explain this connection or that connection, but sometimes we should just stand back and be bold enough to ask the question, "Should we even be teaching this?"; "Is it really relevant to the modern statistics course?"; "Is it related to the GAISE guidelines?"; Do we ever use this idea again later in our course?" As we contemplate the future of teaching statistics, it's a good time to stop, think, and ask the hard questions. The theme of USCOTS 2009 (The United States Conference on Teaching Statistics) is "Letting Go to Grow". In that spirit I'd like to throw out some ideas regarding the classic 'independent vs. mutually exclusive' discussion that is still included in most introductory statistics textbooks and in many courses.

Datasets and Stories

#### Shonda Kuiper Introduction to Multiple Regression: How Much Is Your Car Worth?

Data collected from Kelly Blue Book for several hundred 2005 used General Motors (GM) cars allows students to develop a multivariate regression model to determine car values based on a variety of characteristics such as mileage, make, model, engine size, interior style, and cruise control. Students learn to look at residual plots to check for heteroskedasticity, normality, autocorrelation, and multicollinearity as well as explore techniques for variable selection and develop specially constructed variables.

Key Words: Multiple Regression; Dummy Variables; Heteroskedasticity; Data Transformation; Residuals.

#### Roger Woodard and Jason Leone A Random Sample of Wake County, North Carolina Residential Real Estate Plots

The information for this data set was taken from a Wake County, North Carolina real estate database. Wake County is home to the capital of North Carolina, Raleigh, and to Cary. These cities are the fifteenth and eighth fastest growing cities in the USA respectively, helping Wake County become the ninth fastest growing county in the country. Wake County boasts a 31.18% growth in population since 2000, with a population of approximately 823,345 residents.

This data includes 100 randomly selected residential properties in the Wake County registry denoted by their real estate ID number. For each selected property, 11 variables are recorded. These variables include year built, square feet, adjusted land value, address, et al.