The paper reports some initiatives to freshen up the typical undergraduate business forecasting
course. These include (1) students doing research and presentations on contemporary tools and
industry practices such as neural networks and collaborative forecasting (2) insertion of
Logistic Regression in the curriculum (3) productive use of applets available on the Internet
to convey abstract concepts underlying ARIMA models and (4) showcasing forecasting tools in
timely or familiar applications. These initiatives align with the best practices framed across
the “Making Statistics More Effective in Schools of Business” (MSMESB) conferences. Course
experiences and student feedback are also discussed.
Key Words: ARIMA, Logistic Regression, Pedagogy
Statistics in the Community (STATCOM) is a student-run statistical consulting program that has been
serving its local community since 2001. Directed and staffed by graduate students from Purdue
University’s Department of Statistics, it provides professional consulting services to governmental
and nonprofit groups free of charge. Students work in teams to help community clients address
specific problems and needs. Past clients include school corporations, libraries, community
assistance programs, and the city of West Lafayette. Participation in STATCOM allows students
to apply statistical concepts and classroom material to solve real problems. It also develops
skills in leadership, management, and written and oral communication of results to the general
public. Though important for any future career in statistics, these skills are not typically
emphasized in graduate courses, research, or the on-campus academic consulting service.
The university and academic department also benefit through increased interaction and visibility
in the local community. STATCOM can serve as a model for integrating service learning into graduate
statistical education at other colleges and universities.
Keywords:Community service; Graduate Education; Service-learning
Service-learning projects are a useful method for students to learn both the practice and value of
statistical methods. Effective service learning, however, depends on several factors and can be
implemented according to a variety of models. In this article, different models for incorporating
service-learning in statistics courses are presented along with example statistics courses.
Principles for good service-learning practice will also be presented as a means for assessing
the quality of a service-learning course component.
Keywords:Assessment; Experiential learning; Service-learning; Statistics
Introductory statistics textbooks rarely discuss the concept of variability for a categorical
variable and thus, in this case, do not provide a measure of variability. The impression is
thus given that there is no measurement of variability for a categorical variable.
A measure of variability depends on the concept of variability. Research has shown that
"unalikeability" is a more natural concept than "variation about the mean" for many students.
A "coefficient of unalikeablity" can be used to measure this type of variability.
Variability in categorical data is different from variability in quantitative data. This
paper develops the coefficient of unalikeability as a measure of categorical variability.
Keywords: Variability, Categorical Variable, Unalikeability
Kalamazoo College is a selective, liberal arts college located in Kalamazoo, Michigan with total
enrollment of approximately 1200 students. The academic calendar is comprised of three 10-week
quarters, each of which is followed by one week for final examinations. Kalamazoo College is
distinguished by its four-fold academic program known as the “K-Plan”: (1) Rigorous liberal
arts coursework, (2) study abroad, (3) career development, and (4) the senior individualized
project. With the inception of the K-Plan over 40 years ago, experiential education has long
characterized the College student experience, especially with respect to the last three components
listed above. Over the past ten years, the on-campus experience of Kalamazoo College students
has also become more experiential in nature as a substantial proportion of courses now have
significant service-learning components.
This article reports on a subset of results from a larger study which examined middle and high
school students’ probabilistic reasoning. Students in grades 5, 7, 9, and 11 at a boys’ school
(n=173) completed a Probability Inventory, which required students to answer and justify their
responses to ten items. Supplemental clinical interviews were conducted with 33 of the students.
This article describes students’ specific reasoning strategies to a task familiar from the
literature (Tversky and Kahneman, 1973).
The results call into question the dominance of the
availability heuristic among school students and present other frameworks of student reasoning.
Keywords:availability heuristic, combinatorial thinking, middle school, high
From Research to Practice
A collaborative, statistics education research project (Lovett, 2001) is discussed. Some results
of the project were applied in the computer lab sessions of my elementary statistics course.
I detail the process of applying these research results, as well as the use of knowledge surveys.
Furthermore, I give general suggestions to teachers who want to put educational research results
into effective use in their own classes.
Key Words: Computer lab, Data analysis, Knowledge survey, Research application
Datasets and Stories
The data for 104 software projects is used to develop a linear regression model that uses function
points (a measure of software project size) to predict development effort. The data set is
particularly interesting in that it violates several of the assumptions required of a linear model;
but when the data are transformed, the data set satisfies those assumptions. In addition to
graphical techniques for evaluating model aptness, specific tests for normality of the error
terms and for slope are demonstrated. The data set makes for an excellent case problem for
demonstrating the development and evaluation of a linear regression model.
Key Words: data transformation; residual analysis; linear model assumptions;