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Volume 6, Number 1 (March 1998) ISSN: 1069-1898

Deborah A. Curtis and Michael Harwell, "Training Doctoral Students in Educational Statistics in the United States: A National Survey" (69K)

Although numerous research studies have focused on issues related to the teaching of statistics, few studies have focused on the training of people who may become statistics teachers. The purpose of this study was to examine doctoral students' preparation in statistics in the field of education. A national survey was conducted of twenty-seven quantitative methods (QM) programs. One QM professor from each program was identified and asked to describe and evaluate the training of QM and non-QM doctoral students at his or her institution. The vast majority of professors indicated that most or all of the students in their QM programs received training in the "old standard" procedures -- ANOVA, multiple regression, and traditional multivariate procedures, whereas fewer than half of the professors indicated that most or all of their QM students received training in more recent procedures such as bootstrapping and multilevel models. Professors were also asked to rate the skills of their QM students in areas such as mathematical statistics and computing on a scale from "Weak" to "Strong." Most professors gave high ratings to their QM students' skills with statistical packages, but gave much more mixed ratings of their QM students' training in mathematical statistics. Nearly half of the professors thought that most of their QM students could have benefited from one or two additional statistics courses. Results are discussed in terms of training future doctoral students. --DAC

Key Words: Doctoral student preparation; Statistics education; Survey research.

Thomas E. Love, "A Project-Driven Second Course" (44K)

I trace the development of a new course in modern data analysis involving a wide spectrum of statistical techniques. Because the course is based entirely on case studies, real-data settings, and student projects and is computer-intensive, a series of challenges facing many instructors are addressed. In a single semester, students explore data using tools from EDA, multiple regression, analysis of variance, time series analysis, and categorical data analysis. The focus is on understanding and forecasting in a variety of data settings, learning how to summarize relationships and measure how well these relationships fit data, and how to make meaningful statistical inferences when the usual assumptions do not hold. The course emphasizes what the statistical process is all about: how to conduct studies, what the results mean, and what can be inferred about the whole from pieces of evidence. --TEL

Key Words: Active learning; Data analysis; Data collection; Problem-based learning.

Deborah J. Rumsey, "A Cooperative Teaching Approach to Introductory Statistics" (81K)

Many of today's university undergraduate curricula include two seemingly conflicting themes: (1) increase the quality of teaching to include emphasis on pedagogical elements, such as active learning, in the undergraduate statistics classroom; and (2) cope with a decrease in teaching resources. In this paper, a means by which a department of mathematics or statistics can maintain and increase its standards of teaching excellence in introductory statistics while coping with ever-increasing budgetary pressures is proposed. This process involves promoting what we call cooperative teaching, applying the concepts of cooperative learning to a group of instructors. --DJR

Key Words: Cooperative learning; Statistics education.

Thomas H. Short and Joseph G. Pigeon, "Protocols and Pilot Studies: Taking Data Collection Projects Seriously" (30K)

Although there is consensus among statistics educators that student data collection projects are of substantial value, we feel that the planning and piloting phases of data collection are often neglected. We ask our students to write protocols or detailed plans for how the data will be collected, and to plan and conduct pilot studies before embarking on full scale data collections. We present examples and results from situations including college freshman introductory statistics courses, graduate statistics courses, and teacher training workshops. --THS

Key Words: Assessment; Clinical study; Planning; Rubric.

Bradley A. Warner, David Pendergraft, and Timothy Webb, "That Was Venn, This Is Now" (23K)

Basic probability concepts are difficult for some students to understand initially. Through the use of a Venn diagram disguised as a pizza, we will discuss how to explain introductory probability concepts. Students are able to answer probability questions, including conditional probability, by simply looking at a picture. This tool not only enhances learning but retention as well. --BAW

Key Words: Basic probability; Pizza; Venn diagram.

"Teaching Bits: A Resource for Teachers of Statistics" (39K)

This column features "bits" of information sampled from a variety of sources that may be of interest to teachers of statistics. Bob delMas abstracts information from the literature on teaching and learning statistics, while Bill Peterson summarizes articles from the news and other media that may be used with students to provoke discussions or serve as a basis for classroom activities or student projects. --JG

Robert Carver, "What Does It Take to Heat a New Room? Estimating Utility Demand in a Home" (36K)

In a residential home, energy consumption is closely related to the outdoor temperature and size of the house. In a home of a given size, fuel consumption varies fairly predictably through the year. When homeowners add a room, other things being equal, energy consumption should increase. This dataset permits students to estimate the energy demand, make forecasts for future months, and investigate other relationships.

The dataset contains natural gas and electricity usage data for a single-family residence in the Boston area from September 1990 through May 1997, accompanied by monthly climatological data. The dataset is useful for illustrating the concepts and techniques of central tendency, dispersion, time series analysis, correlation, simple and multiple regression, and variable transformations. --RC

Key Words: Forecasting; Measurement; Regression; Time series; Variable transformation.

Editorial Board for Volume 6, Number 1

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