Volume 4, Number 1 (March 1996) ISSN: 1069-1898
Maxine Pfannkuch and Constance M. Brown, "Building On and Challenging Students' Intuitions About Probability: Can We Improve Undergraduate Learning?" (55K)
Students of statistics must be allowed to experience the omnipresence of variation and experience the dual modes of thinking probabilistically and deterministically to explain that variation. A pilot study to investigate the understanding of variability and probability of a small group of students is described. These students have a strong tendency to think deterministically (especially in real world settings); they have little understanding of variability and its relationship to sample size; and they are generally unable to reconcile their intuitions with the formal probability they have been taught. There were some initial indications that allowing students to experience variation personally made them more aware of their over-emphasis on causal explanations of variability. Lastly, it appears that students' awareness about probabilistic thinking can be raised by actively challenging and discussing their tacit intuitive models about chance. --MP
Key Words: Allowing students to experience variation; Fostering students' awareness of their thinking; Probability as a tool to understand variation.
John C. Nash and Tony K. Quon, "Issues in Teaching Statistical Thinking With Spreadsheets" (31K)
Spreadsheet software is widely used and now includes statistical functionality. This paper discusses the issues raised in teaching statistics with spreadsheet software. The principal concerns relate to aspects of the spreadsheet view of computation that make it difficult to keep track of what calculations have actually been carried out or to control the spreadsheet by means of a script. We also discuss a number of other advantages and deficiencies of spreadsheets for teaching statistics. --JCN
Key Words: Spreadsheet software; Computing paradigm; Statistical functions; Audit files.
Mustafa R. Yilmaz, "The Challenge of Teaching Statistics to Non-Specialists" (38K)
Traditional methods of teaching introductory statistics are often viewed as being ineffective because they fail to establish a clear link between statistics and its uses in the real world. To be more effective, it is essential that teaching objectives are clearly defined at the outset and issues of content and methodology are addressed accordingly. This paper proposes that the relevant objectives should aim to develop the following competencies: (1) ability to link statistics and real-world situations, (2) knowledge of basic statistical concepts, (3) ability to synthesize the components of a statistical study and to communicate the results in a clear manner. Towards these objectives, we propose a revamp of the traditional course design together with the creation of a new software tool that is currently unavailable. --MRY
Key Words: Teaching objectives; Constraints; Integrated course design; Teaching software.
"Teaching Bits: A Resource for Teachers of Statistics" (40K)
This column features "bits" of information sampled from a variety of sources that may be of interest to teachers of statistics. Joan Garfield abstracts information from the literature on teaching and learning statistics, while Laurie Snell 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
Roger W. Johnson, "Fitting Percentage of Body Fat to Simple Body Measurements" (19K)
Percentage of body fat, age, weight, height, and ten body circumference measurements (e.g., abdomen) are recorded for 252 men. Body fat, one measure of health, has been accurately estimated by an underwater weighing technique. Fitting body fat to the other measurements using multiple regression provides a convenient way of estimating body fat for men using only a scale and a measuring tape. This dataset can be used to show students the utility of multiple regression and to provide practice in model building. --RWJ
Key Words: Multiple regression.
Editorial Board for Volume 4, Number 1
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