The role of package driven statistics courses


Book: 
Proceedings of the Third International Conference on Teaching Statistics
Authors: 
Lee, A. J., & Seber, G. A. F.
Editors: 
Vere-Jones, D., Carlyle, S., & Dawkins, B. P.
Category: 
Volume: 
2
Pages: 
217-221
Year: 
1991
Publisher: 
International Statistical Institute
Place: 
Voorburg, Netherlands
Abstract: 

In 1974, one of the authors (GAFS) introduced a terminating first year service course in statistics, paper 26.181, for non-mathematics students. In the following years we observed that the service course 26.181 catered not only for students majoring in other subjects, but also for a substantial number of mathematics students who preferred a more practical approach than the traditional one. The numbers were also steadily growing (about 1500 in 1990). At that stage we realised that 26.181 provided a potential source of advancing statistics students who might be interested in taking a second year follow-up course. In the 1980s Alan Lee (and probably most of us that vintage) had been strongly influenced by works on exploratory data analysis by such people as Tukey , McNeil, Velleman and Hoaglin. Under Lee's direction a second year data analysis course 26.281 was launched in 1981. His aim was to provide further training in practical statistics and data analysis without requiring too much mathematical knowledge or statistical theory. He realised that the students needed easy computer access for a realistic approach to data analysis. Suitable access to a mainframe was out of the question at the time, but micros seemed a viable alternative. A suitable statistical package was therefore developed called STATCALC (Lee et al., 1984) which had partly evolved from several programs on exploratory data analysis adapted from McNeil (1977) by Ross Ihaka. The package runs on IBM and Macintosh personal computers, the latter being currently used in our department. Lee and Peter Mullins also wrote a manual to go with the package. The manual, with its extensive tutorial section, also serves as the text for the course.

The CAUSE Research Group is supported in part by a member initiative grant from the American Statistical Association’s Section on Statistics and Data Science Education

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