This paper discusses several current topics in the areas of statistical graphics research and applications and suggests additional ways that graphical methods can be used to improve statistical education.
This paper discusses several current topics in the areas of statistical graphics research and applications and suggests additional ways that graphical methods can be used to improve statistical education.
This paper is based on our attempts to develop an inference course at Reading in which students learn about concepts primarily through project work.
When it became apparent that many practical problems could not be solved by the standard procedures, a developmental effort began which combined the conceptual power of a prediction model approach with the computational power of high speed computers. A series of short courses were developed to introduce researchers to the new capabilities brought about through the combination of effective application of regression models with computing techniques.
The laws to be introduced share some of the characteristics of two other laws discussed in the paper. They will help to throw light on a very widely-used approach to teaching statistics in tertiary institutions.
This paper is concerned with the process of designing and implementing a new statistics course.
This paper describes the need for a shift in emphasis away from the development of operations as described by Leontiev, towards the provision of increased experience of statistical activity.
Criticism of, exposure to, and justification of the educational task of universities is escalating. The era of the untouchable ivory tower has gone forever. Universities are experiencing an increasing loss of status. The reason for this is that functionality is used as criterion for efficiency end effectiveness. The value and meaning of universities for society, in terms of the provision of manpower, contribution to the national economy, planning, and the solving of problems, are of prime importance. In addition, the democratisation process requires increasingly more claims on and participation in the management and control of universities by all interested parties, whether parents or students or donors or the community (as ratepayers) or the professions. Whatever the case may be, the searchlight is directed more and more at the lecturing function of universities. Present-day universities are no longer "elite" universities, but mass universities. Because of this, as well as the ever-increasing cost of equipment and facilities, the claims of the ratepayer are growing, and therefore he or she looks more and more critically at the effectiveness of universities, which according to him or her - inadmissably oversimplified - is measured in pass and fail figures. In its great and unique task, namely the provision of high-level manpower, only one guarantee for success exists for the educational task of universities: to strive for excellence at all levels; and only one successful reality: a healthy balance between the timeless - striving for intellectual and academic progress and the contemporary - meeting the demand of relevance. For the lecturer this means the optimum allocation of his or her time to teaching, research, and rendering of professional service, and to build and develop these tasks on excellence.
This paper is about teaching large service courses in introductory statistics, with particular reference to engineering students and science students not majoring in mathematics.
In this paper we look at preparatory and bridging courses in statistics at the interface between secondary and tertiary education. We include discussion about similar mathematics courses which contain a probability and statistics component.
During the 60s and 70s students were encouraged to become statisticians because of the intrinsic interest of the discipline and because of the predicted future need for statisticians. Many people who appreciated the wide applicability of statistics felt that it was only a matter of time before employers would be crying out for statisticians and that newspapers would be full of advertisements for interesting and well-paid jobs for statisticians. The ensuing campaign to attract students was reasonably successful and the number of students majoring in statistics showed slow but steady growth. Unfortunately, the demand for statisticians did not grow at the predicted rate. One of the reasons for this was that the people making the employment decisions tended to have little understanding of statistics and would be far more inclined to employ an engineer or an economist, even when the job was primarily statistical nature. For a number of years I have given a course, "Statistics for Research Workers", and have been disturbed by the number of people attending this introductory course who have been working as statisticians. The last decade has seen a dramatic change due primarily, to the computer. Many more people now have the opportunity and the need to work with data, and the ability to readily carry out, though not necessarily understand, quite complex statistical procedures. There has also been a substantial increase in the statistical training of people for a wide range of professions so that there is, overall, a much greater awareness of the need for and the benefits to be gained from statistical expertise. As a consequence, the number of jobs being advertised which specify statistical expertise has grown to the point where there is now a recognised shortage of well trained statisticians. By the early 80s Honours courses were starting to change to reflect the students' interests and in 1984 Melbourne, Monash and LaTrobe Universities started to explore the possibility of mounting a joint MSc by Coursework programme in order to try to satisfy the need for applied statisticians.