• In recent years, responding to the demand of industry and other sectors of the economy, changes have been made both to the structure and curriculum of undergraduate degree programmes in University of Malaya. The Institute of Mathematical Sciences started to offer a separate B.Sc.(Stat) programme in the academic year 1996/97. Prior to this, only one degree, the B.Sc.(Math) degree, was awarded although students awarded this degree might have taken a large number of probability and statistics courses. Already the B.Sc.(Stat) degree programme has undergone several changes and more are being considered. This paper will describe the current state of the degree programme, discuss the changes already made and those being proposed, and compare the programme with the ASA Curriculum Guidelines for Undergraduate Programs in Statistical Sciences.

  • Statistical science is important in a developing economy. Consequently, the teaching of statistics must meet particular and rigorous demands. But in developing countries it is not so easy to direct any of the few available resources toward the teaching of statistics. So those countries have to choose the best way to follow the evolution of statistics and to apply it efficiently, especially in the new contexts of the global economy and the development of new technologies for information and communication.

  • We first note that tests for interaction are missing in virtually all textbooks on nonparametric statistics. We will discuss some reasons why this is so. We then make a case for featuring tests for interaction in the course. By learning how to use median polish and graphical displays students can begin to conceptualize what an interaction means. This will strengthen their understanding of additive models as well. After a conceptual basis for understanding interaction is in place, we can then proceed to design tests for interaction. They will not be strictly nonparametric. This provides a good opportunity for discussion of what it means to have a nonparametric test and why it is impossible to construct an ordinary permutation test for interaction.

  • The bootstrap is a general resampling procedure which can be applied to estimate the sampling distribution of a statistic. From the statistical practitioner's point of view it has attractive properties because it requires few assumptions, little modeling or analysis, and can be applied in an automatic way in a wide variety of situations regardless of their theoretical complexity. The bootstrap can provide answers to questions that are too complicated for traditional statistical analyses, which are usually based on asymptotic normal approximations. A brief discussion of the non-parametric bootstrap is presented, followed by examples and illustrations. Possible suggestions regarding the teaching of these concepts at various levels are made. The key requirements for computer implementation of the bootstrap method include a flexible programming language with a collection of reliable quasi-random number generators, a wide range of built-in statistical bootstrap procedures and a reasonably fast processor. The use of the statistical languages S and Fortran, using the current commercial versions S-Plus 4.5 and Digital Fortran 6.0, are illustrated.

  • A major objective of the University of Transkei Research Resource Centre is to enable staff and students to acquire research knowledge and skills. This is intended to empower faculty to initiate quality research projects and participate effectively in ongoing research. We recognised that research skills of staff and students ranged from none to well-experienced. In addressing different needs we found that an effective method was to relate all activities to the context of the research and where possible to specific research projects. Importantly we endeavoured to anticipate needs and afforded researchers with face-to-face sessions, a number of workshops, short courses and research seminars. This paper discusses how the consultancy process is used to empower social science researchers.

  • The First International Conference on the Teaching of Mathematics was held in Samos, Greece, in July 1998. Presentations by the attendees reflected a recent debate on reforms of the mathematics curriculum and related pedagogy. Chief among these was a greater emphasis on connecting the mathematics curriculum with applications, to make courses in mathematics more "relevant" to students. This manuscript notes that mathematicians tend to teach students to approach data analysis in a constructive manner, proceeding from an understanding of the basic science, while statisticians concentrate on reductive approaches, whereby models are generated upon consideration of the data themselves. It is suggested that departments of mathematics and statistics will need to adopt a new spirit of cooperation, and partner with colleagues in application areas, if curricular enhancements in either domain are to have a reasonable chance at success.

  • We describe the purpose of the "Datasets and Stories" section of this journal. Guidelines for submitting datasets and articles to this section are discussed. Instructions are provided for retrieving data from the JSE data archives.

  • This paper describes one program in the Teaching Statistics Visually (TSV) project. TSV supports inductive learning in introductory undergraduate applied statistics courses. The program (1) helps teach concepts rather than analyze data, (2) focuses on one module in a statistics course, (3) relies on visualization rather than formulas, (4) is easy to use, (5) is flexible, supporting different learning levels, and (6) is easy to manage, requiring commonly available resources and incorporating special features to simplify classroom use. A prototype version of the program "Comparing Two Normal Distributions" is included with this paper. The reader is invited to experiment with the program and to send comments and suggestions for improvement to the authors.

  • 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.

  • In Indian Universities, courses titled 'Statistics Practical' usually involve only numerical evaluation. There is very little scope for independent thinking and decision making on the part of the students. We report here our experience of teaching a practical course on sampling techniques in a different way. On the whole, it was an encouraging exercise.