Literature Index

Time Series Analysis is a particular area of Statistics that has seen remarkable progress in the last 20 years. There has been increased activity and interest in both the theory and practice of the subject that has led in some sense to a unification of methodologies that existed previously. It is clearly not pure coincidence that the growth in Time Series Analysis has occurred at the same time as the growth of computing availability. Software of suitable quality was a little slow in appearing at first. But recent years have seen the introduction of many new and re-vamped statistical packages, and most of these nowadays contain quite extensive Time Series routines.

The use of so-called 'drill and practice' exercises is almost universal in elementary statistics courses. Even in higher education, where there is easy access to statistical computer packages, many teachers still consider that there is an educational benefit in students using hand calculation to work through simple examples on topics such as the Normal distribution or linear regression. Such exercises are particularly attractive for teachers to set as home work assignments since they tend to have unique numerical answers, making them much quicker to mark than more open-ended tasks where students engage in discussion of their results. Regrettably the exercises are equally attractive to minority of lazy students who can happily copy the solutions of a more diligent but weak-willed colleague. The purpose of this article is not to debate the relative merits of such assignments, but merely to facilitate the setting of individualized tasks to combat the increasing problem of plagiarism. Simonite et al. (1998) describe a similar approach, but their method involves Visual Basic macros, which require a substantially higher technical capability than the method described here.<br>This article describes how the mail merge facility within Microsoft Word can be used in conjunction with Microsoft Excel to generate personalized assignments for students at all levels.

This article advocates the use of mnemonics as an aid to learning statistics. Some examples are supplied and others sought.

Two of the most common suggestions for improving statistics education are using substantial open-ended projects and using real data sets for statistical analysis. Both recommendations have been incorporated successfully into an elementary statistics class for non-science majors by having the students design, implement, and analyze data from their own statistical study over the course of a semester. Details of how this implementation was organized as well as a partial list of the students' projects are included.