Literature Index

This article is the culmination of the work on curriculum for Bachelor of Science degrees in statistical science from both the workshop on undergraduate curriculum held on April 28-29, 2000, and the symposium on improving the workforce of the future.

Globalization is bringing about a radical "rethink" regarding the delivery of graduate management education. Today, many students entering a residential MBA program do not possess an undergraduate degree in business. As a result, many business schools are increasingly turning to the Internet to provide "customized" instructional content to ensure that students can remain competitive throughout the program. The purpose of this paper is threefold: 1) to estimate student performance in a residential MBA program; 2) to outline a process for identifying specific learning support resources based on student backgrounds and capabilities; and 3) to illustrate the screening process in providing business statistics support content to students requiring additional preparation. The results show that neural net based classification techniques can effectively identify students for the purpose of providing additional learning resources. Business statistics is one area in which this screening process has been used to deliver specialized content to students with a variety of backgrounds enrolled in a MBA residential program.

The purpose of this volume, and others in the Addenda Series, is to provide instructional ideas and materials that will support implementation of the Curriculum and Evaluation Standards in local settings.

The idea of data as a mixture of signal and noise is perhaps the most fundamental concept in statistics. Research suggests, however, that current instruction is not helping students to develop this idea, and that though many students know, for example, how to compute means or medians, they do not know how to apply or interpret them. Part of the problem may be that the interpretations we often use to introduce data summaries, including viewing averages as typical scores or fair shares, provide a poor conceptual basis for using them to represent the entire group for purposes such as comparing one group to another. To explore the challenges of learning to think about data as signal and noise, the authors examine the "signal/noise" metaphor in the context of three different statistical processes: repeated measures, measuring individuals, and dichotomous events. On the basis of this analysis, several recommendations are made about research and instruction.