Literature Index

Multiple-choice randomized (MCR) examinations in which the order of the items or questions as well as the order of the possible responses is randomized independently for every student are discussed. This type of design greatly reduces the possibility of cheating and has no serious drawbacks. We briefly describe how these exams can be conveniently produced and marked. We report on an experiment we conducted to examine the possible effect of such MCR randomization on student performance and conclude that no adverse effect was detected even in a rather large sample.

Two central concepts in probability theory are those of "independence" and of "mutually exclusive" events and their alternatives. In this article we provide for the instructor suggestions that an be used to equip students with an intuitive, comprehensive understanding of these basic concepts. Let us examine each of these concepts in turn along with common student misunderstandings.

Twenty-five years ago, the term "technology" had a rather different meaning than it does today. Anything other than chalk-and-talk or paper-and-pencil was considered technology for teaching. This might have included anything from fuzzy-felt boards to mechanical gadgets, as well as the multimedia of that period (i.e., television, tape recordings, films, and 35mm slides). The title of this Round Table talk refers to "technology"; however, the papers are concerned mainly with computers and software. The occasional reference to calculators is really only a variation on this theme, because they are essentially hand-held computers. This is merely an observation--not a criticism. The re-invention of the meaning of the term 'technology' is something to which we have all been a party.<br>The developments in computers and computing during the past quarter of a century have been so profound that it is not surprising that they replaced other technological teaching aids. This does not mean that we should forget such alternative aids altogether, nor the need to research their effective use. However, it is obvious that computers have significantly increased the range, sophistication, and complexity of possible classroom activities. Computer-based technology has also brought with it many new challenges for the teacher who seeks to determine what it has to offer and how that should be delivered to students.<br>Innovations in this area tend to be accompanied by a number of myths that have crept into our folklore and belief systems. Myths are not necessarily totally incorrect: They often have some valid foundation. However, if allowed to go unchallenged, a myth may influence our strategies in inappropriate ways. This Round Table conference provides a timely opportunity to recognize and examine the myths that govern innovations and implementations of technology in the classroom, and to establish the extent to which our approaches are justified.

Stock car racing has seen tremendous growth in popularity in recent years. We introduce two datasets containing results from all Winston Cup races between 1975 and 2003, inclusive. Students can use any number of statistical methods and applications of basic probability on the data to answer a wide range of practical questions. Instructors and students can define many types of events and obtain their corresponding empirical probabilities, as well as gain a hands-on computer-based understanding of conditional probabilities and probability distributions. They can model the rapid growth of the sport based on total payouts by year in real and adjusted dollars, applying linear and exponential growth models that are being taught at earlier stages in introductory statistics courses. Methods of making head-to-head comparisons among pairs of drivers are demonstrated based on their start and finish order, applying a simple to apply categorical method based on matched pairs that students can easily understand, but may not be exposed to in traditional introductory methods courses. Spearman's and Kendall's rank correlation measures are applied to each race to describe the association between starting and finishing positions among drivers, which students can clearly understand are ordinal, as opposed to interval scale outcomes. A wide variety of other potential analyses may also be conducted and are briefly described. The dataset nascard.dat is at the driver/race level and contains variables including: driver name, start and finish positions, car make, laps completed, and prize winnings. The dataset nascarr.dat is at the race level and contains variables including: number of drivers, total prize money, monthly consumer price index, track length, laps completed, numbers of caution flags and lead changes, completion time, and spatial coordinates of the track. These datasets offer students and instructors many opportunities to explore diverse statistical applications.