Literature Index

With the dual goal of providing individualized learning and assessment, while simultaneously preserving academic integrity, we have implemented a computerized testing system to generate, administer and grade quizzes in an introductory statistics course for graduate students. A fundamental reason for individualization is to permit each student to learn at his or her own pace. At the same time, administering individualized instruction must not increase the time involvement of the instructor. The ability of the computer to randomly select questions from a test bank, to randomly generate data for the questions, and to randomly order the answer choices makes it possible for learning and assessment to occur in accord with each student's individual needs while maintaining fairness for the students and the instructor.

Group activities are an excellent way to enhance learning. When students are actively involved in a relevant project, understanding and retention are improved. The proposed activity introduces a timely and interesting project typical of the type encountered in statistical practice. Using the computer to successfully developing an appropriate model is a valuable educational experience that builds confidence.

A dataset concerning the relationship between respiratory function (measured by forced expiratory volume, FEV) and smoking provides a powerful tool for investigating a wide variety of statistical matters. This paper gives a brief description of the problem, the data, and several issues and analyses suggested by the problem of quantifying the relationship between FEV and smoking.

This paper presents a data set based on an industrial case study using design of experiments. The data set is pedagogically rich because it has a rather large total sample size from an industrial setting that naturally yields a large third order interaction term. The experiment is a 23 design and is initially presented with no replications. The sample size of the data is then doubled and the analysis repeated, comparing these results with previous results. The process is repeated until eight replications are available for each combination of factors and all parameters are estimated. With eight replications, the analysis shows all main effects and all interactions are statistically significant at the a = 0.05 level. With smaller sample sizes, various main effects and interactions are not found to be statistically significant. Through this presentation the instructor can lead students in discussions about the effect of increased sample sizes, power, statistical significance (or insignificance), interaction terms, Type I and Type II errors as well as the importance and the role of the error term. In addition, students can manipulate the data set in a computer laboratory setting to illustrate many of the concepts inherent in the design of experiments and analysis of variance.