Journal Article

  • This article focuses on a two treatment, two period, two treatment sequence crossover drug interaction study of a new drug and a standard oral contraceptive therapy. Both normal theory and distribution-free statistical analyses are provided along with a notable amount of graphical insight into the dataset. For one of the variables, the decision on the presence or absence of a drug interaction is reversed depending on whether the normal theory or the distribution-free analysis is favored. The data also contain statistically significant period effects, statistically significant but clinically unimportant treatment effects, some modest degree of structural nonnormality; and modest to more extreme outliers. This and 28 other pedagogically useful datasets can be found at www.math.iup.edu/~tshort/Bradstreet.

  • The video lottery terminal dataset contains observations on the three windows of an electronic slot machine for 345 plays together with the prize paid out for each play. The prize payout distribution is so badly skewed that confidence intervals for expected payout based on the central limit theorem are not accurate. This dataset can be used at the graduate or upper undergraduate level to illustrate parametric bootstrapping. The dataset can also be used in a graduate course to illustrate tests of independence for two and three-way contingency tables involving random zeroes, or these tables may be collapsed and used as examples in an introductory course.

  • A simple procedure is presented for obtaining the sample size and acceptance number for a single sample acceptance sampling plan, given the probability of lot acceptance for lots having proportion defective equal to p1, and the probability of lot rejection for lots having proportion defective equal to p2. The procedure gives a practical illustration of the use of the normal approximation to the binomial distribution that is appropriate for courses on statistical quality control as well as on introductory statistics.

  • Conceptual understanding of statistics is usually considered one of several aspects of statistical knowledge. It refers to the ability of students to tie their knowledge of statistical ideas and concepts into a network of interrelated propositions. In this study an attempt was made to analyze the theory of descriptive regression analysis into its constituent propositions. Content analysis of the work of nine students revealed that these propositions were used by the students as cognitive units in their mental representation of the statistical theory. Suggestions for a use of constituent propositions as learning tools are discussed.

  • A recent symposium on "Improving the Work Force of the Future: Opportunities in Undergraduate Statistics Education" was held to focus attention on the importance of undergraduate statistics education. The symposium and the approval of curriculum guidelines for undergraduate degrees by the Board of Directors of the American Statistical Association have done much to define the current state of undergraduate education in statistics and suggest directions for improvement. This article summarizes the activities leading up to the symposium and provides a brief summary of six papers from the symposium that have been published. The article concludes with a discussion of some of the outstanding issues that remain to be addressed.

  • Students in an applied statistics course offering some nonparametric methods are often (subconsciously) restricted in modeling their research problems by what they have learned from the t-test. When moving from parametric to nonparametric models, they do not have a good idea of the variety and richness of general location models. In this paper, the simple context of the Wilcoxon-Mann-Whitney (WMW) test is used to illustrate alternatives where "one distribution is to the right of the other." For those situations, it is also argued (and demonstrated by examples) that a plausible research question about a real-world experiment needs a precise formulation, and that hypotheses about a single parameter may need additional assumptions. A full and explicit description of underlying models is not always available in standard textbooks.

  • Representatives from academia, industry, and government met in May 1999 and in April 2000 at the ASA Headquarters to discuss issues concerning undergraduate education in statistical science. One outcome of these meetings was the symposium entitled "Improving the Workforce of the Future: Opportunities in Undergraduate Education," held August 12 through 13, 2000, in Indianapolis, Indiana. Among the topics discussed in the meetings and at the symposium were guidelines for minor programs in statistical science. This article summarizes the results of these discussions.

  • A data set containing n = 210 observations and published by Lieblein and Zelen (1956) provides a useful example of multiple linear regression applied to an engineering problem. It relates percentiles of the failure time distribution for ball bearings to characteristics of the bearings (load, ball diameter, number of balls) in a theoretically derived equation that can be put into linear form. The analysis requires testing the equality of regression coefficients between manufacturers and between types of ball bearing within manufacturer to see if the same equation applies across the industry. Furthermore, there is special interest in confirming an accepted value for one of these coefficients. The original analysis employed weighted least squares, although this may have been unnecessary. In addition to the regression aspects of the problem, the example is useful for the extensive data manipulation required.

  • In a residential home, energy consumption is closely related to the outdoor temperature and size of the house. In a home of a given size, fuel consumption varies fairly predictably through the year. When homeowners add a room, other things being equal, energy consumption should increase. This dataset permits students to estimate the energy demand, make forecasts for future months, and investigate other relationships.

  • This paper focuses on a third arm of statistical development: statistical thinking. After surveying recent definitions of statistical thinking, implications for teaching beginning students (including non-majors) are discussed. Several suggestions are given for direct instruction aimed at developing "habits of mind" for statistical thinking in students. The paper concludes with suggestions for assessing students' ability to think statistically. While these suggestions are primarily aimed at non-majors, many statistics majors would also benefit from further development of these ideas in their undergraduate education.

Pages

register