Journal Article

  • This paper describes a case study based on data taken from the U.N.E.S.C.O. 1990 Demographic Year Book and The Annual Register 1992 giving birth rates, death rates, life expectancies, and Gross National Products for 97 countries. Suggested activities include exploratory graphical analyses to answer several central questions. These include an investigation into the wealth and life expectancies of different country groups and their population growth. Inequalities in the life experiences of different groups become readily apparent. Students are stimulated to generate their own questions and to find possible solutions.

  • In this paper, I will define statistical literacy (what it is and what it is not) and discuss how we can promote it in our introductory statistics courses, both in terms of teaching philosophy and curricular issues. I will discuss the important elements that comprise statistical literacy, and provide examples of how I promote each element in my courses. I will stress the importance of and ways to move beyond the "what" of statistics to the "how" and "why" of statistics in order to accomplish the goals of promoting good citizenship and preparing skilled research scientists.

  • In linear regression problems in which an independent variable is a total of two or more characteristics of interest, it may be possible to improve the fit of a regression equation substantially by regressing against one of two separate components of this sum rather than the sum itself. As motivation for this "separation principle," we provide necessary and sufficient conditions for an increased coefficient of determination. In teaching regression analysis, one might use an example such as the one contained herein, in which the number of wins of Major League Baseball teams is regressed against team payrolls, for the purpose of demonstrating that an investigator can often exploit intuition and/or subject-matter expertise to identify an efficacious separation.

  • We describe our experiences and express our opinions about a non-introductory statistics course covering data analysis. In addition to the methods of statistics, the course emphasizes the process of data analysis, the communication of results, and the role of statistics in the accumulation of scientific evidence. Since it is impossible to provide explicit instructions for all data analytic situations, the course attempts to impart a body of tools, a spirit of approach, and enough thoroughly covered case studies to give students the skills and confidence to apply this craft on their own.

  • StatVillage is a hypothetical city based on real data that is suitable as a teaching aid for an introductory class in survey sampling. It uses a World Wide Web-based interface to allow the students to actively select sampling units; it then returns the corresponding data for further analysis. The underlying data are actual census records extracted from public use microdata files.

  • We describe a World Wide Web-accessible workshop designed for students in an introductory statistics course that uses a capture-recapture experiment to illustrate the concept of a sampling distribution. In addition to the usual "sampling bowl" experiment, the workshop contains a computer simulation program written in XLISP-STAT that will allow students to further investigate the properties of the estimator.

  • The common development of the hypergeometric probability formula is typically confusing to students in introductory statistics courses. Two alternative developments that appear to be more intuitive and conceptually consistent are presented.

  • A useful way of approaching a statistical problem is to consider whether the addition of some missing information would transform the problem into a standard form with a known solution. The EM algorithm (Dempster, Laird, and Rubin 1977), for example, makes use of this approach to simplify computation. Occasionally it turns out that knowledge of the missing values is not necessary to apply the standard approach. In such cases the following simple logical argument shows that any optimality properties of the standard approach in the full-information situation generalize immediately to the approach in the original limited-information situation: If any better estimate were available in the limited-information situation, it would also be available in the full-information situation, which would contradict the optimality of the original estimator. This approach then provides a simple proof of optimality, and often leads directly to a simple derivation of other properties of the solution. The approach can be taught to graduate students and theoretically-inclined undergraduates. Its application to the elementary proof of a result in linear regression, and some extensions, are described in this paper. The resulting derivations provide more insight into some equivalences among models as well as proofs simpler than the standard ones.

  • This article takes data from a paper in the Journal of the American Medical Association that examined whether the true mean body temperature is 98.6 degrees Fahrenheit. Because the dataset suggests that the true mean is approximately 98.2, it helps students to grasp concepts about true means, confidence intervals, and t-statistics. Students can use a t-test to test for sex differences in body temperature and regression to investigate the relationship between temperature and heart rate.

  • The American Cancer Society and the National Cancer Institute both develop pamphlets and booklets to inform patients with cancer and their families about the nature and treatment of the illness. Written materials are often given to patients to reinforce verbal instructions, or in some cases, given in place of verbal instructions. Unfortunately, published materials may be written at a reading level that is difficult for many patients to understand.<br>The data presented here represent the readabilities of 30 booklets about cancer and the reading levels of 63 patients with cancer. A number of elementary but important statistical issues must be resolved before conclusions can be drawn. To analyze the data, students must be familiar with the notions of scales of measurement, data reduction, measuring center, constructing and interpreting displays, and reaching conclusions in real problems.

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