Teaching

  • This paper discusses a set of programs written in the statistical package Stata that is designed to support interactive student tutorials. The tutorial package has several desirable features, including customized tutorials, full student interaction, checking of student answers, repetition of practice problems using randomly chosen values, and a simple way to gauge student comprehension even when students run the tutorials at home. As an example, a tutorial used in an undergraduate econometrics class is discussed. The example illustrates Monte Carlo experiments on the linear regression model that allow students to demonstrate the validity of various formulas for the sampling distribution of ordinary least squares estimators.

  • 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.

  • This paper discusses reasons for using humor in the statistics classroom. Humor strengthens the relationship between student and teacher, reduces stress, makes a course more interesting, and, if relevant to the subject, may even enhance recall of the material. The authors provide examples of humorous material for teaching students such topics as descriptive statistics, probability and independence, sampling, confidence intervals, hypothesis testing, and regression and forecasting. Also, some references, summarized strategies, and suggestions for becoming more humorous in the classroom are provided.

  • Current recommendations for reforming statistics education include the use of cooperative learning activities as a form of active learning to supplement or replace traditional lectures. This paper describes the use of cooperative learning activities in teaching and learning statistics. Different ways of using cooperative learning activities are described along with reasons for implementing this type of instructional method. Characteristics of good activities and guidelines for the use of groups and evaluation of group products are suggested.

  • One of the main themes of statistics courses is to teach about variability, as well as location. This is especially important for non-statistics students, who often overlook variability. We consider particularly the problem of comparing variability among k samples (k > 2) that are not necessarily drawn from Gaussian populations. This can also be viewed as testing for homoskedasticity of samples. We examine tools for this problem from the perspective of their suitability for inclusion in elementary statistics courses for students of non-mathematical subjects. The ideas are illustrated by an example that arose in a student project.

  • Statistics and research design textbooks routinely highlight the importance of a priori estimation of power in empirical studies. Unfortunately, many of these textbooks continue to rely on difficult-to-read charts to estimate power. That these charts can lead students to estimate power incorrectly will not surprise those who have used them, but what is surprising is that textbooks continue to employ these charts when computer software for this purpose is widely available and relatively easy to use. The use of power charts is explored, and computer software that can be used to teach students to estimate power is illustrated using the SPSS and SAS data analysis programs.

  • So that students can acquire a conceptual understanding of basic statistical concepts, the orientation of the introductory statistics course must change from a lecture-and-listen format to one that engages students in active learning. This is the premise underlying an effort of the authors to produce and use a collection of hands-on activities that illustrate the basic concepts of statistics covered in most introductory college courses. Such activities promote the teaching of statistics more as an experimental science and less as a traditional course in mathematics. An activity-based approach enhances learning by improving the students' attention, motivation, and understanding. This paper presents examples of the types of activities that work well in various classroom settings along with comments from colleagues and students on their effectiveness.

  • Sampling distributions are central to understanding statistical inference, yet they are one of the most difficult concepts for introductory statistics students. Although hands-on teaching methods are preferred, finding the right balance between theory and practical experience has not been easy. Simulation activities have not always captured the research situations that statisticians work with. This paper describes a method developed by the author to teach sampling distributions using a collaborative learning simulation based on political polling. Anecdotally, students found the polling scenario easy to understand, interesting, and enjoyable, and they were able to explain the meaning of sample results and inferences about the population. Sample examination questions are included, with examples of students' responses that suggest that the method helped them to understand sampling error and its role in statistical inference.

  • This dataset contains observations on five groups of male fruitflies -- 25 fruitflies in each group -- from an experiment designed to test if increased reproduction reduces longevity for male fruitflies. (Such a cost has already been established for females.) The five groups are: males forced to live alone, males assigned to live with one or eight interested females, and males assigned to live with one or eight non-receptive females. The observations on each fly were longevity, thorax length, and the percentage of each day spent sleeping. The structure of the experiment provokes lively discussion on experimental design and on contrasts, and gives students opportunities to understand and verbalize what we mean by the term "statistical interaction." Because the variable thorax length has a strong effect on survival, it is important to take it into account to increase the precision of between-group contrasts, even though it is distributed similarly across groups. The dataset can also be used to illustrate techniques of survival analysis.

  • The dataset bestbuy.dat contains actual monthly data on computer usage (Millions of Instructions Per Second, MIPS) and total number of stores from August 1996 to July 2000. Additionally, information on the planned number of stores through December 2001 is available. This dataset can be used to compare time-series forecasting with trend and seasonality components and causal forecasting based on simple linear regression. The simple linear regression model exhibits unequal error variances, suggesting a transformation of the dependent variable.

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