Teaching

  • For the past twenty years, we have been using an original technique to teach statistics and survey sampling methods to postgraduates studying economics and statistics. The students must put their knowledge into practice by carrying out a survey sample for a client who they will have found by themselves. This may include a marketing study for a shop, a brand or a public service, or measuring the audience ratings of a radio station or local television station. More than 100 different surveys have already been carried out by students on this program over the last 20 years. Furthermore, every six years, during the regional parliamentary elections, the entire group (25 students) carries out an estimate of the results for the public local television station, on the basis of the first ballot papers counted in a sample of 300 polling stations; our results are broadcast live on television 30 minutes after the close of polling.

  • At the present time, frequentist ideas dominate most statistics undergraduate programs, and the exposure to Bayesian ideas in undergraduate statistics is very limited. There are historical reasons for this frequentist dominance. Efron (1986) concluded that frequentists had captured the high ground of objectivity (p. 4). Bayesian methods have superior performance, often even outperforming frequentist procedures when evaluated under frequentist criteria. In the past, Bayesian methods were of limited practical use, since analytic solutions for the Bayesian posterior distributions were only possible in a few cases, and the numerical calculation of the posterior often was not feasible because of lack of computer power. Recent developments in computing power, and the development of Markov chain methods for sampling from the posterior have made Bayesian methods possible, even in very complicated models. It is clearly unsatisfactory for our profession that most of our students are not being introduced to the best methods available. In this paper I make a proposal for how our profession should deal with this challenge, by giving my answers to the journalistic "who, what, where, when, why, and how" questions about the place of Bayesian Statistics in undergraduate statistical education.

  • This paper reviews the past and current interest in using Bayesian thinking to introduce statistical inference. Rationale for using a Bayesian approach is described and particular methods are described that make it easier to understand Bayes' rule. Several older and modern introductory statistics books are reviewed that use a Bayesian perspective. It is argued that a Bayesian perspective is very helpful in teaching a course in statistical literacy.

  • Some ideas about how basic aspects of nonparametric curve estimation can be introduced to students at a post secondary level will be discussed here. The idea of estimating population curves, like the density or the regression function, is studied from a nonparametric viewpoint. Starting from well-known estimators as the histogram or the regressogram, the discussion will then go to some of the smoothing methods developed in the last four decades, mainly focusing on the kernel density and regression estimators. Some ideas about the important problem of smoothing parameter selection will also be presented.

  • The aim of this paper is to show how teachers can use Visual Basic language on the spreadsheet for student sin order to gain the skills need in using nonparametric techniques for density and regression function estimations. The author has built a useful didactic tool on Excel Visual Basic for teaching and practice of nonparametric kernel methods, being intended for students with some preliminary knowledge on this topic. This Visual Basic Application (VBA) is loaded into Excel as a MACRO (or into the modules of a Workbook for EXCEL). The specific user functions incorporated into it are easy tools for students to obtain an intuitive perception of nonparametric estimation for density and regression functions. The VBA also allows Excel to make use of an added menu similar to a small Statistical Package specialised in nonparametric methods.

  • Teaching future applied statisticians requires the teaching of consultation skills since the student must learn to interact with research workers, learn to abstract the statistical aspects of substantive problems, to provide appropriate technical assistance, and to effectively communicate statistical results. The approach of the Department of Biostatistics at the University of North Carolina at Chapel Hill is to provide a dual training that includes classroom work, but also involves a 'real' practicum. The objective of this paper is to present various modalities of the experience in training future consultants. These are evaluated by former students of the Department of Biostatistics that are currently involved in consultation and in training in their respective countries. The success of the training is seen through subsequent consultations in worldwide settings.

  • The status of Statistics teaching has not been sufficiently explored in Agricultural Colleges in Argentina. Although Statistics is considered as an important subject in different academic institutions, there is very little information about the way that it is taught in different university curricula. The aims of this study were to (a) gather information about the place of Statistics in college programs through different indicators, and (b) explore different issues concerning the teaching of Statistics in agricultural colleges, such as epistemological views, academic organization, etc. For this purpose, a survey was conducted in the main agricultural colleges in Argentina. Twenty-three teaching teams from different university answered a questionnaire. The responses were analyzed and categories were built in order to draw some conclusions.

  • Little is known about the provision of statistics teaching for PhD students in UK medical schools. A recent survey found that statistics courses were available to PhD students in 13 of 21 schools responding. The provision across these 13 schools was variable in terms of contact hours and content. At a meeting of 27 medical statistics teachers, consensus was reached that such teaching should be undertaken by a subject specialist, however there was no consensus as to the best mode of delivery. We describe the rationale for, content of, and student feedback from our newly developed course programme which emphasises aspects of both design and analysis of research projects.

  • This case study covers several exploratory data analysis ideas, the histogram and boxplot, kernel density estimates, the recently introduced bagplot - a two-dimensional extension of the boxplot - as well as the violin plot, which combines a boxplot with a density shape plot. We apply these ideas and demonstrate how to interpret the output from these tools in the context of data on living standards in Vietnam. The level of the presentation is suitable for an upper-level undergraduate or beginning graduate course in applied statistics. We use data from the Vietnam Living Standards Survey of 1998 (VLSS98) and from the 2000 Vietnam statistical yearbook, the statistical package Stata, and special programs provided by the authors who introduced the bagplot and the violin plot.

  • This paper extends work on the construction of instructional modules that use graphical and simulation techniques for teaching statistical concepts (Marasinghe, et al. 1996; Iversen and Marasinghe 2001). These modules consist of two components: a software part and a lesson part. A computer program written in LISP-STAT with a highly interactive user interface that the instructor and the students can use for exploring various ideas and concepts comprises the software part. The lesson part is a prototype document providing guidance for instructors for creating their own lessons using the software module. This includes a description of concepts to be covered, instructions on how to use the module and some exercises. The regression modules described here are designed to illustrate various concepts associated with regression model fitting such as the use of residuals and other case diagnostics to check for model adequacy, the assessment of the effects of transforming the response variable on the regression fit using well-known diagnostic plots and the use of statistics to measure effects of collinearity on model selection.

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