Statistics education at the post secondary (tertiary) level



This topic will address the reality of the modern requirements of data analysis and context in statistics education. Various new approaches to practical problems have been developed in recent years: resampling, Bayesian inference, nonparametric smoothing, computer-intensive techniques, multivariate software, and data mining, among others. These innovations have been made accessible to a wide variety of researchers and professionals outside of the statistics profession. The topics in this session have been suggested to facilitate our involvement in the modernization of the statistics curricula.


4AA taxonomy of statistics coursesAlison Gibbs (Canada)
4BLess parametric methods in statisticsNoël Veraverbeke (Belgium)
4CMethods for ordinal data analysisGillian Lancaster (United Kingdom)
4DInnovations in teaching statistics at the tertiary levelMike Forster (New Zealand)
4EHeterogeneity of student levelsPenelope Bidgood (United Kingdom)
4FSensible use of multivariate softwareLisa L Harlow (United States)
4GLearning statistics through projectsNicholas Horton (United States)
4HIntegrating consulting with graduate educationIan Gordon (Australia)
4IIntegrating Bayesian methods with traditional statistics educationLaura Martignon (Germany)
4JSampling populationsPierre Lavallée (Canada)