Statistics education at the post secondary (tertiary) level
Convenors
- Elisabeth Svensson (Sweden)
- Larry Weldon (Canada)
Abstract
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.Sessions
| Session | Title | Organizer |
| 4A | A taxonomy of statistics courses | Alison Gibbs (Canada) |
| 4B | Less parametric methods in statistics | Noël Veraverbeke (Belgium) |
| 4C | Methods for ordinal data analysis | Gillian Lancaster (United Kingdom) |
| 4D | Innovations in teaching statistics at the tertiary level | Mike Forster (New Zealand) |
| 4E | Heterogeneity of student levels | Penelope Bidgood (United Kingdom) |
| 4F | Sensible use of multivariate software | Lisa L Harlow (United States) |
| 4G | Learning statistics through projects | Nicholas Horton (United States) |
| 4H | Integrating consulting with graduate education | Ian Gordon (Australia) |
| 4I | Integrating Bayesian methods with traditional statistics education | Laura Martignon (Germany) |
| 4J | Sampling populations | Pierre Lavallée (Canada) |