Yulia R. Gel, Department of Statistics and Actuarial Science
with: Abraham Bovas, Stefan Steiner, Sean Scott
The specific focus of this project is to develop and assess the effectiveness of various novel e-learning techniques for teaching time series and applied regression analysis in undergraduate courses in statistics. In particular, we plan to develop a new e-learning tool that illustrates the key methodological time series and regression concepts and their applications to real-life problems through students’ use of self-learning and self-assessing modules of case studies.
Inventory, production, scheduling of personnel and machinery, financial and marketing decisions are made which depend on short, medium and long-term forecasts. A good data analyst must be able to quickly identify a bridge between available data and the most relevant modelling technique available in order to provide reliable inferences about the future and to adapt the selected statistical model to new changes that arise. The proposed e-learning tool can assist students by providing invaluable practical skills for data analysis and to intensify their theoretical knowledge. We believe that given current trends in learning methodology, our e-tool can eventually become a key instrument for training future forecasters and data analysts.