Design and analysis of experiments is sometimes seen as an area of statistics in which there are few new problems. I will argue that modern industrial processes, often with automatic data collection systems, require advances in the methodology of designed experiments if they are to be applied successfully in practice. The basic philosophy of design will be reexamined in this context. Experiments can now be designed to maximise the information in the data without computational restrictions limiting either the data analysis that can be done or the search for a design. Very large amounts of data may be collected from each experimental unit and various empirical modelling techniques may used to analyse these data. In order to ensure that the data contain the required information, it is vital that attention be paid to the experimental design, the sampling design and any mechanistic information that can be built into the model.
Meet the speaker in Room 212 Cockins Hall at 4:30 p.m. Refreshments will be served.