Brumbaugh Award winner Bradley Jones is perhaps best known for his sizeable contribution to the concept of design of experiments (DOE) and to the field of statistics more broadly. But distinguished career aside, Jones has found other ways to work experimentation and praxis into his daily life—beginning not with statistics as one might assume, but rather with music.
In fact, Jones began his education at the Cincinnati Conservatory where he trained as a classical violinist. Though he no longer plays professionally, Jones says he continues to practice every day. Music and math may go hand in hand, as some of have claimed, but this connection is probably better exemplified in Jones’ penchant for the East Asian chess-like strategy game, go—a book about which Jones once published.
The meticulously strategic thinking required by go players parallels closely the philosophy behind design of experiments. Like go, design of experiments is not difficult to learn, and an analytical approach in both outlets can really pay off. In this one-day seminar, Jones will show how you can accelerate learning cycles with a general, flexible method for experimentation. He’ll demonstrate how to use optimal designs which reduce the cost of experimentation, accommodate multiple types of factors and can be optimized when the design space is constrained.
Watch Bradley Jones speaking with Chris Nachtsheim in the JMP series Analytically Speaking
This seminar is based on real industrial case studies from Optimal Design of Experiments, a seminal text by Bradley Jones and Peter Goos of the University of Antwerp. Among other things, you will learn how to use modern, computer-based methods to:
- Find the few factors that most affect the response of interest.
- Resolve ambiguity about what model best describes the underlying behavior of the system.
- Deal with a problem wherein the blocks cannot be orthogonal to the other factor effects in the model.
- Investigate the behavior of a chemical reaction using a full cubic model in two factors -- even when many of the factor-level combinations are known in advance to be infeasible.
- Take covariate information into account when setting up an experiment.
This seminar will appeal to new practitioners and experts alike. Optimal DOE is available in JMP® and is appropriate in virtually any situation that suggests the possible use of DOE. And please feel free to invite your colleagues! We’ve heard from previous attendees that when they participate with their co-workers, the whole team benefits.