Design of experiments (DOE) is a data-driven strategy that helps practitioners to better understand the underlying behavior of a system by identifying those factors that most affect desired outcomes. By optimizing your experimental design, you can more effectively take covariate information into account when setting up an experiment.
In this recording of a live seminar, DOE expert Bradley Jones shows how you can accelerate learning cycles with a new, more flexible method for experimentation. He demonstrates 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.
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.