D-optimal design – what it is and when to use it
D-optimal designs are used in screening and optimization, as soon as the researcher needs to create a non-standard design. A D-optimal design is a computer generated design, which consists of the best subset of experiments selected from a candidate set. The candidate set is the pool of theoretically possible and practically conceivable experiments.
Watch this webinar and get an introduction to D-optimal design. Our aim is to describe the D-optimality approach and to point out when to use it. In the generation of a D-optimal design, the selection of experimental runs can be driven towards fulfilling different criteria. For this purpose, we will explain two common evaluation criteria, one called the G-efficiency and the other the condition number.
We will describe a number of applications where D-optimal design is useful:
- irregular experimental regions
- combined design with process and mixture factors in the same experimental plan
- multi-level qualitative factors in screening
- optimization designs with qualitative factors
- when the desired number of runs is smaller than required by a classical design
- model updating
- inclusions of already performed experiments