Due to operational or physical considerations, standard factorial and response surface method （RSM） design of experiments （DOE） often prove to be unsuitable. In such cases a computer-generated statistically-optima...Due to operational or physical considerations, standard factorial and response surface method （RSM） design of experiments （DOE） often prove to be unsuitable. In such cases a computer-generated statistically-optimal design fills the breech. This article explores vital mathematical properties for evaluating alternative designs with a focus on what is really important for industrial experimenters. To assess ＂goodness of design＂ such evaluations must consider the model choice, specific optimality criteria （in particular D and I）, precision of estimation based on the fraction of design space （FDS）, the number of runs to achieve required precision, lack-of-fit testing, and so forth. With a focus on RSM, all these issues are considered at a practical level, keeping engineers and scientists in mind. This brings to the forefront such considerations as subject-matter knowledge from first principles and experience, factor choice and the feasibility of the experiment design.展开更多
文摘Due to operational or physical considerations, standard factorial and response surface method （RSM） design of experiments （DOE） often prove to be unsuitable. In such cases a computer-generated statistically-optimal design fills the breech. This article explores vital mathematical properties for evaluating alternative designs with a focus on what is really important for industrial experimenters. To assess ＂goodness of design＂ such evaluations must consider the model choice, specific optimality criteria （in particular D and I）, precision of estimation based on the fraction of design space （FDS）, the number of runs to achieve required precision, lack-of-fit testing, and so forth. With a focus on RSM, all these issues are considered at a practical level, keeping engineers and scientists in mind. This brings to the forefront such considerations as subject-matter knowledge from first principles and experience, factor choice and the feasibility of the experiment design.
