Mathematical Sciences: Topics in Economical Experimental Designs

本研究考察了一阶饱和、过饱和和最小电平变化设计在最大限度地减少区分影响因素和仅贡献噪声的影响因素所需的实验数量的问题中的作用。工业环境中的最佳实验设计也可能以实验数量最小化为目标。理论解决方案往往依赖于对可能的影响因素数量及其相互作用的不切实际的假设。本研究的重点是一种高效和有效的方法来设计最小系列的实验，以确定这些因素，目标是预测全面生产的最佳因素组合或因素设置。

This research examines the roles of first-order saturated, supersaturated and minimal level change designs in the problem of minimizing the number of experiments required to differentiate the influential factors from those only contributing noise. Optimal experimental design in an industrial setting may also have as an objective the minimization of the number of experiments. Theoretical solutions have often depended upon unrealistic assumptions about the likely number of influential factors and their interactions. This research focuses on an efficient and effective approach to the design of a minimal series of experiments to identify those factors with the goal of predicting the optimal combination of factors or factor settings for full-scale production.