Mathematical Sciences: Building Designs for Collecting Better Data and Making Finer Conclusions

我们计划确定最佳和有效的统计 设计在几个常用的科学研究。这些领域 包括但不限于，主因子两水平析因设计 影响和选定的双因素相互作用;决议 和多个两水平部分析因设计（弱） 最小像差;比较用重复测量设计 生物利用度研究。 基于通过设计不良的 研究通常具有误导性，可能完全无效。 好的科学需要好的数据，好的数据只有在 通过统计设计的研究。该提案计划 确定和建立统计上有效的设计/单位成本 以便收集更好的数据， 可以进行推断。我们的发现将立即 应用于许多科学研究领域，包括 医学和工程学我们计划解决设计问题， 美国FDA的科学家向我们提出， 行业

We plan to identify optimal and efficient statistical designs in several commonly used scientific studies. These areas include, but not limite to, two-level factorial designs for main effects and selected two-factor interactions; resolution III and more two-level fractional factorial designs with (weak) minimum aberration; repeated measurements designs for comparative bioavaliability studies. Inference based on data collected via poorly designed studies is generally misleading and could be invalid altogether. Good science calls for good data and good data can come only through statistically designed studies. This proposal plans to identify and build statistically efficient designs/per unit cost so that better data can be collected and valid scientific inference can be made. Our discoveries will have immediate applications to many areas of scientific studies including medicine and engineering. We plan to solve design problems which have been posed to us by scientists at US FDA, and many US industries.