Statistical Tools for Chemical Process and Product Design and Improvement

数学模型对于通过设计、监控和控制来推进产品和工艺非常重要。这些模型往往在方程的数量、形式和相互关联性方面都很复杂。统计技术对于开发模型、估计参数以及确定我们在多大程度上改进了制造性能和产品特性非常重要。工程师和科学家需要统计技术来评估他们估计参数的准确度，确定哪些是重要的，检查预测的质量，并设计额外的实验来提高模型性能和物理理解。产品的改进为分析间接测量的性能(例如聚合物的强度)或复杂的特性(例如颗粒形状)带来了额外的统计需求。测量和表征产品的仪器的快速发展为统计分析提供了新的数据类型，给统计分析带来了新的挑战。

Mathematical models are important for advancing products and processes through design, monitoring, and control. These models are often complex, in the number, form and interconnectedness of equations. Statistical techniques are important for developing models, estimating parameters, and determining how much we have improved manufacturing performance and product characteristics. Engineers and scientists need statistical techniques to assess how well they can estimate parameters, to identify which are important, to check the quality of predictions, and to design additional experiments that improve model performance and physical understanding. Product improvement brings additional statistical needs for analyzing properties that are measured indirectly (e.g., strength of a polymer), or characteristics that are complex (e.g., particle shapes). Rapid advances in instruments for measuring and characterizing products have provided new kinds of data, with new challenges for statistical analysis.