Collaborative Research: Non-parametric Computer Intensive Statistics Course for Engineers

协作研究：工程师非参数计算机强化统计课程

• 批准号: 0632868
• 负责人: Trent McDonald
• 金额: --
• 依托单位: Western EcoSystems Technology Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-02-15 至 2011-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0632868&HistoricalAwards=false
• 关键词: Collaborative; Research; Non; parametric; Computer

Engineering Other (59)This project is developing materials for a course in non-parametric computer intensive (ONCPI) statistics. This course is distinctly different from existing undergraduate statistics courses in that the NPCI methods do not depend on assumed distribution functions (non-parametric) and rely heavily on computer sampling (computer intensive). By teaching the basic concepts of replication, variation, and counting in a hands-on environment, students are gaining an immediate intuitive understanding of statistics, as opposed to memorizing a series of poorly understood statistical 'recipes'. The practical results are that statistical concepts are more transparent, more sophisticated statistics can be carried out, and the course can be taken earlier in an engineering curriculum than a traditional parametric calculus based course. The PIs are developing nine modules for the NCPI course, utilizing selected modules in a junior level civil engineering course.

工程其他（59）这个项目是在非参数计算机密集型（ONCPI）统计学课程开发材料。 本课程与现有的本科统计学课程明显不同，因为NPCI方法不依赖于假设的分布函数（非参数），并且严重依赖于计算机采样（计算机密集型）。 通过在实践环境中教授复制，变异和计数的基本概念，学生可以立即直观地理解统计学，而不是记住一系列理解不清的统计“食谱”。 实际结果是，统计概念更透明，更复杂的统计可以进行，当然可以采取更早的工程课程比传统的参数微积分为基础的课程。 项目主管正在利用初级土木工程课程中的选定单元，为NCPI课程开发九个单元。