COMPARING RANDOMIZATION AND A RANDOM RUN …：比较的随机和随机运行….pptVIP

J. M. Lucas and Associates New Results about Randomization and Split-Plotting by James M. Lucas 2003 Quality Productivity Research Conference Yorktown Heights, New York May 21-23, 2003 Contact Information James M. Lucas J. M. Lucas and Associates 5120 New Kent Road Wilmington, DE 19808 (302) 368-1214 JamesM.Lucas@worldnet.att.net Research Team Huey Ju Jeetu Ganju Frank Anbari Peter Goos Malcolm Hazel Derek Webb John Borkowski PRELIMINARIES  How do you run Experiments? QUESTIONS How many of you are involved with running experiments? How many of you “randomize” to guard against trends or other unexpected events? If the same level of a factor such as temperature is required on successive runs, how many of you set that factor to a neutral level and reset it? ADDITIONAL QUESTIONS How many of you have conducted experiments on the same process on which you have implemented a Quality Control Procedure? What did you find? Randomized Not Reset (RNR) Experiments A large fraction (perhaps a large majority) of industrial experiments are Randomized not Reset (RNR) experiments Properties of RNR experiments and a discussion of how experiments should be conducted: “Lk Factorial Experiments with Hard-to-Change and Easy-to-Change Factors” Ju and Lucas, 2002, JQT 34, 411-421 [studies one H-T-C factor and uses Random Run Order (RRO) rather than RNR] “Factorial Experiments when Factor Levels Are Not Necessarily Reset” Webb, Lucas and Borkowski, 2003, JQT, to appear [studies 1 HTC Factor] Results for Experiments with Hard-to-Change and Easy-to-Change Factors One H-T-C or E-T-C Factor: use split-plot blocking Two H-T-C Factors: may split-plot Three or more H-T-C Factors: consider RNR or Low Cost Options Consider “Diccon’s Rule”: Design for the H-T-C Factor New Results Joint work with Peter Goos Builds on the Kiefer-Wolfowitz Equivalence Theorem Implications about Computer generated designs (especially when there are Hard-to-Chang