Data Transformations with a Full 2⁶ Experimental Design—A Metal-Cutting Case Study

REFERENCES

• Amemiya , T. , Powell , J. L. ( 1981 ). A comparison of the Box-Cox maximum likelihood estimator and the non-linear two-stage least squares estimator . Journal of Econometrics , 17 ( 3 ): 351 – 381 .
   Google Scholar
• Box , G. E. P. , Cox , D. R. ( 1964 ). An analysis of transformations . Journal of the Royal Statistical Society, Series B , 26 ( 2 ): 211 – 252 .
   Google Scholar
• Box , G. E. P. , Hunter , J. S. , Hunter , W. G. ( 2005 ). Statistics for Experimenters: Design, Innovation and Discovery . John Wiley & Sons , Hoboken , NJ .
   Google Scholar
• Box , G. E. P. , Hunter , W. G. , Hunter , J. S. ( 1978 ). Statistics for Experimenters . John Wiley & Sons , Hoboken , NJ .
   Google Scholar
• Dagenais , M. G. , Dufour , J. M. ( 1994 ). Pitfalls of rescaling regression-models with Box-Cox-transformations . Review of Economics and Statistics , 76 ( 3 ): 571 – 575 .
   Google Scholar
• Daniel , C. ( 1976 ). Applications of Statistics to Industrial Experimentation . New York : Wiley .
   Google Scholar
• Freeman , J. , Modarres , R. ( 2006 ). Inverse Box-Cox: The power-normal distribution . Statistics & Probability Letters , 76 ( 8 ): 764 – 772 .
   Web of Science ®Google Scholar
• Garzon , I. ( 2000 ). Optimisation for product and process improvement: Investigation of Taguchi tools and genetic algorithms. Ph.D. dissertation, ISRU, University of Newcastle Upon Tyne.
   Google Scholar
• Goto , M. , Inoue , T. ( 1980 ). Some properties of the power normal distribution . Bulletin of the Biometric Society of Japan , 1 : 28 – 54 .
   Google Scholar
• Gurka , M. J. , Edwards , L. J. , Muller , K. E. , Kupper , L. L. ( 2005 ). Extending the Box-Cox transformation to the linear mixed model . Journal of the Royal Statistical Society, Series A , 169 ( 2 ): 273 – 288 .
   Google Scholar
• Hinkelmann , K. , Kempthorne , O. ( 1994 ). Design and Analysis of Experiments . New York : John Wiley & Sons .
   Google Scholar
• Kalpakjian , S. , Ed. ( 1992 ). Manufacturing, Engineering & Technology, , 2nd ed. Reading , MA : Addison Wesley .
   Google Scholar
• Maekawa , K. ( 1998 ). Computational aspects of tribology in metal machining . Proceedings of the Institution of Mechanical Engineers - Part J: Journal of Engineering Tribology , 212 ( 4 ): 307 – 318 .
   Web of Science ®Google Scholar
• Mills , B. , Redford , A. H. ( 1983 ). Machinability of Engineering Materials . London : Applied Science .
   Google Scholar
• Mønness , E. ( 2011 ). The power-normal distribution: Application to forest stands . Canadian Journal of Forest Research , 41 ( 4 ): 707 – 714 .
   Google Scholar
• Mønness , E. , et al. . ( 2007 ). Comparing different fractions of a factorial design: A metal cutting case study . Applied Stochastic Models in Business and Industry , 23 : 117 – 128 .
   Google Scholar
• Nelder , J. A. ( 1968 ). Weighted regression, quantal response data, and inverse polynomials . Biometrics , 24 ( 4 ): 979 – 985 .
   Google Scholar
• Searle , S. R. ( 1971 ). Linear Models . New York : John Wiley & Sons .
   Google Scholar
• Shapiro , S. S. , Wilk , M. B. ( 1965 ). An analysis of variance test for normality . Biometrica , 52 : 591 – 611 .
   Web of Science ®Google Scholar
• Sleeper , A. ( 2005 ). Design for Six Sigma Statistics . New York : McGraw-Hill Professional .
   Google Scholar
• SYSTAT . ( 2004 ). SYSTAT 11.0 . Chicago , IL . Available at: http://www.systat.com (accessed March 15, 2005).
   Google Scholar
• Wu , J. , Hamada , M. ( 2000 ). Experiments Planning, Analysis, and Parameter Design Optimization . New York : Wiley .
   Google Scholar