Data Transformations with a Full 2^6 Experimental Design—A Metal-Cutting Case Study
Original version
Mønness, E. (2012). Data Transformations with a Full 26 Experimental Design—A Metal-Cutting Case Study. Quality Engineering 24(1), 37-48. DOI: http://dx.doi.org/10.1080/08982112.2011.616150Abstract
Abstract:
The Box-Cox transformation was evaluated with reference to a six-factor full factorial (2^6)
data set with 64 runs. The data were used to determine the optimal operating conditions for a
milling machine with respect to surface finish. A suitable transformation was determined by
minimizing the mean square errors, evaluating the size of the effect significances, the normal
probability plots of the estimated effects, Shapiro-Wilk test and the model residuals. The
achievement of both normality with constant variance and a simple model came about as a
result of a trade-off between several different criteria.
Description
Dette er preprint-versjonen av en fagfellevurdert artikkel publisert i Quality Engineering 24(1).