Treatment of Non-normal Responses for Designed Experiments

dc.contributor.author	OYEYEMI, G. M.
dc.date.accessioned	2023-07-19T14:13:47Z
dc.date.available	2023-07-19T14:13:47Z
dc.date.issued	2004
dc.description.abstract	Many experimental designs, most especially industrial designs produce non-normal response variables. The Least Squares method of modeling may therefore not produce efficient estimates. Models are built using B-technique and Box-Cos methods of data transformation or by using GLM to overcome the non-normality nature of the data. In this paper, these techniques are compared using histograms of the estimated mean responses and by examining the length of the confidence interval about the mean responses. It is observed that B-technique is the best method of data transformation but GLM provides an excellent alternative if the experimental design points are nor replicated.	en_US
dc.description.sponsorship	Self sponsored	en_US
dc.identifier.issn	0331-9504
dc.identifier.uri	https://uilspace.unilorin.edu.ng/handle/20.500.12484/11587
dc.language.iso	en	en_US
dc.publisher	Nigeria Statistical Association	en_US
dc.relation.ispartofseries	17;9 - 19
dc.subject	B-Technique, Robust Parameter Design, Generalized Linear Model	en_US
dc.title	Treatment of Non-normal Responses for Designed Experiments	en_US
dc.type	Article	en_US