Analyzing Designed Experiments with Multiple Responses

This paper is an overview of a unified framework for analyzing designed experiments with univariate or multivariate responses. Both categorical and continuous design variables are considered. To handle unbalanced data, we introduce the so-called Type II* sums of squares. This means that the results are independent of the scale chosen for continuous design variables. Furthermore, it does not matter whether two-level variables are coded as categorical or continuous. Overall testing of all responses is done by 50-50 MANOVA, which handles several highly correlated responses. Univariate p-values for each response are adjusted by using rotation testing. To illustrate multivariate effects, mean values and mean predictions are illustrated in a principal component score plot or directly as curves. For the unbalanced cases, we introduce a new variant of adjusted means, which are independent to the coding of two-level variables. The methodology is exemplified by case studies from cheese and fish pudding production.

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Year of publication:	2007
Authors:	Langsrud, Øyvind ; Jørgensen, Kjetil ; Ofstad, Ragni ; Næs, Tormod
Published in:	Journal of Applied Statistics. - Taylor & Francis Journals, ISSN 0266-4763. - Vol. 34.2007, 10, p. 1275-1296
Publisher:	Taylor & Francis Journals
Subject:	50-50 MANOVA \| general linear model \| least-squares means \| multiple testing \| principal component \| rotation test \| unbalanced factorial design

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Extent:	text/html
Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://ebvufind01.dmz1.zbw.eu/10005458405