A methodology for determining the power of MANOVA when the observations are serially correlated

Bibliographic Details
Main Author:	Eyrich, Norviel Robert
Published:	Georgia Institute of Technology 2009
Subjects:	Analysis of variance Multivariate analysis
Online Access:	http://hdl.handle.net/1853/31089

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spelling	ndltd-GATECH-oai-smartech.gatech.edu-1853-310892013-12-15T03:45:02ZA methodology for determining the power of MANOVA when the observations are serially correlatedEyrich, Norviel RobertAnalysis of varianceMultivariate analysisGeorgia Institute of Technology2009-11-05T12:31:10Z2009-11-05T12:31:10Z1975-12Thesis92823http://hdl.handle.net/1853/31089
collection	NDLTD
sources	NDLTD
topic	Analysis of variance Multivariate analysis
spellingShingle	Analysis of variance Multivariate analysis Eyrich, Norviel Robert A methodology for determining the power of MANOVA when the observations are serially correlated
author	Eyrich, Norviel Robert
author_facet	Eyrich, Norviel Robert
author_sort	Eyrich, Norviel Robert
title	A methodology for determining the power of MANOVA when the observations are serially correlated
title_short	A methodology for determining the power of MANOVA when the observations are serially correlated
title_full	A methodology for determining the power of MANOVA when the observations are serially correlated
title_fullStr	A methodology for determining the power of MANOVA when the observations are serially correlated
title_full_unstemmed	A methodology for determining the power of MANOVA when the observations are serially correlated
title_sort	methodology for determining the power of manova when the observations are serially correlated
publisher	Georgia Institute of Technology
publishDate	2009
url	http://hdl.handle.net/1853/31089
work_keys_str_mv	AT eyrichnorvielrobert amethodologyfordeterminingthepowerofmanovawhentheobservationsareseriallycorrelated AT eyrichnorvielrobert methodologyfordeterminingthepowerofmanovawhentheobservationsareseriallycorrelated
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A methodology for determining the power of MANOVA when the observations are serially correlated

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