We provide an expository presentation of multivariate analysis of variance (MANOVA) for both consumers of research and investigators by capitalizing on its relation to univariate analysis of variance models. We address several questions: (a) Why should one use MANOVA. 9 (b) What is the structure of MANOVA? (C) How are MANOVA test statistics obtained and interpreted? (d) How are MANOVA follow-up...

MOTIVATION Gene class testing (GCT) or gene set analysis (GSA) is a statistical approach to determine whether some functionally predefined sets of genes express differently under different experimental conditions. Shortcomings of the Fisher's exact test for the overrepresentation analysis are illustrated by an example. Most alternative GSA methods are developed for data collected from two exper...

Multivariate Analysis of Variance (MANOVA) is a popular statistical tool in the social sciences, allowing for the comparison of mean vectors across groups. MANOVA rests on three primary assumptions regarding the population: (a) multivariate normality, (b) equality of group population covariance matrices and (c) independence of errors. When these assumptions are violated, MANOVA does not perform...

We have developed an adaptation of multi-variate analysis of variance (MANOVA) to analyze statistically both local and global patterns of multi-electrode array (MEA) electrophysiology data where the activities of many (typically >100) neurons have been recorded simultaneously. Whereas simple application of standard MANOVA techniques prohibits extraction of useful information in this kind of dat...

ABSTRACT In this paper, we have used SAS software for the multivariate analysis of repeated measures data due to Grizzel and Allen (1969). We have applied four multivariate methods viz MANOVA, Profile Analysis, non-parametric multisample rank sum test and non-parametric multisample median test to analyse two sets of data. The findings of the study reveal that profile analysis gives similar resu...