Muvara
expert in pattern analysis
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About Muvara


The trade name MUVARA is an abbreviation of multi variate analysis. Multivariate analysis means to analyse a large number of factors with special interest in getting an overview of all their mutual relations. These factors can for example be the questions of an inquiry, in which the similarities and differences in the whole answering pattern of the interviewed customers are visualised in one simple graphical representation: the smaller the distance between two customers positioned on this inquiry map, the more their complete answering pattern is the same. Furthermore the inquiry map can show some clusters of the customers interviewed and one can determine if there is some sort of segmentation or clear discrimination between groups of customers. On top of this is revealed which answering patterns are dominant. In the foregoing example an interview of customers is mentioned, but the analysis can be applied to all kinds of factors, such as plant products, medical files and thinking patterns.


About Dré Nierop
Dré Nierop

The director of MUVARA is Dré Nierop. He is a graduate in biology and received a Ph.D. in data theory. Since 1976 he has been working on the application and development of new pattern analysis and predictive methods for both quantitative and qualitative data. He has worked for a large number of businesses and foundations in this field for many years, such as the Max Planck Institute, the Dutch Organisation for Scientific Research, the Dutch Prevention Foundation, the Dutch institute for deaf and hard of hearing children, the Dutch Cara Foundation, the Centre of Bio-Farmaceutical Sciences, TNO Nutrition, and the Faculty of Social Sciences of the University of Leiden, where he finished his Ph.D. thesis in 1993 in the department of Data Theory (Multidimensional analysis of grouped variables: An integrated approach. Leiden: DSWO Press, Leiden University). In 1994 he started his company MUVARA on a full-time basis, since he saw in the market not so much a lack of statistical packages, but rather that the statistical packages are not always attuned to the actual problems in businesses and institutions.