Affiliations: [a] Department of Automatics, Mechanics and Informatics, University of Valenciennes, Valenciennes, France | [b] Department of Applied Mathematics, University of Valenciennes, Valenciennes, France | [c] Department of Reliability, Bombardier Transport, Crespin, France
Correspondence:
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Corresponding author: Pierre Loslever, Université de Valenciennes et du Hainaut-Cambrésis Le Mont Houy, F-59313 Valenciennes, Cedex 9, France. Tel.: +33 6 81 16 55 53; Fax: +33 3 27 51 13 16; E-mail: [email protected]
Abstract: Observational and experimental data are often investigated into so that the factor effects and/or variables connections can be assessed quickly and easily via inference tests. This article suggests starting the statistical analysis using a 5-step descriptive procedure: 1) Data characterization, 2) Data coding, 3) Data table drafting, 4) Data table analysis and 5) Result presentation. In order to illustrate this preliminary statistical analysis, two data set examples are considered –one from a small simulated system and one from a large mechatronic system– using two different methods: Principal Component Analysis with usual statistical summaries and Multiple Correspondence Analysis with indicators obtained through fuzzy space windowing. In an Intelligent Data Analysis context, the discussion weighs out the pros and the cons of these approaches, prior to using procedures 5-step inference procedures.
Keywords: Descriptive multivariate analysis, fuzzy windowing, multiple correspondence analysis, principal component analysis, experimental test bench, train door system
