Name
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.3233/IDA-2012-0524 | Intell. Data Anal. |
Keywords | Field | DocType |
intelligent data analysis context,descriptive analysis,principal component analysis,data table analysis,data characterization,statistical analysis,multiple correspondence analysis,preliminary statistical analysis,scale fuzzy windowing comparison,data table,experimental data,usual statistical summary | Relationship square,Data mining,Multiple correspondence analysis,Experimental data,Multivariate statistics,Inference,Computer science,Fuzzy logic,Artificial intelligence,Correspondence analysis,Machine learning,Principal component analysis | Journal |
Volume | Issue | ISSN |
16 | 2 | 1088-467X |
Citations | PageRank | References |
0 | 0.34 | 8 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| P. Loslever | 1 | 22 | 4.78 |
| L. Cauffriez | 2 | 19 | 3.26 |
| N. Caouder | 3 | 0 | 0.34 |
| F. Turgis | 4 | 0 | 0.34 |
| R. Copin | 5 | 0 | 0.34 |