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1. Prolegomena
1.1 Contents
The methods listed for data analysis fall into two groups (factorial and classification), depending on their purpose and the nature of the variables considered (quantitative or qualitative).
Various factorial methods can be deduced from principal component analysis or PCA (p quantitative variables): factorial correspondence analysis (FCA) (2 qualitative variables), multiple correspondence analysis (MCA) (p qualitative variables), discriminant factorial analysis (one qualitative variable and p quantitative variables), multidimensional positioning (distance table), canonical correlation analysis (p and q quantitative variables). This last method, much less widely used, is not described in this article. PCA is therefore given...
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Prolegomena
Principal component analysis (PCA)
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The most useful general and introductory references for this theme are: Bouroche & Saporta (1980), Jobson (1992), Lebart, Morineau & Piron (2006), Mardia, Kent & Bibby (1979), Saporta (2006). More recent additions and developments can be found in: Droesbeke, Fichet & Tassi (1992), Govaert (2003).
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