Abstract
Correspondence factor analysis is a multivariate technique that may be applied to any type of data and to any number of data points. It detects associations and oppositions existing between subjects and objects, measuring their contribution to the total inertia for each factor. The probabilistic character of the data matrix is taken into consideration, and together with the principle of distributional equivalence, results in stability. The projection of the subjects and the objects onto the same set of factorial axes enables two-dimensional graphs to be drawn which offer aid in the interpretation of the results.
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