Confronta i metodi

	Analisi delle Corrispondenze	Biplot: Rappresentazione Simultanea di Righe e Colonne in Dati Multidimensionali
Campo	Statistica	Statistica
Famiglia	Latent structure	Latent structure
Anno di origine	1984	1971
Ideatore	Jean-Paul Benzécri; Michael Greenacre	Ruben Gabriel
Tipo	Exploratory multivariate technique for categorical data	Multivariate graphical display
Fonte seminale	Greenacre, M. J. (1984). Theory and Applications of Correspondence Analysis. Academic Press. ISBN: 978-0-12-299050-2	Gabriel, K. R. (1971). The biplot graphic display of matrices with application to principal component analysis. Biometrika, 58(3), 453–467.
Alias	CA, Simple Correspondence Analysis, Reciprocal Averaging, Karşılıklı Uyum Analizi	Gabriel biplot, PCA biplot, JK biplot, Çift grafik
Correlati	2	2
Sintesi	Correspondence Analysis (CA) is an exploratory multivariate technique for visualizing the association structure of a two-way contingency table. Developed systematically by Jean-Paul Benzécri in France during the 1960s–1970s and brought to an English-language audience by Michael Greenacre in 1984, CA decomposes the chi-square statistic of a cross-tabulation to produce a low-dimensional joint display — called a biplot — in which rows and columns are represented as points whose proximities reflect their associations.	A biplot is a low-dimensional graphical representation of a multivariate data matrix that simultaneously displays both the observations (rows) and the variables (columns) as points or vectors in the same plot. Introduced by Ruben Gabriel in 1971, the technique decomposes the data matrix into a rank-2 approximation using singular value decomposition, allowing the approximate value of any data entry to be read as the inner product of the corresponding row and column markers.