Salīdzināt metodes

	Atbilstības analīze	Biplots: vienlaicīga rindu un kolonnu attēlošana daudzdimensiju datos
Nozare	Statistika	Statistika
Saime	Latent structure	Latent structure
Izcelsmes gads	1984	1971
Autors	Jean-Paul Benzécri; Michael Greenacre	Ruben Gabriel
Tips	Exploratory multivariate technique for categorical data	Multivariate graphical display
Pirmavots	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.
Citi nosaukumi	CA, Simple Correspondence Analysis, Reciprocal Averaging, Karşılıklı Uyum Analizi	Gabriel biplot, PCA biplot, JK biplot, Çift grafik
Saistītās	2	2
Kopsavilkums	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.