Salīdzināt metodes

	Daudzveidu atbilstības analīze (MCA)	Biplots: vienlaicīga rindu un kolonnu attēlošana daudzdimensiju datos
Nozare	Statistika	Statistika
Saime	Latent structure	Latent structure
Izcelsmes gads	2006	1971
Autors	Greenacre & Blasius	Ruben Gabriel
Tips	Multivariate exploratory ordination	Multivariate graphical display
Pirmavots	Greenacre, M., & Blasius, J. (Eds.). (2006). Multiple Correspondence Analysis and Related Methods. Chapman & Hall/CRC. ISBN: 978-1-58488-628-0	Gabriel, K. R. (1971). The biplot graphic display of matrices with application to principal component analysis. Biometrika, 58(3), 453–467.
Citi nosaukumi	MCA, Homogeneity Analysis, Multiple Nominal Component Analysis, Çoklu Uyum Analizi	Gabriel biplot, PCA biplot, JK biplot, Çift grafik
Saistītās	2	2
Kopsavilkums	Multiple Correspondence Analysis (MCA) is a multivariate ordination technique designed to explore and visualize associations among three or more categorical variables simultaneously. By mapping both observations and variable categories onto a shared low-dimensional space, MCA reveals hidden structure in nominal or ordinal survey data. The method was comprehensively systematized and extended by Michael Greenacre and Jorg Blasius in their 2006 edited volume, building on earlier geometric data analysis traditions developed in France by Jean-Paul Benzecri during the 1960s and 1970s.	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.