השוואת שיטות

	ביפלוט: תצוגה סימולטנית של שורות ועמודות בנתונים רב-משתניים	ניתוח התאמה
תחום	סטטיסטיקה	סטטיסטיקה
משפחה	Latent structure	Latent structure
שנת המקור	1971	1984
הוגה השיטה	Ruben Gabriel	Jean-Paul Benzécri; Michael Greenacre
סוג	Multivariate graphical display	Exploratory multivariate technique for categorical data
מקור מכונן	Gabriel, K. R. (1971). The biplot graphic display of matrices with application to principal component analysis. Biometrika, 58(3), 453–467.	Greenacre, M. J. (1984). Theory and Applications of Correspondence Analysis. Academic Press. ISBN: 978-0-12-299050-2
כינויים	Gabriel biplot, PCA biplot, JK biplot, Çift grafik	CA, Simple Correspondence Analysis, Reciprocal Averaging, Karşılıklı Uyum Analizi
קשורות	2	2
תקציר	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.	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.