Multivariate Methoden
15 Methoden in dieser Familie.
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Biplot: Gleichzeitige Darstellung von Zeilen und Spalten in multivariaten DatenA biplot is a low-dimensional graphical representation of a multivariate data matrix that simultaneously displays both the observations (rows) and the variables (columns) as pointsKanonsiche KorrelationsanalyseCanonical Correlation Analysis (CCA) is a multivariate statistical method that identifies pairs of linear combinations — one from each of two variable sets — such that the correlatKorrespondenzanalyseCorrespondence Analysis (CA) is an exploratory multivariate technique for visualizing the association structure of a two-way contingency table. Developed systematically by Jean-PauDiskriminanzanalyseDiscriminant analysis finds linear combinations of predictor variables that best separate two or more known groups. It is used both to understand which predictors distinguish the gLineare Diskriminanzanalyse (LDALinear Discriminant Analysis (LDA) is a parametric supervised classification method that finds the linear combination of continuous predictors that best separates two or more predeMultivariate Analysis of Covariance (MANCOVA)MANCOVA (Multivariate Analysis of Covariance) is a parametric hypothesis test that simultaneously compares two or more groups on multiple continuous dependent variables while stati
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This topic's most-referenced foundational methods, in the order they were developed — a place to start if you're new here.
Alle Methoden 15
Biplot: Gleichzeitige Darstellung von Zeilen und Spalten in multivariaten DatenKanonsiche KorrelationsanalyseKorrespondenzanalyseDiskriminanzanalyseLineare Diskriminanzanalyse (LDAMultivariate Analysis of Covariance (MANCOVA)Multivariate Varianzanalyse (MANOVA)Multidimensionale Skalierung (MDS)Multiple Korrespondenzanalyse (MCA)Robuste Kanonische Korrelationsanalyse (Robuste KKA)Robuste KorrespondenzanalyseRobuste DiskriminanzanalyseRobuste MANOVARobuste Multidimensionale Skalierung (Robuste MDS)Robust Multiple Correspondence Analysis (Robust MCA)