Usporedite metode

	Linearna diskriminacijska analiza (LDA	Multivarijatna analiza varijance (MANOVA)
Područje	Statistika	Statistika
Obitelj	Hypothesis test	Hypothesis test
Godina nastanka	1936	1932
Tvorac	Ronald A. Fisher	Samuel Stanley Wilks (Wilks' Lambda, 1932); Roy, Hotelling, Pillai (mid-20th c.)
Vrsta	Parametric linear classifier / dimensionality reduction	Parametric multivariate mean comparison
Temeljni izvor	Fisher, R.A. (1936). The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics, 7(2), 179–188.	Tabachnick, B.G. & Fidell, L.S. (2013). Using Multivariate Statistics (6th ed.). Pearson. ISBN: 978-0205849574
Drugi nazivi	LDA, Fisher's LDA, Fisher's linear discriminant, discriminant function analysis	Multivariate ANOVA, Çok Değişkenli ANOVA (MANOVA)
Srodne	7	5
Sažetak	Linear Discriminant Analysis (LDA) is a parametric supervised classification method that finds the linear combination of continuous predictors that best separates two or more predefined groups. Introduced by Ronald A. Fisher in his landmark 1936 paper on taxonomic measurements, it simultaneously serves as a classifier and a dimensionality-reduction tool, and can be understood as the classification-oriented counterpart of MANOVA.	MANOVA is a parametric hypothesis test that simultaneously compares group means across multiple continuous dependent variables, controlling the inflation of Type I error that would result from running separate ANOVAs. Key multivariate test statistics — Wilks' Lambda, Pillai's Trace, Hotelling-Lawley Trace, and Roy's Greatest Root — were developed between the 1930s and 1950s, with Wilks' Lambda formalised by Samuel Stanley Wilks in 1932.