Salīdzināt metodes

	Daudzvarianto dispersijas analīze (MANOVA)	Lineārā diskriminantā analīze	Neatkarīgo paraugu t-tests
Nozare	Statistika	Statistika	Statistika
Saime	Hypothesis test	Latent structure	Hypothesis test
Izcelsmes gads	1932	1936	1908
Autors	Samuel Stanley Wilks (Wilks' Lambda, 1932); Roy, Hotelling, Pillai (mid-20th c.)	Ronald A. Fisher	Student (W. S. Gosset)
Tips	Parametric multivariate mean comparison	Supervised classification and dimension reduction	Parametric mean comparison
Pirmavots	Tabachnick, B.G. & Fidell, L.S. (2013). Using Multivariate Statistics (6th ed.). Pearson. ISBN: 978-0205849574	Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2), 179–188.	Student (1908). The probable error of a mean. Biometrika, 6(1), 1–25.
Citi nosaukumi	Multivariate ANOVA, Çok Değişkenli ANOVA (MANOVA)	LDA, Fisher discriminant analysis, discriminant function analysis, canonical discriminant analysis	student t-test, two-sample t-test, unpaired t-test, bağımsız örneklem t-testi
Saistītās	5	4	4
Kopsavilkums	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.	Discriminant 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 groups and to classify new observations into those groups with minimum error.	The independent samples t-test is a parametric hypothesis test that compares the means of two independent groups to decide whether they differ significantly. It builds on the t-distribution introduced by Student (W. S. Gosset) in 1908 and assumes the measured values are continuous, approximately normally distributed, and have equal variances.