Comparer des méthodes

	Analyse de la variance multivariée (MANOVA)	Analyse de Covariance (ANCOVA)	Analyse discriminante	Analyse de variance à un facteur
Domaine	Statistique	Statistique	Statistique	Statistique
Famille	Hypothesis test	Hypothesis test	Latent structure	Hypothesis test
Année d'origine	1932	1932	1936	1925
Auteur d'origine	Samuel Stanley Wilks (Wilks' Lambda, 1932); Roy, Hotelling, Pillai (mid-20th c.)	Ronald A. Fisher	Ronald A. Fisher	Ronald A. Fisher
Type	Parametric multivariate mean comparison	Parametric group comparison with covariate control	Supervised classification and dimension reduction	Parametric mean comparison
Source fondatrice	Tabachnick, B.G. & Fidell, L.S. (2013). Using Multivariate Statistics (6th ed.). Pearson. ISBN: 978-0205849574	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.	Fisher, R. A. (1925). Statistical Methods for Research Workers. Edinburgh: Oliver and Boyd.
Alias	Multivariate ANOVA, Çok Değişkenli ANOVA (MANOVA)	analysis of covariance, covariance analysis, ANCOVA (Kovaryans Analizi)	LDA, Fisher discriminant analysis, discriminant function analysis, canonical discriminant analysis	one-factor ANOVA, single-factor ANOVA, analysis of variance, tek yönlü ANOVA
Apparentées	5	4	4	4
Résumé	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.	ANCOVA is a parametric hypothesis test that compares the adjusted means of two or more independent groups while statistically controlling for one or more continuous covariates. By removing the portion of outcome variance explained by the covariate, ANCOVA increases statistical precision and produces fairer group comparisons. The method builds on the general linear model framework consolidated by Fisher in the early 1930s and is described comprehensively by Tabachnick and Fidell (2013).	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.	One-way ANOVA is a parametric hypothesis test that compares the means of three or more independent groups on a single continuous outcome to decide whether at least one group mean differs. It rests on the variance-partitioning framework introduced by Ronald A. Fisher in 1925.