방법 비교

	다변량 분산 분석 (MANOVA)	판별 분석	일원 분산 분석
분야	통계학	통계학	통계학
계열	Hypothesis test	Latent structure	Hypothesis test
기원 연도	1932	1936	1925
창시자	Samuel Stanley Wilks (Wilks' Lambda, 1932); Roy, Hotelling, Pillai (mid-20th c.)	Ronald A. Fisher	Ronald A. Fisher
유형	Parametric multivariate mean comparison	Supervised classification and dimension reduction	Parametric mean comparison
원전	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.
별칭	Multivariate ANOVA, Çok Değişkenli ANOVA (MANOVA)	LDA, Fisher discriminant analysis, discriminant function analysis, canonical discriminant analysis	one-factor ANOVA, single-factor ANOVA, analysis of variance, tek yönlü ANOVA
관련	5	4	4
요약	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.	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.