قارن الطرق

	تحليل التباين متعدد المتغيرات (MANOVA)	تحليل التغاير (ANCOVA)	تحليل التمييز
المجال	الإحصاء	الإحصاء	الإحصاء
العائلة	Hypothesis test	Hypothesis test	Latent structure
سنة النشأة	1932	1932	1936
صاحب الطريقة	Samuel Stanley Wilks (Wilks' Lambda, 1932); Roy, Hotelling, Pillai (mid-20th c.)	Ronald A. Fisher	Ronald A. Fisher
النوع	Parametric multivariate mean comparison	Parametric group comparison with covariate control	Supervised classification and dimension reduction
المصدر التأسيسي	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.
الأسماء البديلة	Multivariate ANOVA, Çok Değişkenli ANOVA (MANOVA)	analysis of covariance, covariance analysis, ANCOVA (Kovaryans Analizi)	LDA, Fisher discriminant analysis, discriminant function analysis, canonical discriminant analysis
ذات صلة	5	4	4
الملخص	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.