Sammenlign metoder

	Covariansanalyse (ANCOVA)	Diskriminantanalyse	Hotellings T²-test
Fagområde	Statistik	Statistik	Statistik
Familie	Hypothesis test	Latent structure	Hypothesis test
Oprindelsesår	1932	1936	1931
Ophavsperson	Ronald A. Fisher	Ronald A. Fisher	Harold Hotelling
Type	Parametric group comparison with covariate control	Supervised classification and dimension reduction	Multivariate parametric mean comparison
Oprindelig kilde	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.	Hotelling, H. (1931). The Generalization of Student's Ratio. Annals of Mathematical Statistics, 2(3), 360–378.
Aliasser	analysis of covariance, covariance analysis, ANCOVA (Kovaryans Analizi)	LDA, Fisher discriminant analysis, discriminant function analysis, canonical discriminant analysis	Hotelling T² Testi — Çok Değişkenli t-Testi, multivariate t-test, Hotelling T-squared
Relaterede	4	4	6
Resumé	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.	Hotelling's T² test is a multivariate parametric hypothesis test that simultaneously compares the mean vectors of two independent groups across multiple continuous outcome variables. It was introduced by Harold Hotelling in 1931 as the direct multivariate generalization of Student's t-test, replacing the scalar mean difference with a vector difference scaled by the pooled variance-covariance matrix.