Сравнение методов

	Смешанная дисперсия (Mixed ANOVA)	Дисперсионный анализ с контролем ковариат (ANCOVA)	Многомерный дисперсионный анализ (MANOVA)	Однофакторный дисперсионный анализ
Область	Статистика	Статистика	Статистика	Статистика
Семейство	Hypothesis test	Hypothesis test	Hypothesis test	Hypothesis test
Год появления	1925	1932	1932	1925
Автор метода	R. A. Fisher (ANOVA framework); split-plot design formalised in agricultural experimentation	Ronald A. Fisher	Samuel Stanley Wilks (Wilks' Lambda, 1932); Roy, Hotelling, Pillai (mid-20th c.)	Ronald A. Fisher
Тип	Parametric factorial ANOVA	Parametric group comparison with covariate control	Parametric multivariate mean comparison	Parametric mean comparison
Основополагающий источник	Field, A. (2018). Discovering Statistics Using IBM SPSS Statistics (5th ed.). SAGE. ISBN: 978-1526419521	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. (1925). Statistical Methods for Research Workers. Edinburgh: Oliver and Boyd.
Другие названия	split-plot ANOVA, mixed-design ANOVA, between-within ANOVA, Karma ANOVA (Mixed ANOVA — Gruplar Arası × Tekrarlı)	analysis of covariance, covariance analysis, ANCOVA (Kovaryans Analizi)	Multivariate ANOVA, Çok Değişkenli ANOVA (MANOVA)	one-factor ANOVA, single-factor ANOVA, analysis of variance, tek yönlü ANOVA
Связанные	6	4	5	4
Сводка	Mixed ANOVA is a parametric factorial analysis of variance that simultaneously examines at least one between-subjects factor and at least one within-subjects (repeated-measures) factor. Rooted in R. A. Fisher's ANOVA framework formalised in 1925, it is the standard method for experimental and longitudinal designs in which different groups are each measured across multiple time points or conditions.	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).	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.	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.