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Hotelling's T² Test×Многовариантен анализ на дисперсията (MANOVA)×Многовариантен множествен линеен регресионен анализ×
ОбластСтатистикаСтатистикаСтатистика
СемействоHypothesis testHypothesis testRegression model
Година на възникване193119322007
СъздателHarold HotellingSamuel Stanley Wilks (Wilks' Lambda, 1932); Roy, Hotelling, Pillai (mid-20th c.)Johnson & Wichern (textbook treatment); classical multivariate least squares
ТипMultivariate parametric mean comparisonParametric multivariate mean comparisonMultivariate linear regression
Основополагащ източникHotelling, H. (1931). The Generalization of Student's Ratio. Annals of Mathematical Statistics, 2(3), 360–378. link ↗Tabachnick, B.G. & Fidell, L.S. (2013). Using Multivariate Statistics (6th ed.). Pearson. ISBN: 978-0205849574Johnson, R. A. & Wichern, D. W. (2007). Applied Multivariate Statistical Analysis (6th ed.). Pearson. ISBN: 978-0131877153
Други названияHotelling T² Testi — Çok Değişkenli t-Testi, multivariate t-test, Hotelling T-squaredMultivariate ANOVA, Çok Değişkenli ANOVA (MANOVA)multivariate multiple regression, MLR with multiple dependent variables, multiple-outcome regression, Çok Değişkenli Regresyon (MLR — Çoklu DV)
Свързани655
РезюмеHotelling'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.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.Multivariate regression is a linear regression method that predicts several continuous dependent variables at the same time from a shared set of predictors. As developed in standard treatments such as Johnson and Wichern's Applied Multivariate Statistical Analysis (2007), each response equation can be fitted by ordinary least squares while the covariance structure of the residuals is used for joint testing across outcomes.
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ScholarGateСравнение на методи: Hotelling's T² Test · MANOVA · Multivariate Regression. Извлечено на 2026-06-20 от https://scholargate.app/bg/compare