Comparar métodos

	Teste T² de Hotelling	Teste t para amostras independentes	Regressão Múltipla Linear Multivariada
Área	Estatística	Estatística	Estatística
Família	Hypothesis test	Hypothesis test	Regression model
Ano de origem	1931	1908	2007
Autor original	Harold Hotelling	Student (W. S. Gosset)	Johnson & Wichern (textbook treatment); classical multivariate least squares
Tipo	Multivariate parametric mean comparison	Parametric mean comparison	Multivariate linear regression
Fonte seminal	Hotelling, H. (1931). The Generalization of Student's Ratio. Annals of Mathematical Statistics, 2(3), 360–378.	Student (1908). The probable error of a mean. Biometrika, 6(1), 1–25.	Johnson, R. A. & Wichern, D. W. (2007). Applied Multivariate Statistical Analysis (6th ed.). Pearson. ISBN: 978-0131877153
Outros nomes	Hotelling T² Testi — Çok Değişkenli t-Testi, multivariate t-test, Hotelling T-squared	student t-test, two-sample t-test, unpaired t-test, bağımsız örneklem t-testi	multivariate multiple regression, MLR with multiple dependent variables, multiple-outcome regression, Çok Değişkenli Regresyon (MLR — Çoklu DV)
Relacionados	6	4	5
Resumo	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.	The independent samples t-test is a parametric hypothesis test that compares the means of two independent groups to decide whether they differ significantly. It builds on the t-distribution introduced by Student (W. S. Gosset) in 1908 and assumes the measured values are continuous, approximately normally distributed, and have equal variances.	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.