手法を比較

	独立標本t検定	多変量分散分析 (MANOVA)	多変量重回帰分析
分野	統計学	統計学	統計学
系統	Hypothesis test	Hypothesis test	Regression model
提唱年	1908	1932	2007
提唱者	Student (W. S. Gosset)	Samuel Stanley Wilks (Wilks' Lambda, 1932); Roy, Hotelling, Pillai (mid-20th c.)	Johnson & Wichern (textbook treatment); classical multivariate least squares
種類	Parametric mean comparison	Parametric multivariate mean comparison	Multivariate linear regression
原典	Student (1908). The probable error of a mean. Biometrika, 6(1), 1–25.	Tabachnick, B.G. & Fidell, L.S. (2013). Using Multivariate Statistics (6th ed.). Pearson. ISBN: 978-0205849574	Johnson, R. A. & Wichern, D. W. (2007). Applied Multivariate Statistical Analysis (6th ed.). Pearson. ISBN: 978-0131877153
別名	student t-test, two-sample t-test, unpaired t-test, bağımsız örneklem t-testi	Multivariate ANOVA, Çok Değişkenli ANOVA (MANOVA)	multivariate multiple regression, MLR with multiple dependent variables, multiple-outcome regression, Çok Değişkenli Regresyon (MLR — Çoklu DV)
関連	4	5	5
概要	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.	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.