방법 비교

	독립 표본 t-검정	공분산 다변량 분석 (MANCOVA)	다변량 복수 선형 회귀분석 (Multivariate Multiple Linear Regression)
분야	통계학	통계학	통계학
계열	Hypothesis test	Hypothesis test	Regression model
기원 연도	1908	1970	2007
창시자	Student (W. S. Gosset)	Extension of MANOVA and ANCOVA traditions; consolidated in multivariate textbooks by the 1970s–1980s	Johnson & Wichern (textbook treatment); classical multivariate least squares
유형	Parametric mean comparison	Parametric multivariate mean comparison with covariate control	Multivariate linear regression
원전	Student (1908). The probable error of a mean. Biometrika, 6(1), 1–25.	Tabachnick, B. G. & Fidell, L. S. (2019). Using Multivariate Statistics (7th ed.). Pearson. ISBN: 978-0134790541	Johnson, R. A. & Wichern, D. W. (2007). Applied Multivariate Statistical Analysis (6th ed.). Pearson. ISBN: 978-0131877153
별칭	student t-test, two-sample t-test, unpaired t-test, bağımsız örneklem t-testi	MANCOVA, multivariate ANCOVA, MANOVA with covariates, MANCOVA — Çok Değişkenli Kovaryans Analizi	multivariate multiple regression, MLR with multiple dependent variables, multiple-outcome regression, Çok Değişkenli Regresyon (MLR — Çoklu DV)
관련	4	5	5
요약	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.	MANCOVA (Multivariate Analysis of Covariance) is a parametric hypothesis test that simultaneously compares two or more groups on multiple continuous dependent variables while statistically controlling for one or more covariates. It extends MANOVA by incorporating covariate adjustment, a tradition consolidated in multivariate statistical methodology by the 1970s and authoritatively documented by Tabachnick and Fidell (2019).	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.