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| 독립 표본 t-검정× | 공분산 다변량 분석 (MANCOVA)× | 다변량 분산 분석 (MANOVA)× | |
|---|---|---|---|
| 분야 | 통계학 | 통계학 | 통계학 |
| 계열 | Hypothesis test | Hypothesis test | Hypothesis test |
| 기원 연도≠ | 1908 | 1970 | 1932 |
| 창시자≠ | Student (W. S. Gosset) | Extension of MANOVA and ANCOVA traditions; consolidated in multivariate textbooks by the 1970s–1980s | Samuel Stanley Wilks (Wilks' Lambda, 1932); Roy, Hotelling, Pillai (mid-20th c.) |
| 유형≠ | Parametric mean comparison | Parametric multivariate mean comparison with covariate control | Parametric multivariate mean comparison |
| 원전≠ | Student (1908). The probable error of a mean. Biometrika, 6(1), 1–25. DOI ↗ | Tabachnick, B. G. & Fidell, L. S. (2019). Using Multivariate Statistics (7th ed.). Pearson. ISBN: 978-0134790541 | Tabachnick, B.G. & Fidell, L.S. (2013). Using Multivariate Statistics (6th ed.). Pearson. ISBN: 978-0205849574 |
| 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 ANOVA, Çok Değişkenli ANOVA (MANOVA) |
| 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). | 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. |
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