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| 共分散構造を持つ多変量分散分析(MANCOVA)× | 判別分析× | 多変量分散分析 (MANOVA)× | |
|---|---|---|---|
| 分野 | 統計学 | 統計学 | 統計学 |
| 系統≠ | Hypothesis test | Latent structure | Hypothesis test |
| 提唱年≠ | 1970 | 1936 | 1932 |
| 提唱者≠ | Extension of MANOVA and ANCOVA traditions; consolidated in multivariate textbooks by the 1970s–1980s | Ronald A. Fisher | Samuel Stanley Wilks (Wilks' Lambda, 1932); Roy, Hotelling, Pillai (mid-20th c.) |
| 種類≠ | Parametric multivariate mean comparison with covariate control | Supervised classification and dimension reduction | Parametric multivariate mean comparison |
| 原典≠ | Tabachnick, B. G. & Fidell, L. S. (2019). Using Multivariate Statistics (7th ed.). Pearson. ISBN: 978-0134790541 | Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2), 179–188. DOI ↗ | Tabachnick, B.G. & Fidell, L.S. (2013). Using Multivariate Statistics (6th ed.). Pearson. ISBN: 978-0205849574 |
| 別名≠ | MANCOVA, multivariate ANCOVA, MANOVA with covariates, MANCOVA — Çok Değişkenli Kovaryans Analizi | LDA, Fisher discriminant analysis, discriminant function analysis, canonical discriminant analysis | Multivariate ANOVA, Çok Değişkenli ANOVA (MANOVA) |
| 関連≠ | 5 | 4 | 5 |
| 概要≠ | 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). | Discriminant analysis finds linear combinations of predictor variables that best separate two or more known groups. It is used both to understand which predictors distinguish the groups and to classify new observations into those groups with minimum error. | 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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