方法对比
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| 独立样本t检验× | 多元方差分析 (MANOVA)× | 多元多重线性回归× | |
|---|---|---|---|
| 领域 | 统计学 | 统计学 | 统计学 |
| 方法族≠ | Hypothesis test | Hypothesis test | Regression model |
| 起源年份≠ | 1908 | 1932 | 2007 |
| 提出者≠ | Student (W. S. Gosset) | Samuel Stanley Wilks (Wilks' Lambda, 1932); Roy, Hotelling, Pillai (mid-20th c.) | Johnson & Wichern (textbook treatment); classical multivariate least squares |
| 类型≠ | Parametric mean comparison | Parametric multivariate mean comparison | Multivariate linear regression |
| 开创性文献≠ | Student (1908). The probable error of a mean. Biometrika, 6(1), 1–25. DOI ↗ | 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 |
| 别名≠ | 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) |
| 相关≠ | 4 | 5 | 5 |
| 摘要≠ | 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. |
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