方法对比
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| 独立样本t检验× | 协方差多变量分析 (MANCOVA)× | 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 | 1970 | 2007 |
| 提出者≠ | 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 |
| 类型≠ | Parametric mean comparison | Parametric multivariate mean comparison with covariate control | 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. (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 |
| 别名 | 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) |
| 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. | 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. |
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