Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Daudzvarianto dispersijas analīze (MANOVA)× | Daudzvariāciju daudzkārtējā lineārā regresija× | |
|---|---|---|
| Nozare | Statistika | Statistika |
| Saime≠ | Hypothesis test | Regression model |
| Izcelsmes gads≠ | 1932 | 2007 |
| Autors≠ | Samuel Stanley Wilks (Wilks' Lambda, 1932); Roy, Hotelling, Pillai (mid-20th c.) | Johnson & Wichern (textbook treatment); classical multivariate least squares |
| Tips≠ | Parametric multivariate mean comparison | Multivariate linear regression |
| Pirmavots≠ | 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 |
| Citi nosaukumi≠ | Multivariate ANOVA, Çok Değişkenli ANOVA (MANOVA) | multivariate multiple regression, MLR with multiple dependent variables, multiple-outcome regression, Çok Değişkenli Regresyon (MLR — Çoklu DV) |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | 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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