Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Neatkarīgo paraugu t-tests× | Daudzvariāciju daudzkārtējā lineārā regresija× | |
|---|---|---|
| Nozare | Statistika | Statistika |
| Saime≠ | Hypothesis test | Regression model |
| Izcelsmes gads≠ | 1908 | 2007 |
| Autors≠ | Student (W. S. Gosset) | Johnson & Wichern (textbook treatment); classical multivariate least squares |
| Tips≠ | Parametric mean comparison | Multivariate linear regression |
| Pirmavots≠ | Student (1908). The probable error of a mean. Biometrika, 6(1), 1–25. DOI ↗ | Johnson, R. A. & Wichern, D. W. (2007). Applied Multivariate Statistical Analysis (6th ed.). Pearson. ISBN: 978-0131877153 |
| Citi nosaukumi | student t-test, two-sample t-test, unpaired t-test, bağımsız örneklem t-testi | multivariate multiple regression, MLR with multiple dependent variables, multiple-outcome regression, Çok Değişkenli Regresyon (MLR — Çoklu DV) |
| Saistītās≠ | 4 | 5 |
| Kopsavilkums≠ | 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. | 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. |
| ScholarGateDatu kopa ↗ |
|
|