Porównaj metody
Przeglądaj wybrane metody obok siebie; wiersze, które się różnią, są wyróżnione.
| Test T² Hotellinga× | Regresja metodą najmniejszych kwadratów (OLS)× | |
|---|---|---|
| Dziedzina≠ | Statystyka | Ekonometria |
| Rodzina≠ | Hypothesis test | Regression model |
| Rok powstania≠ | 1931 | 2019 |
| Twórca≠ | Harold Hotelling | Wooldridge (textbook treatment); classical least squares |
| Typ≠ | Multivariate parametric mean comparison | Linear regression |
| Źródło pierwotne≠ | Hotelling, H. (1931). The Generalization of Student's Ratio. Annals of Mathematical Statistics, 2(3), 360–378. link ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Inne nazwy≠ | Hotelling T² Testi — Çok Değişkenli t-Testi, multivariate t-test, Hotelling T-squared | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Pokrewne≠ | 6 | 5 |
| Podsumowanie≠ | Hotelling's T² test is a multivariate parametric hypothesis test that simultaneously compares the mean vectors of two independent groups across multiple continuous outcome variables. It was introduced by Harold Hotelling in 1931 as the direct multivariate generalization of Student's t-test, replacing the scalar mean difference with a vector difference scaled by the pooled variance-covariance matrix. | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). |
| ScholarGateZbiór danych ↗ |
|
|