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Przeglądaj wybrane metody obok siebie; wiersze, które się różnią, są wyróżnione.
| Regresja metodą najmniejszych kwadratów (OLS)× | Ważone Metody Najmniejszych Kwadratów (WLS)× | |
|---|---|---|
| Dziedzina≠ | Ekonometria | Statystyka |
| Rodzina | Regression model | Regression model |
| Rok powstania≠ | 2019 | 1935 |
| Twórca≠ | Wooldridge (textbook treatment); classical least squares | Alexander Craig Aitken |
| Typ≠ | Linear regression | Weighted linear estimator |
| Źródło pierwotne≠ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 | Aitken, A. C. (1935). IV.—On least squares and linear combination of observations. Proceedings of the Royal Society of Edinburgh, 55, 42–48. DOI ↗ |
| Inne nazwy | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu | WLS, weighted regression, heteroscedasticity-corrected OLS, variance-weighted least squares |
| Pokrewne≠ | 5 | 3 |
| Podsumowanie≠ | 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). | Weighted Least Squares is a generalization of Ordinary Least Squares (OLS) regression that assigns each observation a weight inversely proportional to its error variance, thereby down-weighting high-variance data points and up-weighting precise ones. Introduced in its general matrix form by Alexander Craig Aitken in 1935, WLS is the canonical remedy when heteroscedasticity is present and the error variance structure is known or can be reliably estimated. |
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