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Przeglądaj wybrane metody obok siebie; wiersze, które się różnią, są wyróżnione.
| Regresja geograficznie ważona (GWR)× | Regresja metodą najmniejszych kwadratów (OLS)× | |
|---|---|---|
| Dziedzina≠ | Analiza przestrzenna | Ekonometria |
| Rodzina | Regression model | Regression model |
| Rok powstania≠ | 2002 | 2019 |
| Twórca≠ | Fotheringham, Brunsdon & Charlton | Wooldridge (textbook treatment); classical least squares |
| Typ≠ | Local spatial regression | Linear regression |
| Źródło pierwotne≠ | Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168 | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Inne nazwy | GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR) | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Pokrewne | 5 | 5 |
| Podsumowanie≠ | Geographically Weighted Regression is a local regression method, introduced by Fotheringham, Brunsdon and Charlton (2002), that allows the regression coefficients to vary across space. Instead of one global equation, it fits a separate set of coefficients at every location, capturing spatial heterogeneity in the relationships. | 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 ↗ |
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