Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Index podmíněnosti× | Regrese metodou ordinárních nejmenších čtverců (OLS)× | |
|---|---|---|
| Obor | Ekonometrie | Ekonometrie |
| Rodina | Regression model | Regression model |
| Rok vzniku≠ | 1980 | 2019 |
| Tvůrce≠ | Belsley, Kuh & Welsch | Wooldridge (textbook treatment); classical least squares |
| Typ≠ | Collinearity diagnostic index | Linear regression |
| Původní zdroj≠ | Belsley, D. A., Kuh, E., & Welsch, R. E. (1980). Regression Diagnostics: Identifying Influential Data and Sources of Collinearity. John Wiley & Sons. ISBN: 978-0-471-05856-4 | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Další názvy | Belsley Condition Index, Collinearity Condition Index, Singular Value Condition Index, Koşul İndeksi | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Příbuzné≠ | 2 | 5 |
| Shrnutí≠ | The Condition Index, introduced by Belsley, Kuh, and Welsch (1980), is a scalar measure derived from singular value decomposition of the scaled regressor matrix. It quantifies the degree of near-linear dependence among predictors in ordinary least squares regression, enabling analysts to detect collinearity that inflates coefficient variance and destabilises parameter estimates. Widely used in economics, social sciences, and biomedical research wherever OLS regression is applied. | 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). |
| ScholarGateDatová sada ↗ |
|
|