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| Метод на най-малките квадрати (МНК)× | Фактор на инфлация на вариацията (VIF)× | |
|---|---|---|
| Област | Иконометрия | Иконометрия |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 2019 | 1970 |
| Създател≠ | Wooldridge (textbook treatment); classical least squares | Donald Marquardt |
| Тип≠ | Linear regression | Diagnostic statistic |
| Основополагащ източник≠ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 | Marquardt, D. W. (1970). Generalized inverses, ridge regression, biased linear estimation, and nonlinear estimation. Technometrics, 12(3), 591–612. DOI ↗ |
| Други названия | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu | VIF, Variance Inflation Index, Multicollinearity Inflation Factor, Varyans Enflasyon Faktörü |
| Свързани≠ | 5 | 3 |
| Резюме≠ | 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). | The Variance Inflation Factor (VIF) is a scalar diagnostic statistic proposed by Donald Marquardt (1970) that quantifies how much the variance of an estimated regression coefficient increases due to linear dependence—multicollinearity—among the predictors in an ordinary least squares model. It is routinely applied in econometrics, social science, and biomedical research whenever analysts suspect that two or more independent variables move together closely enough to destabilize coefficient estimates. |
| ScholarGateНабор от данни ↗ |
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