مقایسهٔ روشها
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| شاخص شرط× | رگرسیون حداقل مربعات معمولی (OLS)× | |
|---|---|---|
| حوزه | اقتصادسنجی | اقتصادسنجی |
| خانواده | Regression model | Regression model |
| سال پیدایش≠ | 1980 | 2019 |
| پدیدآور≠ | Belsley, Kuh & Welsch | Wooldridge (textbook treatment); classical least squares |
| نوع≠ | Collinearity diagnostic index | Linear regression |
| منبع بنیادین≠ | 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 |
| نامهای دیگر | Belsley Condition Index, Collinearity Condition Index, Singular Value Condition Index, Koşul İndeksi | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| مرتبط≠ | 2 | 5 |
| خلاصه≠ | 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). |
| ScholarGateمجموعهداده ↗ |
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