Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Urejeshaji wa Njia ya Viwango Vidogo vya Kawaida (OLS)× | Kielelezo cha Kupanda kwa Tofauti (VIF)× | |
|---|---|---|
| Nyanja | Ekonometriki | Ekonometriki |
| Familia | Regression model | Regression model |
| Mwaka wa asili≠ | 2019 | 1970 |
| Mwanzilishi≠ | Wooldridge (textbook treatment); classical least squares | Donald Marquardt |
| Aina≠ | Linear regression | Diagnostic statistic |
| Chanzo asilia≠ | 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 ↗ |
| Majina mbadala | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu | VIF, Variance Inflation Index, Multicollinearity Inflation Factor, Varyans Enflasyon Faktörü |
| Zinazohusiana≠ | 5 | 3 |
| Muhtasari≠ | 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. |
| ScholarGateSeti ya data ↗ |
|
|