Módszerek összehasonlítása

	Regresszió Ordináris Legkisebb Négyzetes (OLS) módszerrel	A varianciainflációs faktor (VIF)
Tudományterület	Ökonometria	Ökonometria
Módszercsalád	Regression model	Regression model
Keletkezés éve	2019	1970
Megalkotó	Wooldridge (textbook treatment); classical least squares	Donald Marquardt
Típus	Linear regression	Diagnostic statistic
Alapmű	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.
Alternatív nevek	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	VIF, Variance Inflation Index, Multicollinearity Inflation Factor, Varyans Enflasyon Faktörü
Kapcsolódó	5	3
Összefoglaló	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.