เปรียบเทียบวิธี

	ค่าสัมประสิทธิ์เงินเฟ้อความแปรปรวน (Variance Inflation Factor - VIF)	การถดถอยกำลังสองน้อยที่สุดสามัญ (OLS)	Ridge Regression
สาขาวิชา	เศรษฐมิติ	เศรษฐมิติ	การเรียนรู้ของเครื่อง
ตระกูล	Regression model	Regression model	Machine learning
ปีกำเนิด	1970	2019	1970
ผู้ริเริ่ม	Donald Marquardt	Wooldridge (textbook treatment); classical least squares	Hoerl, A.E. & Kennard, R.W.
ประเภท	Diagnostic statistic	Linear regression	L2-regularized linear regression
แหล่งต้นตำรับ	Marquardt, D. W. (1970). Generalized inverses, ridge regression, biased linear estimation, and nonlinear estimation. Technometrics, 12(3), 591–612.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.
ชื่อเรียกอื่น	VIF, Variance Inflation Index, Multicollinearity Inflation Factor, Varyans Enflasyon Faktörü	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
ที่เกี่ยวข้อง	3	5	4
สรุป	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.	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).	Ridge Regression is an L2-regularized linear regression method, introduced by Arthur Hoerl and Robert Kennard in 1970, that reduces multicollinearity by adding a penalty on the size of the coefficients. It shrinks coefficients toward zero without setting any of them exactly to zero, producing more stable estimates when predictors are highly correlated.