Comparer des méthodes

	Indice de Condition	Régression par Moindres Carrés Ordinaires (MCO)	Régression Ridge
Domaine	Économétrie	Économétrie	Apprentissage automatique
Famille	Regression model	Regression model	Machine learning
Année d'origine	1980	2019	1970
Auteur d'origine	Belsley, Kuh & Welsch	Wooldridge (textbook treatment); classical least squares	Hoerl, A.E. & Kennard, R.W.
Type	Collinearity diagnostic index	Linear regression	L2-regularized linear regression
Source fondatrice	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	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.
Alias	Belsley Condition Index, Collinearity Condition Index, Singular Value Condition Index, Koşul İndeksi	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
Apparentées	2	5	4
Résumé	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).	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.