Comparer des méthodes

	Modèle Linéaire Hiérarchique (HLM)	Régression par Moindres Carrés Ordinaires (MCO)
Domaine	Statistique	Économétrie
Famille	Regression model	Regression model
Année d'origine	1992	2019
Auteur d'origine	Bryk & Raudenbush	Wooldridge (textbook treatment); classical least squares
Type	Multilevel linear regression	Linear regression
Source fondatrice	Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage Publications. ISBN: 978-0761919049	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Alias	HLM, multilevel linear model, nested data model, random coefficient model	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Apparentées	4	5
Résumé	The Hierarchical Linear Model (HLM) is a multilevel regression method designed for data in which lower-level units (e.g., students, patients) are nested within higher-level groups (e.g., schools, hospitals). It simultaneously models within-group relationships and between-group variation, producing unbiased estimates and correct standard errors that ordinary regression cannot provide for nested data.	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).