Comparer des méthodes

	Analyse des données compositionnelles (CoDA)	Régression linéaire multiple
Domaine	Statistique	Statistique
Famille	Regression model	Regression model
Année d'origine	1982	1886
Auteur d'origine	John Aitchison	Francis Galton; formalized by Karl Pearson
Type	Constrained multivariate statistical method	Parametric linear model
Source fondatrice	Aitchison, J. (1982). The statistical analysis of compositional data. Journal of the Royal Statistical Society: Series B, 44(2), 139–177.	Galton, F. (1886). Regression towards mediocrity in hereditary stature. Journal of the Anthropological Institute of Great Britain and Ireland, 15, 246–263.
Alias	CoDA, Simplex Analysis, Log-Ratio Analysis, Bileşim Veri Analizi	MLR, OLS regression, multiple regression, linear regression with multiple predictors
Apparentées	2	8
Résumé	Compositional Data Analysis (CoDA) is a branch of multivariate statistics designed for data that represent parts of a whole — proportions, percentages, or concentrations that sum to a constant. Introduced by John Aitchison in his landmark 1982 paper, CoDA recognises that standard Euclidean methods fail on the simplex and instead operates through log-ratio transformations that respect the relative nature of compositional information.	Multiple linear regression (MLR) is a parametric regression model that expresses a continuous outcome as a weighted linear combination of two or more predictor variables plus a random error term. The unknown weights (regression coefficients) are estimated by ordinary least squares (OLS), which minimises the sum of squared residuals. The method traces to Francis Galton's 1886 work on hereditary stature and was placed on firm mathematical footing by Karl Pearson; Draper and Smith's 1966 textbook established it as the standard framework for applied regression.