Methoden vergleichen

	Kompositionelle Datenanalyse (CoDA)	Multiple Lineare Regression
Fachgebiet	Statistik	Statistik
Familie	Regression model	Regression model
Entstehungsjahr	1982	1886
Urheber	John Aitchison	Francis Galton; formalized by Karl Pearson
Typ	Constrained multivariate statistical method	Parametric linear model
Wegweisende Quelle	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.
Aliasnamen	CoDA, Simplex Analysis, Log-Ratio Analysis, Bileşim Veri Analizi	MLR, OLS regression, multiple regression, linear regression with multiple predictors
Verwandt	2	8
Zusammenfassung	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.