Σύγκριση μεθόδων

	Ανάλυση Συνθετικών Δεδομένων (CoDA)	Πολλαπλή Γραμμική Παλινδρόμηση
Πεδίο	Στατιστική	Στατιστική
Οικογένεια	Regression model	Regression model
Έτος προέλευσης	1982	1886
Δημιουργός	John Aitchison	Francis Galton; formalized by Karl Pearson
Τύπος	Constrained multivariate statistical method	Parametric linear model
Θεμελιώδης πηγή	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.
Εναλλακτικές ονομασίες	CoDA, Simplex Analysis, Log-Ratio Analysis, Bileşim Veri Analizi	MLR, OLS regression, multiple regression, linear regression with multiple predictors
Συναφείς	2	8
Σύνοψη	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.