Compară metode

	Analiza de regresie multiplă	Modelarea ecuațiilor structurale
Domeniu	Statistică pentru cercetare	Statistică pentru cercetare
Familie	Process / pipeline	Process / pipeline
Anul apariției	1801	1921
Autorul original	Carl Friedrich Gauss	Sewall Wright
Tip	Method	Method
Sursa seminală	Draper, N. R., & Smith, H. (1966). Applied Regression Analysis. John Wiley & Sons.	Jöreskog, K. G., & Sörbom, D. (1973). LISREL: A general computer program for estimating a linear structural equation system. Research Bulletin 73-5. University of Stockholm.
Denumiri alternative	MLR, multivariate regression, linear regression	SEM, path analysis, latent variable modeling, causal modeling
Înrudite	4	3
Rezumat	Multiple regression analysis is a statistical method for modeling the relationship between a continuous dependent variable and two or more independent variables (predictors). Originating from Gauss's early 19th-century work and formalized by Draper and Smith (1966), it estimates linear equations predicting outcomes from multiple predictors while accounting for confounding relationships, making it indispensable in epidemiology, economics, psychology, and clinical research.	Structural equation modeling (SEM) is a comprehensive statistical framework combining path analysis (Sewall Wright, 1921) and confirmatory factor analysis to test complex causal models linking observed and latent variables. Formalized by Jöreskog (1973) with LISREL software, SEM enables simultaneous estimation of measurement relationships (how variables measure latent constructs) and structural relationships (how constructs influence outcomes), making it powerful for theory testing in psychology, epidemiology, organizational research, and health sciences where complex mediation, moderation, and latent processes require integrated analysis.