Σύγκριση μεθόδων

	Λογιστική Παλινδρόμηση	Ανάλυση Πολλαπλής Παλινδρόμησης
Πεδίο	Ερευνητική Στατιστική	Ερευνητική Στατιστική
Οικογένεια	Process / pipeline	Process / pipeline
Έτος προέλευσης	1958	1801
Δημιουργός	David Roxbee Cox	Carl Friedrich Gauss
Τύπος	Method	Method
Θεμελιώδης πηγή	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Draper, N. R., & Smith, H. (1966). Applied Regression Analysis. John Wiley & Sons.
Εναλλακτικές ονομασίες	logit model, binomial logistic regression, LR	MLR, multivariate regression, linear regression
Συναφείς	3	4
Σύνοψη	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.	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.