Compara mètodes

	Regressió lineal simple	Regressió Logística
Camp	Estadística	Estadística per a la recerca
Família	Regression model	Process / pipeline
Any d'origen	1805	1958
Autor original	Adrien-Marie Legendre (least squares, 1805); Francis Galton (regression concept, 1886)	David Roxbee Cox
Tipus	Parametric bivariate regression	Method
Font seminal	Legendre, A. M. (1805). Nouvelles méthodes pour la détermination des orbites des comètes. Firmin Didot, Paris. [Appendix: Sur la méthode des moindres quarrés, pp. 72–80]	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
Àlies	SLR, ordinary least squares regression, OLS regression, bivariate regression	logit model, binomial logistic regression, LR
Relacionats	7	3
Resum	Simple linear regression is the foundational parametric method for modelling a straight-line relationship between one continuous predictor and one continuous outcome, estimating the slope and intercept by ordinary least squares (OLS). The least squares principle was first published by Adrien-Marie Legendre in 1805, and Francis Galton introduced the concept of regression to the mean in 1886, coining the term that names the entire family of methods.	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.