Compara mètodes

	Regressió per Mínims Quadrats Ordinàris (MQO)	Regressió Lasso	Regressió Logística	Regressió quantílica
Camp	Econometria	Aprenentatge automàtic	Estadística per a la recerca	Econometria
Família	Regression model	Machine learning	Process / pipeline	Regression model
Any d'origen	2019	1996	1958	1978
Autor original	Wooldridge (textbook treatment); classical least squares	Tibshirani, R.	David Roxbee Cox	Koenker & Bassett
Tipus	Linear regression	Regularized linear regression (L1 penalty)	Method	Conditional quantile regression
Font seminal	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
Àlies	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization	logit model, binomial logistic regression, LR	conditional quantile regression, regression quantiles, Kantil Regresyon
Relacionats	5	4	3	5
Resum	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).	Lasso regression, introduced by Robert Tibshirani in 1996, is a linear regression method that adds an L1 penalty to the loss so that it shrinks coefficients and performs variable selection at the same time, producing a sparse model. By driving some coefficients exactly to zero it keeps only the predictors that matter.	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.	Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails.