Compară metode

	Regresia prin metoda celor mai mici pătrate ordinare (OLS)	Regresia Lasso	Regresia Logistică	Modelul cu Efecte Fixe pentru Date Panou
Domeniu	Econometrie	Învățare automată	Statistică pentru cercetare	Econometrie
Familie	Regression model	Machine learning	Process / pipeline	Regression model
Anul apariției	2019	1996	1958	2014
Autorul original	Wooldridge (textbook treatment); classical least squares	Tibshirani, R.	David Roxbee Cox	Hsiao (textbook treatment); within transformation of panel data
Tip	Linear regression	Regularized linear regression (L1 penalty)	Method	Panel data regression
Sursa seminală	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.	Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press.
Denumiri alternative	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	fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli
Înrudite	5	4	3	5
Rezumat	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.	The Panel Data Fixed Effects model estimates relationships from panel data (the same units observed over several time periods) while controlling for unit- and/or time-specific effects, supporting causal inference. It is developed as the within estimator in standard treatments such as Hsiao's Analysis of Panel Data (2014).