Comparar métodos

	Regressão por Mínimos Quadrados Ordinários (MQO)	Regressão Logística	Modelo de Efeitos Fixos para Dados em Painel	Regressão Quantílica
Área	Econometria	Estatística para pesquisa	Econometria	Econometria
Família	Regression model	Process / pipeline	Regression model	Regression model
Ano de origem	2019	1958	2014	1978
Autor original	Wooldridge (textbook treatment); classical least squares	David Roxbee Cox	Hsiao (textbook treatment); within transformation of panel data	Koenker & Bassett
Tipo	Linear regression	Method	Panel data regression	Conditional quantile regression
Fonte seminal	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	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.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
Outros nomes	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	logit model, binomial logistic regression, LR	fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli	conditional quantile regression, regression quantiles, Kantil Regresyon
Relacionados	5	3	5	5
Resumo	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).	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).	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.