방법 비교

	최소제곱법(OLS) 회귀	로지스틱 회귀	조건부 분위수 회귀
분야	계량경제학	연구 통계	계량경제학
계열	Regression model	Process / pipeline	Regression model
기원 연도	2019	1958	1978
창시자	Wooldridge (textbook treatment); classical least squares	David Roxbee Cox	Koenker & Bassett
유형	Linear regression	Method	Conditional quantile regression
원전	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.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
별칭	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	logit model, binomial logistic regression, LR	conditional quantile regression, regression quantiles, Kantil Regresyon
관련	5	3	5
요약	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.	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.