So sánh phương pháp

	Hồi quy Bình phương Tối thiểu Thông thường (OLS)	Hồi quy Logistic	Hồi quy Quantile
Lĩnh vực	Kinh tế lượng	Thống kê nghiên cứu	Kinh tế lượng
Họ	Regression model	Process / pipeline	Regression model
Năm ra đời	2019	1958	1978
Người khởi xướng	Wooldridge (textbook treatment); classical least squares	David Roxbee Cox	Koenker & Bassett
Loại	Linear regression	Method	Conditional quantile regression
Công trình gốc	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.
Tên gọi khác	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
Liên quan	5	3	5
Tóm tắt	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.