So sánh phương pháp

	Mô hình Hồi quy Probit	Hồi quy Logistic	Hồi quy Bình phương Tối thiểu Thông thường (OLS)
Lĩnh vực	Kinh tế lượng	Thống kê nghiên cứu	Kinh tế lượng
Họ	Regression model	Process / pipeline	Regression model
Năm ra đời	2018	1958	2019
Người khởi xướng	Greene (textbook treatment); classical discrete-choice modelling	David Roxbee Cox	Wooldridge (textbook treatment); classical least squares
Loại	Binary discrete-choice model	Method	Linear regression
Công trình gốc	Greene, W. H. (2018). Econometric Analysis (8th ed.). Pearson. ISBN: 978-0134461366	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Tên gọi khác	probit regression, normit model, Probit Modeli	logit model, binomial logistic regression, LR	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Liên quan	5	3	5
Tóm tắt	The probit model is a regression method for a binary (0/1) outcome that maps a linear index of the predictors through the standard normal cumulative distribution function to produce a probability. It is a classical discrete-choice alternative to logistic regression, developed in standard econometrics treatments such as Greene's Econometric Analysis (2018).	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.	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).