Vertaile menetelmiä

	Probit-regressiomalli	Logistinen regressio	OLS-regressio (Ordinary Least Squares)
Tieteenala	Ekonometria	Tutkimuksen tilastomenetelmät	Ekonometria
Menetelmäperhe	Regression model	Process / pipeline	Regression model
Syntyvuosi	2018	1958	2019
Kehittäjä	Greene (textbook treatment); classical discrete-choice modelling	David Roxbee Cox	Wooldridge (textbook treatment); classical least squares
Tyyppi	Binary discrete-choice model	Method	Linear regression
Alkuperäislähde	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
Rinnakkaisnimet	probit regression, normit model, Probit Modeli	logit model, binomial logistic regression, LR	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Liittyvät	5	3	5
Tiivistelmä	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).