Sammenlign metoder

	Tobit-modellen for sensurerte regresjonsdata	Logistisk regresjon	Minste kvadraters metode (OLS)
Fagfelt	Økonometri	Forskningsstatistikk	Økonometri
Familie	Regression model	Process / pipeline	Regression model
Opprinnelsesår	1958	1958	2019
Opphavsperson	James Tobin	David Roxbee Cox	Wooldridge (textbook treatment); classical least squares
Type	Censored regression (limited dependent variable)	Method	Linear regression
Opprinnelig kilde	Tobin, J. (1958). Estimation of Relationships for Limited Dependent Variables. Econometrica, 26(1), 24-36.	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
Alias	censored regression, limited dependent variable model, Tobit Modeli (Sansürlü Regresyon)	logit model, binomial logistic regression, LR	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Relaterte	4	3	5
Sammendrag	The Tobit model is a regression for outcomes that are censored at a threshold, estimating the relationship by maximum likelihood. Introduced by James Tobin in 1958, it addresses the pile-up of observations at a limit (typically zero) in data such as spending, wages, or duration.	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).