Sammenlign metoder

	Logistisk regresjon	Negativ binomial regresjon	Kvantilregresjon
Fagfelt	Forskningsstatistikk	Økonometri	Økonometri
Familie	Process / pipeline	Regression model	Regression model
Opprinnelsesår	1958	2011	1978
Opphavsperson	David Roxbee Cox	Hilbe (textbook treatment); generalized linear model framework	Koenker & Bassett
Type	Method	Generalized linear model for count data	Conditional quantile regression
Opprinnelig kilde	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Hilbe, J. M. (2011). Negative Binomial Regression (2nd ed.). Cambridge University Press.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
Alias	logit model, binomial logistic regression, LR	NB regression, NB2 regression, negatif binom regresyonu	conditional quantile regression, regression quantiles, Kantil Regresyon
Relaterte	3	4	5
Sammendrag	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.	Negative Binomial Regression is a generalized linear model for count outcomes that extends Poisson regression to handle overdispersion, where the variance of the counts exceeds their mean. Developed in the GLM tradition and treated in depth by Hilbe (2011), it adds a dispersion parameter so that inference stays valid when Poisson would understate the spread of the data.	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.