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	लॉजिस्टिक रिग्रेशन	ऋणात्मक द्विपद समाश्रयण (Negative Binomial Regression)	क्वांटाइल रिग्रेशन
क्षेत्र	अनुसंधान सांख्यिकी	अर्थमिति	अर्थमिति
परिवार	Process / pipeline	Regression model	Regression model
उद्भव वर्ष	1958	2011	1978
प्रवर्तक	David Roxbee Cox	Hilbe (textbook treatment); generalized linear model framework	Koenker & Bassett
प्रकार	Method	Generalized linear model for count data	Conditional quantile regression
मौलिक स्रोत	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.
उपनाम	logit model, binomial logistic regression, LR	NB regression, NB2 regression, negatif binom regresyonu	conditional quantile regression, regression quantiles, Kantil Regresyon
संबंधित	3	4	5
सारांश	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.