विधियों की तुलना करें

	लॉजिस्टिक रिग्रेशन	क्वांटाइल रिग्रेशन
क्षेत्र	अनुसंधान सांख्यिकी	अर्थमिति
परिवार	Process / pipeline	Regression model
उद्भव वर्ष	1958	1978
प्रवर्तक	David Roxbee Cox	Koenker & Bassett
प्रकार	Method	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.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
उपनाम	logit model, binomial logistic regression, LR	conditional quantile regression, regression quantiles, Kantil Regresyon
संबंधित	3	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.	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.