विधियों की तुलना करें

	शून्य-स्फीत पॉइसन (ZIP) प्रतिगमन	लॉजिस्टिक रिग्रेशन
क्षेत्र	सांख्यिकी	अनुसंधान सांख्यिकी
परिवार	Regression model	Process / pipeline
उद्भव वर्ष	1992	1958
प्रवर्तक	Diane Lambert	David Roxbee Cox
प्रकार	Count regression (two-component mixture)	Method
मौलिक स्रोत	Lambert, D. (1992). Zero-Inflated Poisson Regression, with an Application to Defects in Manufacturing. Technometrics, 34(1), 1–14.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
उपनाम	ZIP regression, zero-inflated count model, Sıfır-Şişirilmiş Poisson Regresyonu (ZIP)	logit model, binomial logistic regression, LR
संबंधित	4	3
सारांश	Zero-Inflated Poisson regression is a two-component model for count data that contains more zeros than an ordinary Poisson model can explain. Introduced by Diane Lambert in 1992, it combines a logistic model for the zero-generating mechanism with a Poisson model for the genuine counting process.	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.