পদ্ধতির তুলনা করুন

	লজিস্টিক রিগ্রেশন	মাল্টিনোমিয়াল লজিস্টিক রিগ্রেশন	নেগেটিভ বাইনোমিয়াল রিগ্রেশন
ক্ষেত্র	গবেষণা পরিসংখ্যান	অর্থমিতি	অর্থমিতি
পরিবার	Process / pipeline	Regression model	Regression model
উদ্ভবের বছর	1958	1974	2011
প্রবর্তক	David Roxbee Cox	McFadden	Hilbe (textbook treatment); generalized linear model framework
ধরন	Method	Multinomial logistic regression	Generalized linear model for count data
মৌলিক উৎস	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	McFadden, D. (1974). Conditional Logit Analysis of Qualitative Choice Behavior. In P. Zarembka (Ed.), Frontiers in Econometrics (pp. 105-142). Academic Press. ISBN: 978-0127761503	Hilbe, J. M. (2011). Negative Binomial Regression (2nd ed.). Cambridge University Press.
অপর নাম	logit model, binomial logistic regression, LR	multinomial logistic regression, polytomous logistic regression, softmax regression, Çok Kategorili Lojistik Regresyon	NB regression, NB2 regression, negatif binom regresyonu
সম্পর্কিত	3	5	4
সারসংক্ষেপ	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.	Multinomial logistic regression is a maximum-likelihood method for a nominal (unordered) dependent variable with more than two categories. Building on McFadden's 1974 treatment of qualitative choice, it gives each category its own set of coefficients relative to a reference category.	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.