Linganisha mbinu

	Regresheni ya Logistiki	Usuli wa Regresi ya Binomiali Hasiri	Urejeshaji wa Njia ya Viwango Vidogo vya Kawaida (OLS)	Uchanganuzi wa Poisson na Negative Binomial
Nyanja	Takwimu za Utafiti	Ekonometriki	Ekonometriki	Ekonometriki
Familia	Process / pipeline	Regression model	Regression model	Regression model
Mwaka wa asili	1958	2011	2019	1998
Mwanzilishi	David Roxbee Cox	Hilbe (textbook treatment); generalized linear model framework	Wooldridge (textbook treatment); classical least squares	Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)
Aina	Method	Generalized linear model for count data	Linear regression	Generalized linear model for count data
Chanzo asilia	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.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press.
Majina mbadala	logit model, binomial logistic regression, LR	NB regression, NB2 regression, negatif binom regresyonu	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon
Zinazohusiana	3	4	5	4
Muhtasari	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.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).	Poisson regression is a generalized linear model for count outcomes — events tallied as non-negative integers such as hospital admissions, accidents, or article counts. It models the log of the expected count as a linear function of the predictors, and is developed in the standard count-data treatment of Cameron and Trivedi (1998); when the counts are over-dispersed, the closely related negative binomial model (Hilbe, 2011) is preferred.