Linganisha mbinu

	Usuli wa Regresi ya Binomiali Hasiri	Urejeshaji wa Njia ya Viwango Vidogo vya Kawaida (OLS)
Nyanja	Ekonometriki	Ekonometriki
Familia	Regression model	Regression model
Mwaka wa asili	2011	2019
Mwanzilishi	Hilbe (textbook treatment); generalized linear model framework	Wooldridge (textbook treatment); classical least squares
Aina	Generalized linear model for count data	Linear regression
Chanzo asilia	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
Majina mbadala	NB regression, NB2 regression, negatif binom regresyonu	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Zinazohusiana	4	5
Muhtasari	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).