Linganisha mbinu

	Regression ya Kiasi (Quantile Regression)	Urejeshaji wa Njia ya Viwango Vidogo vya Kawaida (OLS)	Uchanganuzi wa Poisson na Negative Binomial
Nyanja	Ekonometriki	Ekonometriki	Ekonometriki
Familia	Regression model	Regression model	Regression model
Mwaka wa asili	1978	2019	1998
Mwanzilishi	Koenker & Bassett	Wooldridge (textbook treatment); classical least squares	Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)
Aina	Conditional quantile regression	Linear regression	Generalized linear model for count data
Chanzo asilia	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.	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	conditional quantile regression, regression quantiles, Kantil Regresyon	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	5	5	4
Muhtasari	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.	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.