Bandingkan kaedah

	Regresi Beta	Regresi Gamma (GLM)	Regresi Kuasa Dua Terkecil Biasa (OLS)
Bidang	Statistik	Statistik	Ekonometrik
Keluarga	Regression model	Regression model	Regression model
Tahun asal	2004	1989	2019
Pengasas	Ferrari & Cribari-Neto	McCullagh & Nelder (GLM framework)	Wooldridge (textbook treatment); classical least squares
Jenis	Generalized linear model (beta distribution)	Generalized linear model	Linear regression
Sumber perintis	Ferrari, S. L. P. & Cribari-Neto, F. (2004). Beta Regression for Modelling Rates and Proportions. Journal of Applied Statistics, 31(7), 799–815.	McCullagh, P. & Nelder, J. A. (1989). Generalized Linear Models (2nd ed.). Chapman and Hall.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Alias	beta regression model, proportion regression, Beta Regresyonu	gamma GLM, gamma generalized linear model, Gamma Regresyonu (GLM)	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Berkaitan	4	4	5
Ringkasan	Beta regression is a generalized linear model introduced by Ferrari and Cribari-Neto (2004) for outcomes that are rates or proportions confined to the open interval (0,1). It models the mean of a beta-distributed response through a link function, making it the natural choice for fractions, probability scores, and proportion indices.	Gamma regression is a generalized linear model that uses the gamma distribution to model a positive, right-skewed continuous outcome. Developed within the GLM framework of McCullagh and Nelder (1989), it is an alternative to ordinary linear regression for variables such as health-care costs, durations, and income.	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).