Bandingkan kaedah

	Regresi Gamma (GLM)	Regresi Logistik	Regresi Kuasa Dua Terkecil Biasa (OLS)
Bidang	Statistik	Statistik Penyelidikan	Ekonometrik
Keluarga	Regression model	Process / pipeline	Regression model
Tahun asal	1989	1958	2019
Pengasas	McCullagh & Nelder (GLM framework)	David Roxbee Cox	Wooldridge (textbook treatment); classical least squares
Jenis	Generalized linear model	Method	Linear regression
Sumber perintis	McCullagh, P. & Nelder, J. A. (1989). Generalized Linear Models (2nd ed.). Chapman and Hall.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Alias	gamma GLM, gamma generalized linear model, Gamma Regresyonu (GLM)	logit model, binomial logistic regression, LR	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Berkaitan	4	3	5
Ringkasan	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.	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.	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).