Сравнение методов

	Гамма-регрессия (Обобщенная линейная модель)	Логистическая регрессия	Регрессия методом обыкновенных наименьших квадратов (ОНМК)
Область	Статистика	Статистика исследований	Эконометрика
Семейство	Regression model	Process / pipeline	Regression model
Год появления	1989	1958	2019
Автор метода	McCullagh & Nelder (GLM framework)	David Roxbee Cox	Wooldridge (textbook treatment); classical least squares
Тип	Generalized linear model	Method	Linear regression
Основополагающий источник	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
Другие названия	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
Связанные	4	3	5
Сводка	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).