手法を比較

	ベータ回帰	ガンマ回帰 (GLM)	ロジスティック回帰	最小二乗法 (OLS) 回帰
分野	統計学	統計学	研究統計	計量経済学
系統	Regression model	Regression model	Process / pipeline	Regression model
提唱年	2004	1989	1958	2019
提唱者	Ferrari & Cribari-Neto	McCullagh & Nelder (GLM framework)	David Roxbee Cox	Wooldridge (textbook treatment); classical least squares
種類	Generalized linear model (beta distribution)	Generalized linear model	Method	Linear regression
原典	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.	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
別名	beta regression model, proportion regression, Beta Regresyonu	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
関連	4	4	3	5
概要	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.	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).