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	Multiple Linear Regression	一元配置分散分析
分野	統計学	統計学
系統	Regression model	Hypothesis test
提唱年	1886	1925
提唱者	Francis Galton; formalized by Karl Pearson	Ronald A. Fisher
種類	Parametric linear model	Parametric mean comparison
原典	Galton, F. (1886). Regression towards mediocrity in hereditary stature. Journal of the Anthropological Institute of Great Britain and Ireland, 15, 246–263.	Fisher, R. A. (1925). Statistical Methods for Research Workers. Edinburgh: Oliver and Boyd.
別名	MLR, OLS regression, multiple regression, linear regression with multiple predictors	one-factor ANOVA, single-factor ANOVA, analysis of variance, tek yönlü ANOVA
関連	8	4
概要	Multiple linear regression (MLR) is a parametric regression model that expresses a continuous outcome as a weighted linear combination of two or more predictor variables plus a random error term. The unknown weights (regression coefficients) are estimated by ordinary least squares (OLS), which minimises the sum of squared residuals. The method traces to Francis Galton's 1886 work on hereditary stature and was placed on firm mathematical footing by Karl Pearson; Draper and Smith's 1966 textbook established it as the standard framework for applied regression.	One-way ANOVA is a parametric hypothesis test that compares the means of three or more independent groups on a single continuous outcome to decide whether at least one group mean differs. It rests on the variance-partitioning framework introduced by Ronald A. Fisher in 1925.