Сравнение методов

	Множественная линейная регрессия	Регрессия методом обыкновенных наименьших квадратов (ОНМК)
Область	Статистика	Эконометрика
Семейство	Regression model	Regression model
Год появления	1886	2019
Автор метода	Francis Galton; formalized by Karl Pearson	Wooldridge (textbook treatment); classical least squares
Тип	Parametric linear model	Linear regression
Основополагающий источник	Galton, F. (1886). Regression towards mediocrity in hereditary stature. Journal of the Anthropological Institute of Great Britain and Ireland, 15, 246–263.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Другие названия	MLR, OLS regression, multiple regression, linear regression with multiple predictors	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Связанные	8	5
Сводка	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.	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).