Regression model
Multiple Linear Regression
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.
StatMind ile uygulaSoonVideoSoon
Tam yöntemi oku
Members only
Sign inSign in with a free account to read this section.
Sources
- Galton, F. (1886). Regression towards mediocrity in hereditary stature. Journal of the Anthropological Institute of Great Britain and Ireland, 15, 246–263. DOI: 10.2307/2841583 ↗
- Pearson, K., & Lee, A. (1908). On the generalised probable error in multiple normal correlation. Biometrika, 6(1), 59–68. DOI: 10.1093/biomet/6.1.59 ↗
- Draper, N. R., & Smith, H. (1966). Applied Regression Analysis (1st ed.). John Wiley & Sons. ISBN: 9780471221708
- Montgomery, D. C., Peck, E. A., & Vining, G. G. (2012). Introduction to Linear Regression Analysis (5th ed.). John Wiley & Sons. ISBN: 9780470542811
Related methods
Referenced by
Benjamini-Hochberg ProcedureCanonical Correlation AnalysisChow TestCompositional Data AnalysisDurbin-Watson TestGeneralized Additive ModelOrdinary Least SquaresPartial Least SquaresPower Analysis for RegressionPrincipal Components RegressionRamsey RESET TestResponse Surface MethodologyRobust Multiple linear regressionSimple Linear RegressionStepwise Regression