Bandingkan metode
Tinjau metode pilihan Anda berdampingan; baris yang berbeda akan disorot.
| Regresi Linier Berganda× | Regresi Polinomial× | |
|---|---|---|
| Bidang | Statistika | Statistika |
| Keluarga | Regression model | Regression model |
| Tahun asal≠ | 1886 | 2012 |
| Pencetus≠ | Francis Galton; formalized by Karl Pearson | Montgomery, Peck & Vining (textbook treatment); classical least squares |
| Tipe≠ | Parametric linear model | Linear regression in transformed predictors |
| Sumber perintis≠ | Galton, F. (1886). Regression towards mediocrity in hereditary stature. Journal of the Anthropological Institute of Great Britain and Ireland, 15, 246–263. DOI ↗ | Montgomery, D. C., Peck, E. A. & Vining, G. G. (2012). Introduction to Linear Regression Analysis. Wiley. ISBN: 978-0470542811 |
| Alias≠ | MLR, OLS regression, multiple regression, linear regression with multiple predictors | polynomial least squares, curvilinear regression, Polinom Regresyonu |
| Terkait≠ | 8 | 4 |
| Ringkasan≠ | 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. | Polynomial regression is a regression method that models non-linear relationships by including squared and higher-degree terms of an explanatory variable, and it is a core tool of response surface analysis. As developed in Montgomery, Peck and Vining's Introduction to Linear Regression Analysis (2012), it remains linear in its parameters even though the fitted curve bends. |
| ScholarGateSet data ↗ |
|
|