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| 多項式回帰× | 最小二乗法 (OLS) 回帰× | |
|---|---|---|
| 分野≠ | 統計学 | 計量経済学 |
| 系統 | Regression model | Regression model |
| 提唱年≠ | 2012 | 2019 |
| 提唱者≠ | Montgomery, Peck & Vining (textbook treatment); classical least squares | Wooldridge (textbook treatment); classical least squares |
| 種類≠ | Linear regression in transformed predictors | Linear regression |
| 原典≠ | Montgomery, D. C., Peck, E. A. & Vining, G. G. (2012). Introduction to Linear Regression Analysis. Wiley. ISBN: 978-0470542811 | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| 別名≠ | polynomial least squares, curvilinear regression, Polinom Regresyonu | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| 関連≠ | 4 | 5 |
| 概要≠ | 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. | 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). |
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