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| Methode der kleinsten Quadrate (OLS)× | Response-Oberflächenmethode (ROM)× | |
|---|---|---|
| Fachgebiet≠ | Ökonometrie | Versuchsplanung |
| Familie≠ | Regression model | Hypothesis test |
| Entstehungsjahr≠ | 2019 | 1951 |
| Urheber≠ | Wooldridge (textbook treatment); classical least squares | George E. P. Box & K. B. Wilson |
| Typ≠ | Linear regression | Second-order polynomial response surface model |
| Wegweisende Quelle≠ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 | Box, G. E. P. & Wilson, K. B. (1951). On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society, Series B, 13(1), 1–45. link ↗ |
| Aliasnamen≠ | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu | RSM, Central Composite Design, Box-Behnken Design, CCD |
| Verwandt≠ | 5 | 7 |
| Zusammenfassung≠ | 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). | Response Surface Methodology is a collection of statistical and mathematical techniques for building an empirical second-order polynomial model that relates a continuous response variable to two or more controllable input factors, and then locating the factor settings that optimize that response. The approach was introduced by George E. P. Box and K. B. Wilson in their landmark 1951 paper and has since become a cornerstone of process optimization across engineering, chemistry, food science, and pharmaceutics. |
| ScholarGateDatensatz ↗ |
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