Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Regressão Polinomial× | Metodologia de Superfície de Resposta (RSM)× | |
|---|---|---|
| Área≠ | Estatística | Delineamento experimental |
| Família≠ | Regression model | Hypothesis test |
| Ano de origem≠ | 2012 | 1951 |
| Autor original≠ | Montgomery, Peck & Vining (textbook treatment); classical least squares | George E. P. Box & K. B. Wilson |
| Tipo≠ | Linear regression in transformed predictors | Second-order polynomial response surface model |
| Fonte seminal≠ | Montgomery, D. C., Peck, E. A. & Vining, G. G. (2012). Introduction to Linear Regression Analysis. Wiley. ISBN: 978-0470542811 | 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 ↗ |
| Outros nomes≠ | polynomial least squares, curvilinear regression, Polinom Regresyonu | RSM, Central Composite Design, Box-Behnken Design, CCD |
| Relacionados≠ | 4 | 7 |
| Resumo≠ | 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. | 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. |
| ScholarGateConjunto de dados ↗ |
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