方法对比
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| 响应面方法 (RSM)× | 全因子实验设计× | |
|---|---|---|
| 领域 | 实验设计 | 实验设计 |
| 方法族 | Hypothesis test | Hypothesis test |
| 起源年份≠ | 1951 | 1926 |
| 提出者≠ | George E. P. Box & K. B. Wilson | R. A. Fisher |
| 类型≠ | Second-order polynomial response surface model | Parametric factorial experiment |
| 开创性文献≠ | 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 ↗ | Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley. ISBN: 978-0471718130 |
| 别名≠ | RSM, Central Composite Design, Box-Behnken Design, CCD | factorial experiment, 2^k factorial, full factorial, Faktöriyel Deneme Deseni (Full Factorial, 2^k) |
| 相关≠ | 7 | 5 |
| 摘要≠ | 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. | A full factorial design is a parametric experimental method in which every combination of factor levels is tested simultaneously, enabling the estimation of all main effects and all interaction effects in a single study. Rooted in R. A. Fisher's foundational work on designed experiments (1926) and systematically developed by Box, Hunter, and Hunter (2005) and Montgomery (2017), the 2^k form tests k two-level factors across 2^k experimental runs and is the benchmark against which all other factorial designs are measured. |
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