Everything about Response surface methodology
Statistical approach
In statistics, response surface methodology (RSM) explores the relationships between several explanatory variables and one or more response variables. RSM is an empirical model which employs the use of mathematical and statistical techniques to relate input variables, otherwise known as factors, to the response. RSM became very useful because other methods available, such as the theoretical model, could be very cumbersome to use, time-consuming, inefficient, error-prone, and unreliable. The method was introduced by George E. P. Box and K. B. Wilson in 1951. The main idea of RSM is to use a sequence of designed experiments to obtain an optimal response. Box and Wilson suggest using a second-degree polynomial model to do this. They acknowledge that this model is only an approximation, but they use it because such a model is easy to estimate and apply, even when little is known about the process. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Response surface methodologyA 5-minute orientation
- Skim a recent paper: Response surface methodology (RSM) for optimizing engine performance and emissions fueled with biofuel: Review of RSM for sustainability energy transitionInfluential work since 2022
- Pick up a classic book: Sensitivity AnalysisThe most-reprinted book on the subject
- Read the landmark paper: Response surface methodology (RSM) as a tool for optimization in analytical chemistryCited 5,484 times
- Go sideways: Optimal experimental designA closely related topic
- Go sideways: Taguchi methodsA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🏆 Landmark papers (most cited)
Response surface methodology (RSM) as a tool for optimization in analytical chemistry
Response surface methodology
Rubbercrete mixture optimization using response surface methodology
Optimization of convective drying by response surface methodology
Response Surface Methodology: 1966-1988
Response surface methodology: A neural network approach
Response Surface Methodology: 1966–l988
Bonded-particle model calibration using response surface methodology
🆕 Influential since 2022
Response surface methodology (RSM) for optimizing engine performance and emissions fueled with biofuel: Review of RSM for sustainability energy transition
Response Surface Methodology Using Observational Data: A Systematic Literature Review
New approaches on composite designs for Response Surface Methodology
Response surface methodology for design of gas turbine combustor
Optimization of reduced graphene oxide yield using response surface methodology
📚 Books worth reading
Sensitivity AnalysisAndrea Saltelli, K. Chan · 2000
The pharmacology of functional, biochemical, and recombinant receptor systemsTerrence P. Kenakin · 2000
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Optimal experimental design In the design of experiments, optimal experimental designs (or optimum designs) are a class of experimental designs that are optimal with respect to some…
- Taguchi methods Taguchi methods (Japanese: タグチメソッド) are statistical methods, sometimes called robust design methods, developed by Genichi Taguchi to improve the quality…
- Polynomial and rational function modeling In statistical modeling (especially process modeling), polynomial functions and rational functions are sometimes used as an empirical technique for curve…
- Design of experiments The design of experiments (DOE), also known as experimental design, refers to the construction of procedures that attempt to explain how changes in one…
- Statistical theory The theory of statistics provides a basis for the whole range of techniques, in both study design and data analysis, that are used within applications…
- List of publications in statistics This is a list of publications in statistics, organized by field. Some reasons why a particular publication might be regarded as important: Topic creator…
- Multifactor design of experiments software Software that is used for designing factorial experiments plays an important role in scientific experiments and represents a route to the implementation…
- Engineering statistics Engineering statistics combines engineering and statistics using scientific methods for analyzing data. Engineering statistics involves data concerning…