Hypothesis test
Response Surface Methodology (RSM)
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.
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Sources
- 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 ↗
- Myers, R. H., Montgomery, D. C. & Anderson-Cook, C. M. (2016). Response Surface Methodology: Process and Product Optimization Using Designed Experiments (4th ed.). Wiley. ISBN: 978-1118916032
Related methods
Referenced by
Adaptive ExperimentAdaptive Fractional Factorial ExperimentAdaptive Full Factorial ExperimentBayesian Box-Behnken DesignBayesian Design of ExperimentsBayesian Fractional Factorial DesignBayesian Taguchi methodBox-Behnken DesignCentral Composite DesignConjoint AnalysisDesign of experimentsDouble-blind fractional factorial experimentFactorial ExperimentFractional Factorial DesignFractional Factorial ExperimentFull Factorial DesignFull Factorial ExperimentHybrid Box-Behnken DesignHybrid Central Composite DesignHybrid design of experimentsHybrid Fractional Factorial DesignHybrid Full Factorial DesignHybrid Response Surface MethodologyHybrid Taguchi MethodIndustrial applications full factorial designIndustrial Applications Response Surface MethodologyMixture DesignMulti-response Design of ExperimentsMulti-response Fractional Factorial DesignMulti-response full factorial designMulti-response Response Surface MethodologyMulti-response Six Sigma DMAICMulti-response Taguchi methodOptimal Experimental DesignOptimization-assisted Box-Behnken designOptimization-assisted central composite designOptimization-assisted design of experimentsOptimization-assisted fractional factorial designOptimization-assisted full factorial designOptimization-assisted process capability analysisOptimization-assisted quality function deploymentOptimization-assisted Reliability AnalysisOptimization-assisted response surface methodologyOptimization-assisted Six Sigma DMAICOptimization-assisted Taguchi methodPilot Factorial ExperimentPilot Fractional Factorial ExperimentPilot full factorial experimentPlackett-Burman DesignPolynomial RegressionPragmatic Fractional Factorial ExperimentRisk-based Box-Behnken DesignRisk-based central composite designRisk-based design of experimentsRisk-based Response Surface MethodologyRisk-based Taguchi methodRobust Box-Behnken DesignRobust Central Composite DesignRobust Fractional Factorial DesignRobust Full Factorial DesignRobust Response Surface MethodologySensitivity Analysis with Box-Behnken DesignSensitivity analysis with central composite designSensitivity Analysis with Fractional Factorial DesignSensitivity Analysis with Process Capability AnalysisSensitivity analysis-integrated design of experimentsSensitivity analysis-integrated full factorial designSensitivity analysis-integrated response surface methodologySensitivity Analysis-integrated Taguchi MethodSimulation-assisted Box-Behnken designSimulation-assisted design of experimentsSimulation-assisted fractional factorial designSimulation-assisted quality function deploymentSimulation-assisted response surface methodologySimulation-assisted Taguchi methodSix Sigma DMAICSurrogate-Based OptimizationTaguchi Method