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WIREs Computational Statistics, 2010
AbstractThe purpose of this article is to provide a survey of the various stages in the development of response surface methodology (RSM). The coverage of these stages is organized in three parts that describe the evolution of RSM since its introduction in the early 1950s.
André I. Khuri, Siuli Mukhopadhyay
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AbstractThe purpose of this article is to provide a survey of the various stages in the development of response surface methodology (RSM). The coverage of these stages is organized in three parts that describe the evolution of RSM since its introduction in the early 1950s.
André I. Khuri, Siuli Mukhopadhyay
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2018
Response surface methodology or in short RSM is a collection of mathematical and statistical tools and techniques that are useful in developing, understanding, and optimizing processes and products. Using this methodology, the responses that are influenced by several variables can be modeled, analyzed, and optimized.
Dharmaraja Selvamuthu, Dipayan Das
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Response surface methodology or in short RSM is a collection of mathematical and statistical tools and techniques that are useful in developing, understanding, and optimizing processes and products. Using this methodology, the responses that are influenced by several variables can be modeled, analyzed, and optimized.
Dharmaraja Selvamuthu, Dipayan Das
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2017
Experiments for fitting a predictive model involving several continuous variables are known as response surface experiments. The objectives of response surface methodology include the determination of variable settings for which the mean response is optimized and the estimation of the response surface in the vicinity of this good location.
Angela Dean +2 more
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Experiments for fitting a predictive model involving several continuous variables are known as response surface experiments. The objectives of response surface methodology include the determination of variable settings for which the mean response is optimized and the estimation of the response surface in the vicinity of this good location.
Angela Dean +2 more
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Response Surface Methodology in Biotechnology
Quality Engineering, 2010ABSTRACT Many experiments in biotechnology exploit the principles and methods of response surface methodology (RSM). The Quality by Design initiative in pharmaceutical development will accelerate this trend. In this article we give a broad picture of the important role played by RSM in biotechnology experimentation.
David M. Steinberg, Dizza Bursztyn
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An experimental methodology for response surface optimization methods
Journal of Global Optimization, 2011zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Daniel J. Lizotte +2 more
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Introduction to Response-Surface Methodology
2017Until now, we have considered how a dependent variable, yield, or response depends on specific levels of independent variables or factors. The factors could be categorical or numerical; however, we did note that they often differ in how the sum of squares for the factor is more usefully partitioned into orthogonal components.
Paul D. Berger +2 more
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Stochastic programming methods in the response surface methodology
Computational Statistics & Data Analysis, 2005zbMATH Open Web Interface contents unavailable due to conflicting licenses.
José A. Díaz-García +2 more
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A class of composite designs for response surface methodology
Computational Statistics & Data Analysis, 2014zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Stelios D. Georgiou +2 more
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Optimization of convective drying by response surface methodology
Computers and Electronics in Agriculture, 2019Abstract In this work, response surface methodology (RSM) is applied to state an optimized system for convective drying of apple slices using desirability function. The interaction of the independent parameters including air temperature (T = 70–90 °C), air velocity (V = 4–5 m/s), and apple slice geometry (G = circle, square, and triangle) with the ...
H. Majdi +2 more
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Multiple Comparisons with a Control in Response Surface Methodology
Technometrics, 1993Quadratic response surface methodology often focuses on finding the levels of some (coded) predictor variables x = (x l, x 2, …, x k ) that optimize the expected value of a response variable y. Typically the experimenter starts from some best guess or “control” combination of the predictors (usually coded to x = 0) and performs an experiment varying ...
Ping Sa, Don Edwards
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