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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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2004
Abstract : There is a problem faced by experimenters in many technical fields, where, in general, the response variable of interest is y, and there is a set of predictor variables x1, x2,...xk. For example, in Dynamic Network Analysis (DNA) Response Surface Methodology (RSM) might be useful for sensitivity analysis of various DNA measures for different
Jeff Reminga +2 more
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Abstract : There is a problem faced by experimenters in many technical fields, where, in general, the response variable of interest is y, and there is a set of predictor variables x1, x2,...xk. For example, in Dynamic Network Analysis (DNA) Response Surface Methodology (RSM) might be useful for sensitivity analysis of various DNA measures for different
Jeff Reminga +2 more
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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: 1966-1988
Technometrics, 1989Response sarfxe methodology (RSM) is a collection of tools developed in the 1950s for the purpose of determining optimum operating conditions in applications in the chemical industry. This article reviews the progrrss of RSM in the general areas of experimental design and analysis and indicates how its role has been affected by advanccs in other fields
Raymond H. Myers +3 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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Optimization of mead production using Response Surface Methodology
Food and Chemical Toxicology, 2013The main aim of the present work was to optimize mead production using Response Surface Methodology. The effects of temperature (x₁: 20-30°C) and nutrients concentration (x₂: 60-120g /hL) on mead quality, concerning the final concentrations of glucose (Y₁), fructose (Y₂), ethanol (Y₃), glycerol (Y₄) and acetic acid (Y₅), were studied.
Gomes, Teresa +6 more
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Damage Identification Using Response Surface Methodology
Key Engineering Materials, 2009As a combination of statistical and mathematical techniques, response surface methodology gives explicit functions to express the relationship between the inputs and outputs of a physical system. This methodology has been widely applied to design optimization, response prediction and model validation but so far little literature related to its ...
Sheng En Fang, Ricardo Perera
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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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