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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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IIE Transactions, 1996
(1996). Response Surface Methodology. IIE Transactions: Vol. 28, Scheduling and Logistics, pp. 1031-1032.
Raymond H. Myers, Douglas C. Montgomery
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(1996). Response Surface Methodology. IIE Transactions: Vol. 28, Scheduling and Logistics, pp. 1031-1032.
Raymond H. Myers, Douglas C. Montgomery
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Response Surfaces: Designs and Analyses, Empirical Model-Building and Response Surfaces
Journal of Quality Technology, 1988(1988). Response Surfaces: Designs and Analyses, Empirical Model-Building and Response Surfaces. Journal of Quality Technology: Vol. 20, No. 3, pp. 214-216.
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Response Surface Designs for Experiments in Bioprocessing
Biometrics, 2005SummaryMany processes in the biological industries are studied using response surface methodology. The use of biological materials, however, means that run‐to‐run variation is typically much greater than that in many experiments in mechanical or chemical engineering and so the designs used require greater replication.
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2020
Response Surface Methods (RSMs) are statistical and numerical models that approximate the relationship between multiple input variables and an output variable. This chapter introduces the methodology and its importance for engineer- ing design optimisation. The basic steps to build RSMs and validate the model accuracy are explained.
Péter Zénó Korondi +2 more
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Response Surface Methods (RSMs) are statistical and numerical models that approximate the relationship between multiple input variables and an output variable. This chapter introduces the methodology and its importance for engineer- ing design optimisation. The basic steps to build RSMs and validate the model accuracy are explained.
Péter Zénó Korondi +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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Response Surfaces: Designs and Analyses
Technometrics, 1988Michael L. Deaton +2 more
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2012
Response surface modelling is introduced and its bond to design of experiments is discussed. Several RSM techniques are presented from a theoretical point of view throughout the chapter. Among the various techniques described the typical approximation by least squares method is found, as well as the different weighted-average interpolating methods such
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Response surface modelling is introduced and its bond to design of experiments is discussed. Several RSM techniques are presented from a theoretical point of view throughout the chapter. Among the various techniques described the typical approximation by least squares method is found, as well as the different weighted-average interpolating methods such
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2018
The term surface aberrations refers to features of the OF response to DVs, features of the topology, that make it difficult for optimization algorithms. Finding the optimum of an unconstrained quadratic surface is a trivial exercise. However, for many applications several types of OF features cause difficulty for the algorithm.
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The term surface aberrations refers to features of the OF response to DVs, features of the topology, that make it difficult for optimization algorithms. Finding the optimum of an unconstrained quadratic surface is a trivial exercise. However, for many applications several types of OF features cause difficulty for the algorithm.
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Response Surface Methods in Economics
Revue de l'Institut International de Statistique / Review of the International Statistical Institute, 1969Burdick, D. S., Naylor, T. H.
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