2016
DOI: 10.22237/jmasm/1462077780
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JMASM37: Simple Response Surface Methodology Using RSREG (SAS)

Abstract: Response surface methodology (RSM) can be used when the response variable, y, is influenced by several variables, x's. When treatments take the form of quantitative values, then the true relationship between response variables and independent variables might be known. Examples are given in SAS.

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Cited by 6 publications
(3 citation statements)
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“…Also, in order to evaluate the synergistic effects of both glucose, sodium acetate and the effects of their high concentration on Y. lipolytica growth and PHB content, response surface methodology RSM analysis was used [30]. RSM analysis was done using SAS RSREG procedure [31]. For the statistical analysis, linear, seconddegree polynomials, and quadratic models were used to describe the relationship between the input variables and the responses.…”
Section: Discussionmentioning
confidence: 99%
“…Also, in order to evaluate the synergistic effects of both glucose, sodium acetate and the effects of their high concentration on Y. lipolytica growth and PHB content, response surface methodology RSM analysis was used [30]. RSM analysis was done using SAS RSREG procedure [31]. For the statistical analysis, linear, seconddegree polynomials, and quadratic models were used to describe the relationship between the input variables and the responses.…”
Section: Discussionmentioning
confidence: 99%
“…Also, to evaluate the synergistic effects of both glucose and sodium acetate and the effects of their high concentrations on Y. lipolytica growth and PHB content, the response surface methodology (RSM) analysis was performed [29]. RSM analysis was conducted via the SAS RSREG procedure [30]. For statistical analysis, linear, second-degree polynomials and quadratic models were applied to describe the relationship between the input variables and the responses.…”
Section: Discussionmentioning
confidence: 99%
“…It involves the use of a sequence of designed experiments to obtain an optimal response through linear models and second-degree polynomials. It is useful also for modeling and analysis of problems in which a response of interest is influenced by some variables (Ba et al, 2007) (Khoshnamvand et al, 2018) (Kumari et al, 2019) (Oehlert et al, 2000) (Amir et al, 2016) (Ngo et al, 2012). In NPs field, RSM have been used for modeling the NPS production process as well as optimization of the parameter affecting the production processes (Beg et al, 2002) (Gadekar et al, 2019) (Nikaeen et al, 2020).…”
Section: Introductionmentioning
confidence: 99%