Response Surface Methodology Using a Fullest Balanced Model: A Re-Analysis of a Dataset in the Korean Journal for Food Science of Animal Resources

  • Rheem, Sungsue (Department of Applied Statistics, Korea University) ;
  • Rheem, Insoo (Department of Laboratory Medicine, Dankook University Hospital) ;
  • Oh, Sejong (Division of Animal Science, Chonnam National University)
  • Received : 2017.02.03
  • Accepted : 2017.02.08
  • Published : 2017.02.28


Response surface methodology (RSM) is a useful set of statistical techniques for modeling and optimizing responses in research studies of food science. In the analysis of response surface data, a second-order polynomial regression model is usually used. However, sometimes we encounter situations where the fit of the second-order model is poor. If the model fitted to the data has a poor fit including a lack of fit, the modeling and optimization results might not be accurate. In such a case, using a fullest balanced model, which has no lack of fit, can fix such problem, enhancing the accuracy of the response surface modeling and optimization. This article presents how to develop and use such a model for the better modeling and optimizing of the response through an illustrative re-analysis of a dataset in Park et al. (2014) published in the Korean Journal for Food Science of Animal Resources.


Supported by : Korea University


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