Response Surface Methodology Using a Fullest Balanced Model: A Re-Analysis of a Dataset in theKorean Journal for Food Science of Animal Resources.
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 Parket al.(2014) published in theKorean Journal for Food Science of Animal Resources.
Department of Applied Statistics, Korea University, Sejong 30019, Korea.
Sungsue
S
Rheem
Department of Laboratory Medicine, Dankook University Hospital, Cheonan 31116, Korea.
Insoo
I
Rheem
Division of Animal Science, Chonnam National University, Gwangju 61186, Korea.
Sejong
S
Oh
fullest balanced model; lack of fit; optimization; response surface methodology; search on a grid; second-order model