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As for the individual tree growth reference database, several stand-level variables were compiled and boosted regression was used to screen variables important for predicting number of ingrowth trees per ha per year (ANINGR).
Taguchi's design of experiment (DoE) method was used to screen variables, as process variables are highly correlated this poses difficulty to fill in the design matrix of Taguchi's DoE method.
For each potential predictor, stepwise modeling was performed to screen variables for inclusion into the model.
Bivariate regression analyses were used to screen variables for multivariate regression models.
This shortcoming applies whether one is using bivariate analyses to screen variables or to select variables directly (category A).
In this paper we describe a novel use of the RICC as a useful tool to screen variables for clustering in multicenter studies.
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The reflections on the screen variable consisted of three conditions: reflections across the whole screen (scenario 1), no reflections on the screen (scenario 2), and reflections on half of the screen (scenario 3).
Therefore, methods for screening variables and system optimisation are discussed.
Then screened variables were optimized by Box Behnken experiment design.
The interactions between the screened variables were then evaluated using central composite design.
Central Composite Design was used for further optimization of these screened variables for enhanced lipase production.
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