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The most important model improvement is to take into account the temperature dependence of the lubricant viscosity.
However, model improvement is still needed to match the CH3OH and CO time-histories at later times.
Model improvement is demonstrated not only by an increase in (R^{2}) (0.46 compared to 0.23) but also by increases in adjusted (R^{2}) ((bar{R}^{2})) which accounts for the number of independent variables in the regression (0.34 compared to 0.11).
Another avenue for model improvement is the integration of the underlying biological and pharmacological complexities following a systems pharmacology approach.
This model improvement is important for analyzing gene content changes in lineage leading to parasitic bacteria, in which massive gene losses are often observed (Moran 2003; Charlebois and Doolittle 2004; Moran et al. 2009).
In the case of categorical dependent variables, as in the present study, the model improvement is based on the homogeneity of the data within new nodes compared to those that were dichotomized.
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The model results show a qualitative agreement with experimental trends and a number of areas for model improvement are discussed.
If initial nutrient concentrations were considered as additional independent variables, a slight model improvement was achieved, when the concentration of manganese was included.
Again model improvement was validated by a stepwise integration of predictors, and by increasing the basis dimension of the 2-dimensional spatial trend function (Tables 3 and 4).
Significant model improvement was assessed using likelihood ratio tests (LRT) to compare nested models.
Model improvement was greatest for four floristic variables (overall change in tree-community composition, floristic vectors 1 and 2, and the rate of tree-species turnover).
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