In these preliminary analyses, we used general linear models of response profiles and modeling time with indicator variables for assessment rather than with a linear variable.
Some mathematical models of response surface were created by the mixed regression model and response surface method.
Some mathematical models of response surface are gained by the mixed regression method and response surface method.
Optimum values predicted from those models of response surfaces were in excellent agreement with the experimental results.
In addition, the performances of the neural network models are significantly superior compared with those of second-order swelling kinetics, quadratic and cubic models of response surface methodology.
To examine change in cognitive functioning over time we used general linear models of response profiles.
In this context, our distinction between 5 models of response selectivity (Fig. 1) could provide a framework to guide future studies.
Dynamical (attractor-based) models of response set reversals entail suppression of responses at one level of a neural hierarchy coupled to enhanced responses at complementary levels because of reciprocal information passing (Friston 1997).
Accurate identification of (genetic) markers of ovarian response to gonadotrophin stimulation would strengthen the development of predictive models of response and may enable the use of truly individualized ART stimulation treatment protocols (Fauser et al., 2008).
The integration of these data sets enhanced our understanding of the relationship between biomarker prevalence, exposure to contaminants and population-specific response, thereby yielding more informative predictive models of response and prospects for environmental remediation.
