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Multiple linear regression was used to fit response surfaces to the data.
In cases where a predictive equation could be fit, response surface plots with time set to four days were generated.
The methodology starts by the design of an experiment suitable to fit response surfaces to some analytical responses of interest in the problem being studied.
We used linear mixed effects models to fit response trends over time and to determine treatment group effects (i.e. interaction of treatment X period).
This result indicates that the model used to fit response variable is significant (P < 0.0001) and adequate to represent the relationship between the responses and the independent variables.
It also confirms whether a scale meets the requirements of fundamental measurement, allowing for mathematical investigation for reasons of misfit through the assessment of the parametric properties such as individual person fit and item fit, response thresholds, differential item functioning (DIF), local dependency and person separation index (PSI) [ 21, 22].
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Because words in a pair were never close semantically, participants were asked to relax their response criterion to aim at an equal number of 'fit' and 'don't fit' responses.
We describe ridge approximation for fitting response surfaces in several variables.
Unless the model shows an adequate fit, proceeding with an investigation and optimization of the fitted response surface is likely to give poor or misleading results.
Such designs minimize the maximum mean squared error of the predicted values, with the maximum taken over a class of departures from the fitted response function.
An optimization technique combined with a semi-expanding space grid digital simulation was utilized for fitting response curves obtained with the cell to estimate the parameters.
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