When the statistics were computed for the fixed-effects models, 'Regression 1' approach turned out to be better than 'Regression 2' approach, and optimization was again clearly better than the other methods (Table 8).
For the second aim, calculations were based on a mixed-model regression approach, with condition (intervention versus control) as a fixed effect and preschool nested within condition as a random effect.
Nevertheless, further research is necessary to evaluate systematically the performance of different correction procedures (i.e. component models, regression approaches), since the discussion about that topic has not been settled.
One is that, while there has been some debate concerning the relative merits of model I vs model II regression approaches in allometric analyses [ 27], it is now generally accepted that phylogenetic correction produces more accurate estimates of allometric slopes [ 28, 29].
Because we are interested in predicting pandemic potential from ferret SAR and not in the regression coefficients themselves, model I regression (i.e. ordinary logistic regression) is the preferred statistical model as it minimizes squared residuals in the y direction, whereas model II regression approaches do not (Sokal and Rohlf, 1995, p. 545, Table 14.3; Legendre and Legendre, 1998).
In fact, the present study illustrates that the improper handling of the Thomas model using traditional regression approach may lead to imprecise values of design parameters and erroneous conclusions of adsorption performance.
McGroddy et al. (2008) used a simple water-balance model and a regression approach to predict DON leaching loss from undisturbed forests with low atmospheric N inputs.
Moreover, to highlight robustness and precision of the addressed approaches, two different regression models have been developed and results obtained from the aforementioned intelligent models and regression approaches are compared with the corresponding refractive index data measured in laboratory.
Traditional methods to infer the copy number from a SNP array can be referred to segmentation, modeling and regression approaches.
For both models, a multiple regression approach was taken to predict Hm from age, Site Index, environmental data and RapidEye spectra and indices.
