The proposed regression models, which involve the transformations of some predictor variables, provide predictive capability comparable to that produced by the Random Forest models. Interactions between dependent variables are not needed. In spite of the generally good agreement of model fit statistics between analysis and validation data sets, the linear models fit to the analysis and validation data sets are not identical, i.e. some variables that are highly significant in the analysis data set do not remain so in the validation data set. These discrepancies, which may suggest aspects of the modeling that will not be generalized to future studies or may be artifacts of the smaller sample size of the validation data set, will be discussed below.