From the set of variables, some variables may accounted for large amount of variability in the data set.
If the set of variables changes, a new value function must be elicited.
Table 1 presents the set of variables and detailed descriptions which are used as potential covariates.
where xx denotes the set of variables in x with variable x omitted.
Consequently, the set of variables was reduced step by step for every type of road independently.
A possible relationship using the set of variables (x) can be noted as follows.
For each k, let I k ∗ denote the set of variables with outdegree k.
This means that for the Australian dataset, the set of variables {A2, A3, A4, A5, A6, A7, A10 and A13} is more significant than the set of variables {A1, A2, A3, A6, A8, A9, A11 and A14}.
According to Kim [37], PCA or LDA analyzes all of the variance of the set of variables (common variance and unique variance), whereas FA analyzes only common variance (correlation) of the set of variables.
where ND X denotes the set of variables that correspond to the nondescendent nodes of X and PA X the set of variables that correspond to its parent nodes.
Descriptive statistics mean, standard deviation, minimum and maximum are computed for the set of variables included in the models.
