## 4 Test Results  
### Principal Components Analysis  
The data dimensionality was reduced with the PCA method, which sought to find linear transformational functions that would bind the highest possible number of highly-correlated variables. The outlier results were eliminated first. Outlier values were those that were outside the 1.5 interquartile range [34]. After the elimination step, a correlation table was prepared. Analysis of the correlation table data indicates that features such as V\({}_{\mathrm{m}}\), N\({}_{\mathrm{w}}\), \(\rho_{\mathrm{ssd}}\) and ITS are correlated at the significance level of \(R^{2}>0.5\). Parameters TSR and W\({}_{\mathrm{RW+M2}}\) are correlated significantly at \(R^{2}>0.58\). The highest collinearity occurs between the set of dynamic modulus E* results and the set of phase angle \(\delta\) results, regardless of frequency level or test temperature used. The coefficient of determination is several times higher than 0.9. Both E* and \(\delta\) are also strongly correlated with the recycled mixtures physical properties (V\({}_{\mathrm{m}}\), N\({}_{\mathrm{w}}\)) and with ITS. The strong correlation in the group of rheological parameters, E and \(\delta\), makes constructing any classical model using multiple regression impractical. The inverse of the correlation matrix in the model would also be difficult. As a result, the PCA method turned out to be the best  
Figure 2: Multidimensional analysis stages. solution because it allowed the transformation of the measured parameter values into a new data set in such a way that new variables would be maximally uncorrelated with each other. Further analyzes were performed on new variables (new principal components). With PCA the total number of original variables were reduced to 4 new variables. The Kaiser criterion [36] was adopted, according to which the number of factors in the analysis could have factor loadings greater than 1, and the proportion of the variance explained was more than 85%. The results of the analysis are in Table 2.  
Finally, the phenomenon of collinearity between variables was eliminated when by the lost in representation quality approximately of 11%. Note that variable 5 did not make any significant contribution to explaining the 2.2% variability in the dataset. Thereby, the number of variables was limited to 4 using the Kaiser criterion. At this stage in the analysis, each new variable was functionally related with the real variables introduced into the dataset. The correlation and eigenvector table (Table 3) shows the nature of the relationship between raw variables and the given new grouping variable. Using results of eigenvectors together with the mean and the standard deviation statistics in Table 3 every new result out of analysis can be transformed to new collection of uncorrelated variables (Z1-Z4). Note that all the results of the elastic stiffness modulus (E) and the phase angle (\(\delta\)) showed the highest correlation with the first variable (Z1). This factor, based on Table 2, explains almost 57% of the variation in the entire dataset. It should be noted that between moduli determined at different temperatures (in viscoelastic range) there were a relationship according to well know TTSP (time temperature superposition principle). The impact of the stiffness modulus and phase angle parameters on the observed data set were expressed by the parameter Z1 which defines the viscoelastic character of the recycled mixes. The presented analysis assumed the possibility of entering missing stiffness modulus data on the basis of the results coming from the leading curve. Factor Z1 is in fact responsible for the rheological characteristics in the linear viscoelasticity range of the recycled mixtures. It gather a whole impact of stiffness modulus and phase angle at all. Regardless of how many stiffness modulus measurements were made its influence on analysis will be the greatest.