## 3 Results
The sum of squares decomposition of the total asymmetry revealed that the symmetric component explained 88.93% of the total variance, while the asymmetric component explained 11.07%, 11.05% of which can be attributed to fluctuating asymmetry, and 0.02% to directional asymmetry. When looking at the PCA of the symmetric component of the face (Figure 6), this showed that the first two principal components, PC1 and PC2, account respectively for 26.32% and 13.87% of the total variance. From the plot, it is evident that right (in green in Figure 6) and left (in blue in Figure 6) septal deviated faces do not show any particular pattern in their symmetric shape, as their convex hulls almost entirely overlap. Looking at the shape variations (Figure 7), for negative (grey) vs. positive (red) values of the symmetric PC1, the grey warpings show an overall shorter zygomatic and maxilla along the supero-inferior axis. The zygomatic looks wider, and both the maxilla and zygomatic result more anteriorly projected when compared to red warpings. For negative values, the orbits appear bigger and more infero-laterally displaced. Along the second component PC2, we observe more subtle differences between negative and positive values, with grey warpings showing a slightly longer and more forward projected maxilla, nasal cavity, and nasal bridge. A Multivariate Procrustes ANOVA was performed to test for differences in the symmetrical morphology of the face due to septal deviation, age, and sex (Table 5). Results show that septal deviation and sex have no influence on the symmetric morphology of the face, but that changes in age do impact significantly, explaining 14% of the total variance. Subsequently, a Principal Component Analysis (PCA) of the asymmetric component of facial shape was produced (Figure 8) to explore the morphological patterns of facial asymmetry and its variation in the sample. In the PCA plot, the origin represented perfect symmetry. The length and direction of the vectors represented the magnitude and direction of the asymmetry; therefore, the further the specimens from the origin, the more asymmetrical their face. In addition, the specimens were colour coded according to the relative side of septal deviation to observe any patterns between the morphology of the asymmetric component of the face and the relative side of septal deviation, with the dimension of the circles proportional to the magnitude of each individual's septal deviation. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Spetal Deviation** & & & & & \\
**(Absolute Values)** & **Estimate** & **Standard Error** & **T-Value** & **\(p\)-Value** &
\begin{tabular}{c} **Type of Sum** \\ **of Squares** \\ \end{tabular} \\ \hline Intercept & 0.008 & 0.0009 & 9.157 & \textless{}0.001 *** & 1 \\ \hline
**Age** & \(-0.0002\) & 0.0002 & \(-1.27\) & 0.211 & 1 \\ \hline
**Deviation side** & 0.0055 & 0.0009 & \(-5.60\) & **\textless{}0.001 *** & 1 \\ \hline
**F-statistic 15.5; Degrees of freedom 43; Adjusted R squared 0.39; \(p\)-value \(<\) 0.001 ***;** Sample size: 46 specimens. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of the Multiple Linear regression between the dependent variable of septal deviation (absolute values) and the independent variables of age and deviation side. Asterisks indicate significance: * \(p\)-value \(\leq\) 0.05; **\(p\)-value \(\leq\) 0.01; *** \(p\)-value \(\leq\) 0.001. Figure 6: PCA of the symmetric component of the facial complex. Colours indicate whether an individual was characterised by left (blue) or right (green) nasal septal deviation. PC1 explains 26.32% of the total variance, while PC2 explains 13.87% of the total variance. Sample size: 46 specimens. Figure 7: Surface warpings of the maximum and minimum PC1 (**top**) and PC2 (**below**) scores along the symmetric PCA. The grey surface indicates minimum (negative) values while the red surface indicates maximum (positive) values along the PCA axes.