The previous sections focused on the effect of material viscosity and thermal degradation on ∆W , ∆D i , and RA and competing factors were discovered. On the other hand, thermal properties such as transition temperatures or material crystallinity, other rheological properties, and printing parameters could all have an impact on printing quality, and their relationships should be investigated. The Pearson correlation coefficent describes the linear correlation between two factors. Using the dataset described in section 2.6, the Pearson correlation coefficient matrix was generated. Figure displays the corresponding results as a heatmap, with each cell displaying the coefficient of correlation between two cell components. The scale of the heatmap ranges from 1 to -1, with 1 indicating direct association between two parameters, -1 indicating direct negative correlation, and 0 indicating no correlation. The correlation coefficient between targets and material properties found to be significantly low, indicating no positive or negative linear correlation between two factors. Moreover, the correlation coefficient between targets and printing parameters also found to be almost zero. On the other hand, correlation coefficient between ∆D i and ∆W found to be around 0.8 which indicates a strong positive correlation. This result is also consistent with the previous discussions where the ∆D i shown to have similar trend with ∆W . Between RA -∆W , and RA -∆D i no direct correlation found at all. This is also consistent with the previously discussed relation between RA and ∆W where both over and under-extrusion could increase the roughness average. It is noteworthy to men-tion that while the Pearson Correlation Coefficient found to be successful to reveal the correlation of targets with each other, it is not possible to deduce the effect of material properties or printing parameters on the targets. It is evident that these factors do not act in isolation when influencing the printing and final quality and that is why there is no single direct correlation between one target and one property. In fact, there is a complex interplay or collaboration among multiple factors at play. Given the substantial amount of experimental data and the intricate interplay between these parameters, it becomes challenging to assess the relationship between individual factors and properties.