## 3 Results and Discussion
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Figure 2: _Cont._  
Figure 2: Box plots and pairwise comparisons of class material, acoustic comfort and material type. The Kruskal–Wallis test with [PERSON]’s post hoc multiple comparisons was applied to test the differences in the (**a**) thickness, (**c**) sound absorption coefficient and (**e**) frequency among different class materials. The pairwise comparisons (**b**,**d**,**f**) illustrate the statistical significance of differences between each pair of class materials, with lines connecting pairs where the adjusted significance level is less than 0.05. The results of [PERSON]'s post hoc multiple comparisons (Figure 2) indicate (with a blue line in Figure 3b,d,f) that all the differences between the absorption acoustic class and between the absorption class performances are significant (Figure 3g-i) for all the continuous variables from Table 7. According to [PERSON]'s post hoc multiple comparisons, even though there were statistically significant differences between class performances, there were no two-by-two class performances for sound absorption (3(h) or frequency 3(i)). Additionally, the box plots in Figure 3a,c,e show the quantitative differences between the absorption acoustic class and the thickness (Figure 3a), sound absorption coefficient (Figure 3c) and frequency (Figure 3e), respectively. The Kruskal-Wallis test with [PERSON]'s post hoc multiple comparisons was applied to test the differences in the (a) thickness, (c) sound absorption coefficient and (e) frequency among different absorption acoustic classes. The pairwise comparisons (b, d, f) illustrate the statistical significance of differences between each pair of absorption acoustic classes, with lines connecting pairs where the adjusted significance level is less than 0.05. These significant differences justify determining the best predictor for (1) acoustic comfort, (2) absorption acoustic class and (3) absorption class performance by applying the multilinear regression models with collinearity diagnosis, with these indicators as the dependent variable in the models; as independent variables, the following were considered: (1) class material, (2) thickness (mm), (3) sound absorption coefficient, (4) frequency (Hz) and (5) material type. For all three models, the results are presented below. According to Table 7, the following can be determined:  
In Model 1, with acoustic comfort as the dependent variable, the independent variables introduced in the model explained 38.5% of the dependent variable variance. In Model 2, with the absorption acoustic class as the dependent variable, the independent variables introduced in the model explained 87.3% of the dependent variable variance. In Model 3, with absorption class performance as the dependent variable, the independent variables introduced in the model explained 88.0% of the dependent variable variance. Specific Results:  
The material class, thickness, sound absorption coefficient, frequency and type of material are all relevant predictors, but their specific contribution to each aspect of acoustic performance varies. The results suggest that adjustments in thickness and the selection of material type have a significant impact on improving acoustic absorption and acoustic comfort. For example, increasing the thickness of a material significantly enhances its acoustic absorption coefficient. Figure 3: _Cont._  
Figure 3: _Cont_. Figure 3: _Cont._  
The testing frequency is also a significant factor, as materials exhibit different acoustic behaviors at various frequencies. Therefore, the selection of materials depends on the specific frequency spectrum targeted for acoustic performance optimization. All the regression models are statistically significant according to the ANOVA results in Table 8.  
According to the results from Tables 9 and 10, the best predictor(s) for each model are as follows: