## 3 Results and Discussion
The performance of the absorption class varies significantly between different material groups, suggesting that material selection should be application-specific and frequency requirement-specific. Metals and glass-based materials generally perform well, while plastics/polymers and textile materials show varied performance due to variations in composition and structure. The results obtained based on the qualitative analysis indicate an imperative need to consider both the material type and the testing frequency when evaluating and designing solutions for passive noise control and improving acoustic comfort in various environments. These findings provide important directions for the development of new materials or combinations of existing materials to optimize sound absorption and acoustic comfort according to the specific requirements of each application. All research hypotheses have been validated. It has been determined that increasing the thickness from 10 mm to 50 mm resulted in an approximately 30% improvement in the absorption coefficient. Additionally, materials analyzed in five categories (metals, glass-based materials, wood-based materials, polymers, and textiles) demonstrated variable efficiencies at different frequencies regarding acoustic absorption properties, with textile and polymeric materials showing superior properties in the mid-frequency range. The statistical methods used, including descriptive analysis, the Mann-Whitney U test, three multiple linear regression models and the CHAID decision tree method, provided a solid scientific basis for material selection, emphasizing the importance of integrating acoustic considerations in vehicle design from the design phase. This contributes to adopting sustainable strategies in material selection to ensure acoustic comfort and has a significant impact on the automotive manufacturing industry. Descriptive analysis allowed for a preliminary evaluation of the data collected from experimental tests and revealed significant variations between material properties and the acoustic absorption coefficient. The Kruskal-Wallis analysis revealed significant differences between material classes in terms of the thickness, sound absorption coefficient and frequency. These results, consolidated by multiple [PERSON]'s Post Hoc comparisons, indicate significant variability in acoustic properties depending on the material composition. The three multiple linear regression models, with the collinearity diagnosis for the best predictors of acoustic comfort, acoustic absorption class and absorption class performance, identified the sound absorption coefficient as the main predictor for acoustic comfort and absorption class performance. This underscores the importance of optimizing the absorption coefficient in developing materials for improving acoustic properties. The decision tree method allowed for identifying and visualizing relationships between categorical variables and the acoustic performance of different materials. Analyzing these relationships, important factors in material selection for specific applications could be identified and prioritized, essential for guiding the material selection process to achieve effective and customized solutions for noise control in various acoustic environments. It was also determined that certain combinations of material properties can optimize sound absorption at certain frequencies. The research results indicate that the acoustic performance of various materials used and currently employed to ensure comfort in the cabins of transportation systems is significantly influenced by their physical properties. An analysis and evaluation of materials based on experimental results highlighted a direct correlation between the material composition and its acoustic efficiency, identifying optimal materials for different acoustic environments and frequency ranges where the acoustic absorption coefficient reaches maximum values.