## 4 Conclusions  
This study was based on real data collected by the authors and provided a comprehensive evaluation of various materials used in the automotive industry to improve noise absorption and acoustic comfort. Through quantitative analysis using multiple linear regression models and decision tree methodologies, we identified the sound absorption coefficient as the key predictor for optimizing acoustic comfort and material performance. The findings emphasize the need for rigorous material selection early in the design process to ensure acoustic efficiency without compromising sustainability. Key results demonstrated that material thickness and composition significantly impact absorption performance, particularly for textile- and polymer-based materials, which exhibited superior mid-frequency absorption. Increasing material thickness by 40 mm, for example, improved the absorption coefficient by approximately 30%. This study's predictive models offer a new framework for evaluating acoustic performance in the early design phases, contributing to sustainable material selection strategies. The statistical methods, including descriptive analysis, the Mann-Whitney U test, multiple linear regression models and the CHAID decision tree method, provided a robust scientific framework for material selection in vehicle design, emphasizing the importance of acoustic considerations from the outset. Descriptive analysis revealed significant variations in material properties and acoustic absorption coefficients, while Kruskal-Wallis analysis and [PERSON]'s post hoc comparisons highlighted differences between material classes. Multiple linear regression models identified the sound absorption coefficient as a key predictor of acoustic comfort and absorption performance. The decision tree method further clarified relationships between variables, aiding in the identification of crucial factors for optimizing material selection to enhance noise control in automotive manufacturing. The novelty of our research lies in the development and validation of predictive models that estimate acoustic performance based on the physical properties of materials categorized into different classes. This study emphasizes the importance of selecting materials not only based on acoustic performance but also on their environmental sustainability impact in accordance with current and future regulations in the field of energy efficiency and carbon emissions. Although predictive models provide a solid basis for material selection, they need to be continuously updated and refined to reflect new findings and emerging technologies. Furthermore, the complex interaction between various material parameters and their influence on acoustic properties requires additional investigation to thoroughly understand all aspects of acoustic performance. The findings contribute to advancing knowledge in the field of noise reduction by offering a clearer understanding of how material properties, such as thickness and composition, directly influence acoustic absorption. By identifying the sound absorption coefficient as the primary predictor of acoustic comfort, this research provides a quantitative framework for material selection in the early design phase, something previously lacking in the industry. Additionally, the use of advanced statistical methods helps to refine material selection processes, enabling the creation of customized solutions for noise control. This contributes to both theoretical knowledge and practical applications, especially in the context of sustainable material innovation in automotive manufacturing. This research lays the groundwork for future innovations in noise control by emphasizing the direct impact of material properties on acoustic performance, guiding the development of customized noise reduction solutions in automotive manufacturing. For future research, we are focusing on exploring the integration of advanced artificial intelligence (AI) and machine learning technologies to further optimize predictive models.