## 5 Discussion  
The research presented in this document is a critical exploration of the relationship between telecommunications infrastructure, digital inclusion, and economic growth within the context of Mexico. A fundamental theme of the study is the persistent digital divide that continues to affect both urban and rural populations. The paper evaluates how different variables, such as broadband access, service bundling, and fiber optic penetration, influence the digital landscape. One of the standout aspects of the study is its use of predictive models to identify regions with low technological adoption and to offer policy recommendations. This data-driven approach is essential in addressing the disparities in technological access across the country. Furthermore, the correlation between multiple services and broadband adoption underscores the importance of tailored service offerings to drive higher technology uptake, particularly in underserved regions. The models proposed are not just academic exercises but offer real-world applications, especially in informing public policy decisions that can better allocate infrastructure investments. From a methodological perspective, the use of [PERSON]'s correlation matrix and logistic regression model demonstrates a rigorous statistical approach to understanding the dynamics of telecommunications service adoption. By linking broadband access, computer availability, and multiple service subscriptions, the research highlights key variables that affect fiber optic adoption. This type of modeling is essential for policymakers, as it provides a clear indication of where investments can be most effective. The research further explores the correlation between these variables, showing that infrastructure growth and digital TV penetration have a direct impact on the likelihood of multiple service adoption, while access to only one service remains a challenge in regions with slower infrastructure development. This detailed analysis contributes significantly to the literature on telecommunications and offers a comprehensive framework for improving digital inclusion in Mexico. One of the paper's most important contributions is its focus on the practical implications of its findings. By using predictive models to forecast the reduction of the digital divide across different states, the research offers a roadmap for strategic investments in telecommunications infrastructure. The predicted probabilities for fiber optic adoption by state, for example, provide a granular understanding of where public policy can have the most significant impact. This type of predictive analytics is invaluable for governmental planning and budget allocation, as it allows for targeted interventions in regions that are most in need of technological advancement. The research does not stop at identifying problems but actively proposes solutions that can help bridge the digital divide, particularly through service bundling and promoting multi-service adoption strategies. However, there are some limitations to consider. While the research provides a robust framework for analyzing telecommunications data, the complexity of the digital divide suggests that there may be additional social and economic factors not fully accounted for in the model. For instance, digital literacy and affordability are critical variables that could influence technology adoption but are not directly explored in depth. Moreover, while the study emphasizes infrastructure investment, it does not fully address the regulatory and bureaucratic challenges that may impede the rollout of new technologies. Future research could expand on these aspects, integrating more qualitative data to complement the quantitative models and providing a more holistic view of the barriers to digital inclusion. Despite these gaps, the study remains a valuable resource for understanding the intersection of technology, infrastructure, and socio-economic development.