## 3 Materials and Methods
Figure 5 shows the predicted probability of fiber optic adoption across Mexican states, based on factors such as broadband access, availability of computer equipment, and the percentage of households with three telecommunication services. Each bar represents a state, and the height of the bar indicates the likelihood that households in that state will adopt fiber optic technology. Higher bars reflect states with a greater probability of adopting fiber optic infrastructure, suggesting that these regions have more favorable conditions, such as better internet access and higher technology adoption rates. Conversely, states with shorter bars have lower probabilities of fiber optic adoption, indicating less favorable conditions for adopting this technology. \begin{table}
\begin{tabular}{l c c c c} \hline  & **Estimate** & **SE** & **tStat** & \(p\) **Value** \\ \hline (Intercept) & \(-1.0005\) & 2.9241 & \(-0.34215\) & 0.73224 \\ \(x_{1}\) & 0.008396 & 0.084437 & 0.099435 & 0.92079 \\ \(x_{2}\) & \(-0.10806\) & 0.13164 & \(-0.82089\) & 0.41171 \\ \(x_{3}\) & 0.27948 & 0.21577 & 1.2953 & 0.19522 \\ \hline \end{tabular}
\end{table} 
Table 12: Model coefficients. The correlation matrix shown in Figure 6 was generated using Pearson's correlation coefficient, which shows the linear relationships between the four variables. These relationships help highlight which factors may have a positive or negative correlation with each other in the context of reducing the digital divide. For instance, negative correlations may suggest that improvements in certain areas (such as technological infrastructure) correlate with a reduction in households relying on only one service. Now, based on Table 13, the concepts are explained as follows. The intercept is the baseline value when all other variables are zero. The infrastructure growth, a small positive coefficient, suggests that increased infrastructure growth slightly increases the probability of being in the high \"one service\" category, though the effect is minimal. Digital TV penetration, a negative coefficient, indicates that as digital TV penetration increases, the probability of relying on only one service decreases. Combined services, a positive coefficient, suggests that households with access to multiple services are more likely to be in the high \"one service\" category, though the relationship is counterintuitive and may indicate other latent factors. Therefore, the negative correlation between one service and  
Figure 5: Predicted probability of fiber optic adoption across Mexican states based on broadband access, computer equipment availability, and telecommunication services. Figure 6: Correlation matrix of digital divide reduction. infrastructure growth (\(-0.3077\)) suggests that as infrastructure grows, fewer households rely on just one service. The strong positive correlation between digital TV penetration and combined services (0.7778) indicates that states with higher digital TV penetration tend to have a higher number of households subscribing to multiple services. Figure 7 displays the predicted probabilities of reducing the digital divide for each Mexican state. Higher bars indicate regions where the digital divide is expected to diminish, suggesting that these regions have favorable conditions for improving digital access. The model helps identify areas where policies and investments could have the most significant impact on reducing the digital divide.