# Engineering Supply Chain Transportation Indexes through Big Data Analytics and Deep Learning
## 3 Results
### Statistical Analysis
Next, for the analysis of the impact of supply chain firms' website big data analytics on Inferred Rates, Truckload Line Haul Index, and Net Trailer Orders, the authors deployed three linear regression models. The first regression model with Inferred Rates as the dependent variable was verified overall with R\({}^{2}\) = 1.00 and a \(p\)-value = 0.000 \(<\) a = 0.01 level of significance, as seen in Table 6. The independent variables of the authority score, paid costs, bounce rate, time on site, and website visits were found to be statistically significant with \(p\)-values = 0.000 \(<\) a = 0.001 significance level. For every 1% increase in authority score, paid costs, bounce rate, time on site, and website visits, supply chain firms' Inferred Rates variate up to \(-\)69.9%, \(-\)68.8%, 70.1%, 87.1%, and \(-\)43.9% accordingly. The third research hypothesis (H3) is verified, and it means that there is a strong connection between supply chain firms' website big data analytics and their Inferred Rates. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variables** & **Standardized Coefficient** & **R\({}^{2}\)** & **F** & \(p\)**-Value** \\ \hline Constant & - & & & 0.000 \({}^{**}\) \\ Authority Score & 0.017 & & & 0.000 \({}^{**}\) \\ Paid Costs & \(-\)1.051 & 1.000 & - & 0.000 \({}^{**}\) \\ Bounce Rate & 0.322 & 1.000 & - & 0.000 \({}^{**}\) \\ Time on Site & \(-\)0.075 & & & 0.000 \({}^{**}\) \\ Website Visits & \(-\)0.640 & & & 0.000 \({}^{**}\) \\ \hline \hline \end{tabular}  
* indicate statistical significance at the 99% level.  
\end{table} 
Table 4: Impact of Supply Chain Firms’ Big Data Analytic Metrics on their Freight Shipments Index. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variables** & **Standardized Coefficient** & **R\({}^{2}\)** & **F** & \(p\)**-Value** \\ \hline Constant & - & & & 0.000 \({}^{**}\) \\ Authority Score & \(-\)0.225 & & & 0.000 \({}^{**}\) \\ Paid Costs & \(-\)1.207 & 1.000 & - & 0.000 \({}^{**}\) \\ Bounce Rate & 0.555 & 1.000 & - & 0.000 \({}^{**}\) \\ Time on Site & 0.248 & & & 0.000 \({}^{**}\) \\ Website Visits & \(-\)0.747 & & & 0.000 \({}^{**}\) \\ \hline \hline \end{tabular}  
* indicate statistical significance at the 99% level.  
\end{table} 
Table 5: Impact of Supply Chain Firms’ Big Data Analytic Metrics on their Freight Expenditure Index. In Table 7, the linear regression model of the Truckload Line Haul Index is produced, which is verified overall with R\({}^{2}\) = 1.00 and a \(p\)-value = 0.000 \(<\) a = 0.01 level of significance. Supply chain website authority score, paid costs, bounce rate, time on site, and website visits had a significant impact on the Truckload Line Haul Index, with \(p\)-values \(<\) a = 0.01 significance level. The Truckload Line Haul Index decreases by 27.6%, 76%, and 22.3%, increases by 37.2%, and decreases by 44.7% when the authority score, paid costs, bounce rate, time on site, and website visits increase by 1%. Thus, the fourth research hypothesis (H4) is verified, with supply chain firms' website big data analytics being capable of decreasing their Truckload Line Haul Index. Regarding the last linear regression model of supply chain Net Trailer Orders, it was verified overall with R\({}^{2}\) = 1.00 and a \(p\)-value = 0.000 \(<\) a = 0.01 level of significance, as observed in Table 8. All of the independent variables included in Table 8 had a statistically significant effect on supply chain firms' Net Trailer Orders dependent variable. With every 1% rise in the authority score, paid costs, bounce rate, time on site, and website visits, supply chain firms' Net Trailer Orders variate up to \(-\)23.5%, 20.1%, 85.7%, \(-\)44.1%, and \(-\)70.9%, respectively. Hence, the fifth and final research hypothesis (H5) of the present research is verified, which means that Net Trailer Orders and supply chain firms' website big data analytics are connected positively.