## 4 Discussion
The constant term, in the original AICc expression, has been arbitrarily taken to be zero since the estimator is used for direct comparison between two models. Moreover, n and p are the sample size and the 'augmented' number of parameters (p = k + 2), respectively; the RSS is the residual sum of squares in the linear regression context. Particularly, for the least-squares model fitting, the \(p\)-value in the AICc is meant to incorporate the two contributions--besides the k controlling factors--owing to: (1) the intercept and (2) the noise in the model. Hence, for a single parameter model (k = 1), we have \(p\) = 3 and so forth. For our case study, we have n/p = 0.54 (<40), which justifies the adoption of the corrected version of the Akaike information criterion. In Table 11, we progressively list the groups of strong effects to minimize the AICc score. The sequential insertion of impending active factors in the group decreases the AICc score until the four factors--FC4, FC1, FC11, and F12--all partake in the model. Incorporating F9 and F18 in the model causes AICc score to increase. By combining the predictive and comparative features of LASSO and AICc, we verified that the final cut of the active factors agrees exactly with our results. However, the clear gain in practicality is supported by the fact that the implementation of MDAC and GLM may be notably more accessible to energy engineers. This is because only the basic functions of an ordinary spreadsheet application are needed to be recalled using any portable computing device. On the other hand, LASSO and AICc require more advanced knowledge of statistical interpretation of the information. Similarly, we reapply the LASSO method and the AICc estimator on the combined JR dataset. The 'glmnet'-generated output is shown in Figure 13B. The leading effects are quickly identified to be: FC2, FC1, F19, FC8, and FC15. We notice that the LASSO prediction agrees in identifying the four out of the five factors when it is compared with the outcome of our proposed method. The fifth factor is identified as the FC15 according to the LASSO technique; the new method predicts the FC4 instead. Next, we estimate the AICc score for the sequential insertion of the five candidate factors in the model and we list them in Table 12.  
It becomes apparent that the five-factor solution favors our solution version because it minimizes the AICc score to an optimal value of \(-\)50.55. In comparison, the AICc estimate for the LASSO solution version is a non-optimal value of \(-\)47.98. It is remarkable that even a six-factor solution that compromises the difference between the two approaches, by including both FC4 and FC15 in the model, does not improve the overall quality of the model since the AICc score climbs even more to a value of \(-\)43.51. Therefore, we verify that our proposed solution could also be more competitive with other more sophisticated methods. We may now ponder on the tangible benefits of this real study by contemplating the differences between the outcomes of the original energy assessment and its resulting improvement recommendation (Table 13). It becomes evident that the proposed approach offers a quick and practical resort to a complex situation by providing a pathway for \begin{table}
\begin{tabular}{l c c c c} \hline \multirow{2}{*}{**Factor Group**} & **Sum of** & \(p\) & **n** & **AICc** \\ \cline{2-5}  & **Squares** & & & \\ \hline
**FC4** & 11,822.58 & 3 & 14 & 102.74 \\
**FC4,FC1** & **8810.14** & **4** & **14** & **102.67** \\ FC4,FC1,FC11** & 4538.14 & 5 & 14 & 98.44 \\
**FC4,FC1,FC11** & **1622.77** & **6** & **14** & **90.54** \\
**F12** & **1365.94** & 7 & 14 & 96.79 \\
**FC4,FC1,FC11** & **647.7** & 8 & 14 & 98.48 \\
**F12,F9,FC18** & & & & \\ \hline \end{tabular}
\end{table} 
Table 11: The corrected Akaike information criterion scores for sequential factor screening of the EC response. ramping up the energy performance status by directly intervening in the simultaneous minimization of the energy consumption of the respective reference apartment unit.