## 4 Discussion  
It is indispensable that the new developments we explicated in the preceding sections to be compared to other existent methods. Direct assessment of our method with that of [PERSON] and [PERSON] [76] is not possible because the latter method: (1) does not allocate statistical significance to the outcomes, (2) has been exclusively tested on deterministic relationships--no indication that it can handle uncertainty as it was needed in our case, (3) has been used as a \"poly-parameterizer\" of a single dimension, instead of being tested in a large number of independent physical parameters, (4) has not demonstrated capabilities of simultaneously engaging continuous and categorical variables, (5) has not demonstrated capabilities of concurrently handling at least bi-response (independent) outputs and (6) the number of segments in the chosen dimension--for all studied functions--is set ambiguously large; thus, it markedly augmented the volume of the subsequent data analysis. The synthesis of tools that we used in this work (MDAC and GLM) are simple, well-accepted, and well-deployed in a wide range of scientific fields. Therefore, simplicity was sought and attained in this work by endorsing lean principles:  
1. Demanding markedly less experimental work by designing trials through a supersaturated planning scheme--the ratio of the number of the profiled parameters to the number of the executed runs was noticeably greater than one. 2. Permitting a large number of parameters to be investigated at the same time. 3. Tolerating different types of parameters (continuous/categorical/discrete) to mingle in the same model. 4. Utilizing or demanding no new tools that otherwise would possibly prolong the assimilation and penetration horizon. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Term** & **Coef** & **SE Coef** & **T-Value** & \(p\)**-Value** & **VIF** \\ \hline
**Constant** & 0.9953 & 0.0443 & 22.48 & 0.000 & \\ FC1 & \(-0.1882\) & \(0.0456\) & \(-4.13\) & 0.003 & 1.36 \\ FC2 & \(-0.1687\) & \(0.0504\) & \(-3.35\) & 0.010 &  
The least absolute shrinkage and selection operation (LASSO) has been recommended as an advanced graphical/statistical technique that may aid in comparing supersaturated screening solutions, which have been derived from various other approaches [75]. To achieve superb computational speed while ensuring convenience in the verification process, we implemented the R-package 'glmnet' [102], which permits the fitting of the LASSO and the Elastic-Net model paths in regression through the coordinate descent [103]. In Figure 13A, we depict the LASSO results (coefficients of factors versus log(\(\lambda\))) for the EC response. The first six stronger contributions are: FC4, FC1, FC11, F12, F9, and FC18. The LASSO-derived list of active factors includes all four active factors that appeared in the final solution of our method (FC4, FC1, FC11, F12). Therefore, we ought to decide whether or not the F9 and FC18 should have been predicted by our proposed method. Consequently, a versatile comparison measure should be employed to allow assessing the upshot of this discrepancy. By simultaneously keeping score of the finer model-fitting accuracy and the tolerated model complexity, the measure should provide guidance toward a solution that precludes either a potential underfitting or overfitting. Thus, a proper estimator of the prediction error that takes into account (1) the relative quality of the statistical model and (2) the small dataset is the 'corrected' Akaike information criterion (AICc) [104; 105; 106]. The AICc, which is convenient and useful as long as the condition n/p < 40 holds, is defined as follows:  
\[\text{AICc}=\text{nlog}\left(\frac{\text{RSS}}{\text{n}}\right)+2\text{p}+\frac {2\text{p}^{2}+2\text{p}}{\text{n}-\text{p}-1}\]  
Figure 13: LASSO plots (‘glmnet’ R-package) for the identification of important effects in the (**A**) EC and (**B**) JR supersaturated datasets.