## 6 Results
### DC Current  
The computed results of the first set of data related to DC_CurrentString1 using (i) the method of Ordinary Least Squares, (ii) the Ridge Method, (iii) the LASSO method, and (iv) the Elastic Net Method are given in Table 1.  
The results of the Multiple Linear Regression Model show that the RMSE values range from 0.648 to 2.018, with the best-performing model with the parameters/factors (X2, X5) having the lowest RMSE (0.648) and highest R-squared (0.9352). In addition, the worst model has the highest value of RMSE (2.018) and the parameters/factors X3 and X7. It may be mentioned that coefficients for the predictor variables vary widely across different combinations, indicating diverse influences of the variables on the DC current. The LAR models show a slight improvement in RMSE compared to MLRM, with values ranging from 0.645 to 2.020. The R-squared values are slightly higher or comparable to those of the LR models, indicating a better or similar fit. Also, the worst model has the highest value of RMSE (2.018) and the parameters/factors X3 and X7. Additionally, some coefficients are shrunk to zero, indicating variable selection by the Lasso method, which helps in simplifying the model. The RR models, which have RMSE values ranging from 0.642 to 2.041, show slight variations compared to the other methods. The R-squared values are generally high, indicating a good fit, with the highest being 0.9363 for the (X2, X5) combination; also, the worst model has the highest value of RMSE (2.018) and the parameters/factors X3 and X7. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \multicolumn{2}{l}{**DC\_CurrentString1**} & \multicolumn{5}{c}{**Model Estimators**} \\ \hline
**Regression** & **Predictor Variables** & **RMSE** & **R Sq.** & **Constant** & **b1** & **b2** \\ \hline \multirow{6}{*}{Linear} & X2, X3 & 0.650 & 0.9347 & \(-147.80\) & 0.21 & 0.14 \\  & X2, X5 & 0.648 & 0.9352 & \(-1.786\) & 0.174 & \(-0.08\) \\  & X2, X7 & 0.661 & 0.9327 & \(-5.735\) & 0.212 & \(-0.026\) \\  & X3, X5 & 0.840 & 0.8911 & \(-8.329\) & 0.023 & \(-0.378\) \\  & X3, X7 & 2.018 & 0.3716 & \(-1619.401\) & 1.606 & 3.089 \\  & X5, X7 & 0.774 & 0.9077 & 13.877 & \(-0.38\) & 0.54 \\ \hline \multirow{6}{*}{Lasso} & X2, X3 & 0.648 & 0.9351 & \(-139.65\) & 0.206 & 0.133 \\  & X2, X5 & 0.645 & 0.9358 & \(-1.81\) & 0.173 & \(-0.078\) \\  & X2, X7 & 0.654 & 0.9340 & \(-5.58\) & 0.208 & 0 \\  & X3, X5 & 0.848 & 0.8892 & \(14.48\) & 0 & \(-0.37\) \\  & X3, X7 & 2.020 & 0.3706 & \(-1609.098\) & 1.596 & 3.06 \\  & X5, X7 & 0.774 & 0.9074 & 13.86 & \(-0.37\) & 0.52 \\ \hline \multirow{6}{*}{Ridge} & X2, X3 & 0.676 & 0.9294 & \(-191.138\) & 0.187 & 0.185 \\  & X2, X5 & 0.642 & 0.9363 & 1.528 & 0.135 & \(-0.133\) \\ \cline{1-1}  & X2, X7 & 0.657 & 0.9333 & \(-4.849\) & 0.193 & 0.025 \\ \cline{1-1}  & X3, X5 & 0.907 & 0.8730 & \(-76.24\) & 0.089 & \(-0.341\) \\ \cline{1-1}  & X3, X7 & 2.041 & 0.3571 & \(-1473.314\) & 1.462 & 2.756 \\ \cline{1-1}  & X5, X7 & 0.804 & 0.9003 & 13.145 & \(-0.348\) & 0.499 \\ \hline \multirow{6}{*}{Elastic Net} & X2, X3 & 0.648 & 0.9351 & \(-144.558\) & 0.205 & 0.138 \\ \cline{1-1}  & X2, X5 & 0.645 & 0.9358 & \(-1.586\) & 0.071 & \(-0.083\) \\ \cline{1-1}  & X2, X7 & 0.653 & 0.9341 & \(-5.548\) & 0.207 & 0 \\ \cline{1-1}  & X3, X5 & 0.842 & 0.8905 & \(-6.71\) & 0.02 & \(-0.38\) \\ \cline{1-1}  & X3, X7 & 2.020 & 0.3705 & \(-1607.260\) & 1.594 & 3.060 \\ \cline{1-1}  & X5, X7 & 0.774 & 0.9075 & 13.844 & \(-0.377\) & 0.527 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance comparison of regression techniques for predicting DC Current String 1. The coefficients are generally more stable compared to the LR due to the regularization effect of RR, which helps manage the multicollinearity. The results of the ENR models also show RMSE values ranging from 0.645 to 2.020, similar to Lasso. The R-squared values are high, comparable to those of Lasso and Ridge, with the highest being 0.9358 for the (X2, X5) combination.