## 3 Experimental Process and Analysis
### Diagnostic Analysis  
To verify the applicability of the model, the diagnostic analysis was generally made by making the regression analysis and giving some diagnostic plots.  
The design of the normal probability plot made the cumulative normal distribution a straight line. In Figure 6a, the points on the normal probability plot were approximately distributed in a straight line, which indicates the accuracy of the data for calculating the electric field intensity.  
As can be seen from the residuals versus run plot in Figure 6b, the residuals of runs were distributed within the prescribed appropriate range, which implies that choosing the quadratic polynomial as the response surface function is particularly suitable. In Figure 6c and Table 9, there was a good correlation  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Source of** & **Sum of** & **Degrees of** & **Mean** & \(F_{0}\) & \(p\)**-Value** & **Significance** \\
**Variation** & **Squares** & **Freedom** & **Square** & & & \\ \hline Regression & \(3.468\times 10^{7}\) & \(14\) & \(2.477\times 10^{6}\) & \(128.92\) & \(<\)0.0001 & *** \\ \(R_{1}\) & \(2.146\times 10^{7}\) & \(1\) & \(2.146\times 10^{7}\) & \(1116.64\) & \(<\)0.0001 & *** \\ \(h\) & \(1.266\times 10^{6}\) & \(1\) & \(1.266\times 10^{6}\) & \(65.91\) & \(<\)0.0001 & *** \\ \(s\) & \(6.708\times 10^{6}\) & \(1\) & \(6.708\times 10^{6}\) & \(349.10\) & \(<\)0.0001 & *** \\ \(w\) & \(2.714\times 10^{6}\) & \(1\) & \(2.714\times 10^{6}\) & \(141.25\) & \(<\)0.0001 & *** \\ \(R_{1}h\) & \(4.917\times 10^{4}\) & \(1\) & \(4.917\times 10^{4}\) & \(2.56\) & \(0.1305\) & \\ \(R_{1}s\) & \(1.660\times 10^{5}\) & \(1\) & \(1.660\times 10^{5}\) & \(8.64\) & \(0.0101\) & * \\ \(R_{1}w\) & \(3.936\times 10^{3}\) & \(1\) & \(3.936\times 10^{3}\) & \(0.20\) & \(0.6573\) & \\ \(hs\) & \(4.207\times 10^{3}\) & \(1\) & \(4.207\times 10^{3}\) & \(0.22\) & \(0.6466\) & \\ \(tw\) & \(2.741\times 10^{3}\) & \(1\) & \(2.741\times 10^{3}\) & \(0.14\) & \(0.7109\) & \\ \(sw\) & \(7.415\times 10^{3}\) & \(1\) & \(7.415\times 10^{3}\) & \(0.39\) & \(0.5438\) & \\ \({R_{1}}^{2}\) & \(2.365\times 10^{6}\) & \(1\) & \(2.365\times 10^{6}\) & \(123.06\) & \(<\)0.0001 & *** \\ \(h^{2}\) & \(3.424\times 10^{4}\) & \(1\) & \(3.424\times 10^{4}\) & \(1.78\) & \(0.2018\) & \\ \(s^{2}\) & \(1.235\times 10^{6}\) & \(1\) & \(1.235\times 10^{6}\) & \(64.26\) & \(<\)0.0001 & *** \\ \(w^{2}\) & \(199.590\) & \(1\) & \(199.590\) & \(0.01\) & \(0.9202\) & \\ _Lack of Fit_ & \(2.882\times 10^{5}\) & \(15\) & \(2.882\times 10^{5}\) & & & \\ Pure Error & \(0.000\) & \(5\) & \(0.000\) & & & \\ Total & \(3.497\times 10^{7}\) & \(29\) & & & & \\ \hline \hline \end{tabular} Note: *** Extremely significant; ** Very significant; * Significant  
\end{table}
Table 8: Analysis of variance.  
Figure 6: **(a) Normal probability distribution of residuals; (b) residuals versus run; and (c) predicted values versus actual values.  
between the predicted value and the actual value, which indicates that the polynomial model has a good predictive ability. In a word, the response surface model adopted in this study is acceptable.