## 3 Results and Discussion
### Multiple Linear Regression Analysis  
In order to examine the effects of plasma process parameters on enhancing surface hydrophobicity and the correlations among the effects of individual parameters, multiple linear regression analysis was employed. The parameters are flow rate of helium, amount of TMS, and discharge power. Multiple linear regression is used as an analyzing tool to investigate the associations between particular independent variables and the dependent variable, even with existence of correlations between independent variables. Multiple linear regression was applied to analyze the effects of the independent variables--discharge power (Power), amount of TMS (TMS), and flow rate of helium (Helium)--on a dependent variable, contact angle (CA), by means of software SPSS. Table 7 indicates that the amount of TMS is highly related to contact angle (within a 95% confidence interval). The amount of TMS applied is critical for enhancing surface hydrophobicity. On the contrary, the flow rate of helium and discharge power were found to have less significant effects. According to Table 8, the adjusted square R\({}^{2}\) is 0.733, which means that 73.3% of the variance in contact angle can be predicted from the independent variables. [PERSON] and [PERSON] suggested that the fitness of the regression model is acceptable if the R-square is greater than 0.6.  
R-square statistics indicate the model's fitness by representing the percentage of dependent variations that fit into the linear regression model. The significance of the F-test for this regression model is smaller than 0.000; the model is highly able to predict the contact angle with the independent variables with a 95% confidence level.  
Table 8 shows a comparison of the contributions of the independent variables by standardized coefficients that convert the variables to the same scale. The regression coefficient of flow rate of helium is -0.071. The negative result indicates that the contact angle decreases as the flow rate of helium increases. This agrees with the result in the findings that a low flow rate facilitates plasma deposition. On the other hand, the regression coefficients are 0.804 and 0.248 for amount of TMS and discharge power, respectively. The positive sign indicates that the contact angle increases as the discharge power or amount of TMS increases. Having the highest coefficient (0.804) among all independent variables, the amount of TMS contributes strongly to contact angle results. All the relationships are highly statistically significant (significance: 0.000) except flow rate of helium. The flow rate of helium contributes relatively less to plasma deposition. In fact, the flow rate of helium provides a medium for ionization instead of being a critical factor for the deposition.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Independent Variable** & **Significance (1-Tailed)** \\ \hline Helium & 0.334 \\ TMS & 0.000 \\ Power & 0.298 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Significance of the model parameters.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Independent** & **Unstandardized** & **Standardized** & **Collinearity** \\
**Variable** & **Coefficients** & **Coefficients** & **Statistics Tolerance** \\ \hline (Constant) & –69.216 & N/A & N/A \\ Helium & –0.642 & –0.071 & 1.000 \\ TMS & 548.704 & 0.804 & 1.000 \\ Power & 0.846 & 0.248 & 1.000 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Coefficients of the regression model.  
The Collinearity Statistics Tolerance demonstrates the proportion of variation of an independent variable not explained by the linear relationships with other independent variables. A value close to 1 indicates no multicollinearity problem, whereas a value close to 0 indicates a multicollinearity problem. Table 8 shows that the tolerance values of all the independent variables are equal to one. Hence, all independent variables are highly correlated and have stable standard errors for the regression coefficient.