## 4 Results
### Multiple Regression Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dependent** & \multirow{2}{*}{**Independent Variable**} & \multirow{2}{*}{**B**} & \multirow{2}{*}{**Std. Error**} & \multirow{2}{*}{**Beta (\(\beta\))**} & \multirow{2}{*}{**t**} & \multirow{2}{*}{_p-Value_} & \multirow{2}{*}{**VIF**} \\ \hline  & (constant) & \(-\)17.000 & 11.519 & & \(-\)1.476 & 0.144 & \\  & **gender** & 16.214 & 5.202 & 0.347 & 3.117 & **0.003 *** & 1.638 \\  & smoking status & 9.397 & 7.292 & 0.142 & 1.288 & 0.201 & 1.613 \\  & alcohol status & 0.491 & 4.691 & 0.011 & 0.105 & 0.917 & 1.411 \\  & coffee status & 4.975 & 5.022 & 0.104 & 0.991 & 0.325 & 1.462 \\  & voice user status & \(-\)2.877 & 4.478 & \(-\)0.064 & \(-\)0.642 & 0.522 & 1.319 \\  & **comorbidity** & 10.743 & 4.716 & 0.223 & 2.278 & **0.025 *** & 1.268 \\ Total & surgery status & \(-\)6.746 & 4.632 & \(-\)0.168 & \(-\)1.456 & 0.149 & 1.758 \\ scores\_diff 1 & voice training status & 6.952 & 7.914 & 0.146 & 0.878 & 0.382 & 3.631 \\  & number of voice trainings & \(-\)0.368 & 0.991 & \(-\)0.057 & \(-\)0.371 & 0.711 & 3.136 \\  & F0\_diff 1 & \(-\)0.075 & 0.062 & \(-\)0.148 & \(-\)1.215 & 0.228 & 1.966 \\  & Jitter\_diff 1 & \(-\)0.079 & 0.095 & \(-\)0.077 & \(-\)0.833 & 0.407 & 1.125 \\  & Shimmer\_diff 1 & \(-\)0.246 & 0.629 & \(-\)0.051 & \(-\)0.392 & 0.696 & 2.212 \\  & NHR\_diff 1 & \(-\)27.929 & 23.575 & \(-\)0.161 & \(-\)1.185 & 0.240 & 2.440 \\  & **SFF\_diff 1** & \(-\)0.251 & 0.111 & \(-\)0.260 & \(-\)2.268 & **0.026 *** & 1.740 \\  & MPT\_diff 1 & 0.436 & 0.534 & 0.081 & 0.817 & 0.416 & 1.312 \\ \hline  & (constant) & \(-\)30.154 & 11.094 & & \(-\)2.718 & 0.008 & \\  & **gender** & 19.186 & 5.010 & 0.370 & 3.830 & **\textless{0.001 *}** & 1.638 \\  & smoking status & 3.508 & 7.025 & 0.048 & 0.499 & 0.619 & 1.613 \\  & alcohol status & 3.613 & 4.518 & 0.072 & 0.800 & 0.426 & 1.411 \\  & coffee status & 9.011 & 4.836 & 0.170 & 1.863 & 0.066 & 1.462 \\  & voice user status & 0.672 & 4.313 & 0.014 & 0.156 & 0.877 & 1.319 \\  & **comorbidity** & 14.147 & 4.542 & 0.265 & 3.115 & **0.003 *** & 1.268 \\ Physical & surgery status & \(-\)8.583 & 4.461 & \(-\)0.193 & \(-\)1.924 & 0.058 & 1.758 \\ scores\_diff 1 & voice training status & 8.368 & 7.622 & 0.158 & 1.098 & 0.275 & 3.631 \\  & number of voice trainings & \(-\)0.139 & 0.955 & \(-\)0.019 & \(-\)0.146 & 0.884 & 3.136 \\  & F0\_diff 1 & \(-\)0.086 & 0.059 & \(-\)0.153 & \(-\)1.445 & 0.152 & 1.966 \\  & **Jitter\_diff 1** & \(-\)0.245 & 0.092 & \(-\)0.214 & \(-\)2.668 & **0.009 *** & 1.125 \\  & Shimmer\_diff 1 & \(-\)0.703 & 0.605 & \(-\)0.130 & \(-\)1.161 & 0.249 & 2.212 \\  & NHR\_diff 1 & \(-\)24.291 & 22.705 & \(-\)0.126 & \(-\)1.070 & 0.288 & 2.440 \\  & **SFF\_diff 1** & \(-\)0.314 & 0.107 & \(-\)0.293 & \(-\)2.940 & **0.004 *** & 1.740 \\  & **MPT\_diff 1** & 1.069 & 0.514 & 0.180 & 2.081 & **0.041 *** & 1.312 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Multiple regression coefficients of overall parameters in K-VRQOL. Table 4 summarizes the model fit statistics for the multiple regression analyses conducted on the total, physical, and emotional domains of the K-VRQOL. For the total domain, the model demonstrated a moderate level of explanatory power with an R\({}^{2}\) of 0.378 and an adjusted R\({}^{2}\) of 0.264 (\(p<\) 0.001), indicating that approximately 26.4% of the variance in the overall improvement in voice-related quality of life was explained by the selected predictors. In the physical domain, the model showed relatively strong explanatory power, with an R\({}^{2}\) of 0.533 and an adjusted R\({}^{2}\) of 0.447 (\(p<\) 0.001). This suggests that nearly 44.7% of the variance in the improvement in physical voice-related quality of life could be attributed to the independent variables included in the model. For the emotional domain, the model yielded a lower R\({}^{2}\) value of 0.284 and an adjusted R\({}^{2}\) of 0.153 (\(p=\) 0.014), indicating a modest level of model fit, with 15.3% of the variance in emotional improvement being explained by the predictors.