## 3 Materials and Methods
### Statistical Analysis  
In this study, multiple regression analysis was employed to identify the factors influencing K-VRQOL. Multiple regression is a statistical technique that allows the simultaneous analysis of the relationship between a single continuous dependent variable and two or more independent variables [30]. One of its key advantages lies in its ability to assess the individual contribution of each predictor while controlling for the effects of other variables in the model. As an extension of simple linear regression, multiple regression is particularly suitable for clinical and social science research, where multiple interacting factors often influence the outcome of interest [31; 32].  
Each regression coefficient indicates the extent and direction (positive or negative) of the change in the dependent variable associated with a one-unit change in the corresponding independent variable. The goodness-of-fit of the model is evaluated using the coefficient of determination (R\({}^{2}\)) and the adjusted R\({}^{2}\), while the overall significance of the model is assessed using an F-test, and the statistical significance of individual predictors is tested using t-tests. To ensure the reliability of the analysis, key statistical assumptions--such as the linearity, independence of errors, homoscedasticity, normality of residuals, and absence of multicollinearity--were examined prior to the interpretation of the results. In  
\begin{table}
\begin{tabular}{l l c c c c c} \hline \hline
**Emotional domain: 4, 5, 8, 10 Physical domain: 1, 2, 3, 6, 7, 9** & & & & & & \\ \hline
1. & I have trouble speaking loudly or being heard in noisy situations. & 1 & 2 & 3 & 4 & 5 \\
2. & I run out of air and need to take frequent breaths when talking. & 1 & 2 & 3 & 4 & 5 \\
3. & I sometimes do not know what will come out when I begin speaking. & 1 & 2 & 3 & 4 & 5 \\
4. & I am sometime anxious or frustrated (because of my voice). & 1 & 2 & 3 & 4 & 5 \\
5. & I sometimes get depressed (because of my voice). & 1 & 2 & 3 & 4 & 5 \\
6. & I have trouble using the telephone (because of my voice). & 1 & 2 & 3 & 4 & 5 \\
7. & I have trouble doing my job or practicing my profession (because of my voice). & 1 & 2 & 3 & 4 & 5 \\
8. & I avoid going out socially (because of my voice). & 1 & 2 & 3 & 4 & 5 \\
9. & I have to repeat myself to be understood. & 1 & 2 & 3 & 4 & 5 \\
10. & I have become less outgoing (because of my voice). & 1 & 2 & 3 & 4 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: K-VRQOL [9; 11].  
particular, multicollinearity, which can distort the interpretation of regression coefficients, was assessed using the variance inflation factor (VIF) and tolerance values [30; 31; 32].  
Multiple regression analysis is widely used across various disciplines and is particularly valuable in studies that aim to explain or predict health-related quality of life. In this study, the independent variables included gender, smoking status, voice use status, voice training experience, and acoustic parameters measured before and after therapy (e.g., fundamental frequency, jitter, shimmer, NHR, SFF, and MPT). The analysis aimed to determine the significant predictors of K-VRQOL and provide a basis for clinical intervention and rehabilitation planning for individuals with voice disorders.