## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **B** & **Std.Err-of b** & **t** & **\(p\)-Value** \\ \hline Intercept & 6.928794 & 0.278102 & 24.91457 & \textless{}0.001 \\ Gender (0: males, 1: females) & \(-\)0.638936 & 0.236061 & \(-\)2.70666 & 0.007043 \\ Mean CS & 1.891935 & 0.069632 & 27.17068 & 0.000000 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Linear model regression analysis. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **Stage** & **N** & \begin{tabular}{c} **Mean Age** \\ **(Years)** \\ \end{tabular} & **Std.Dev** & **Std.Err** & \(-\)**95\%** & \(+\)**95\%** & \
\begin{tabular}{c} **Min Age** \\ **(Years)** \\ \end{tabular} &
\begin{tabular}{c} **Max Age** \\ **(Years)** \\ \end{tabular} \\ \hline \multirow{4}{*}{Total} & CS1 & 78 & 9.21 & 1.36 & 0.15 & 8.91 & 9.52 & 6.40 & 13.20 \\  & CS2 & 76 & 10.34 & 1.61 & 0.18 & 9.97 & 10.71 & 6.40 & 13.80 \\  & CS3 & 53 & 10.97 & 1.41 & 0.19 & 10.58 & 11.36 & 6.90 & 16.00 \\  & CS4 & 82 & 13.61 & 2.90 & 0.32 & 12.98 & 14.25 & 7.90 & 20.90 \\  & CS5 & 123 & 16.63 & 3.06 & 0.28 & 16.09 & 17.18 & 10.80 & 22.40 \\  & CS6 & 62 & 17.68 & 2.89 & 0.37 & 16.95 & 18.42 & 12.30 & 22.40 \\ \hline \multirow{4}{*}{Males} & CS1 & 45 & 9.53 & 1.41 & 0.21 & 9.11 & 9.96 & 7.60 & 13.20 \\  & CS2 & 48 & 10.63 & 1.61 & 0.23 & 10.17 & 11.10 & 7.50 & 13.80 \\  & CS3 & 30 & 11.14 & 1.62 & 0.29 & 10.54 & 11.75 & 6.90 & 16.00 \\  & CS4 & 33 & 14.19 & 2.52 & 0.44 & 13.30 & 15.09 & 9.20 & 20.70 \\  & CS5 & 41 & 17.06 & 2.83 & 0.44 & 16.17 & 17.95 & 11.90 & 22.10 \\  & CS6 & 20 & 18.19 & 2.49 & 0.56 & 17.03 & 19.35 & 13.30 & 22.30 \\ \hline \multirow{4}{*}{Females} & CS1 & 33 & 8.78 & 1.17 & 0.20 & 8.36 & 9.19 & 6.40 & 11.20 \\  & CS2 & 28 & 9.83 & 1.51 & 0.29 & 9.24 & 10.42 & 6.40 & 12.70 \\ \cline{1-1}  & CS3 & 23 & 10.74 & 1.08 & 0.23 & 10.28 & 11.21 & 8.80 & 13.20 \\ \cline{1-1}  & CS4 & 49 & 13.22 & 3.09 & 0.44 & 12.34 & 14.11 & 7.90 & 20.90 \\ \cline{1-1}  & CS5 & 82 & 16.42 & 3.16 & 0.35 & 15.72 & 17.11 & 10.80 & 22.40 \\ \cline{1-1}  & CS6 & 42 & 17.44 & 3.06 & 0.47 & 16.49 & 18.40 & 12.30 & 22.40 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Sample distribution of chronological age among CS1–CS6. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**CVM** & \{**CS 1\}-** & \{**CS 2\}-** & \{**CS 3\}-** & \{**CS 4\}-** & \{**CS 5\}-** & \{**CS 6\}-** \\
**Stage** & **9.2128** & **10.338** & **10.970** & **13.612** & **16.631** & **17.684** \\ \hline CS 1 & & 0.004 & 0.000 & 0.000 & 0.000 & 0.000 \\ CS 2 & 0.004 & & 0.146 & 0.000 & 0.000 & 0.000 \\ CS 3 & 0.000 & 0.146 & & 0.000 & 0.000 & 0.000 \\ CS 4 & 0.000 & 0.000 & 0.000 & & 0.000 & 0.000 \\ CS 5 & 0.000 & 0.000 & 0 The following regression equation for age prediction was calculated:  
\[\text{Predicted age}=6.928794-\text{Sex}\times 0.638936+\text{CS}\times 1.891935 \tag{1}\]  
The accuracy of age prediction was rather high with only 27 out of the 474 subjects (6%) appearing as outliers using the \(\pm\)25D approach. The majority of the 27 outliers (22/27, i.e., 81%) were \(\geq\)20 years old. The residuals were mostly positive, indicating a tendency to underestimate chronological age in the subjects.