\begin{table}%[t]%t6
\caption{Numerical values for the example of Sect.~\ref{section_two_examples}
(see also Fig.~\ref{plot_pds}). The upper table lists the input line-strength
indices employed in the computation of the bivariate polynomial transformations
in Figs.~\ref{plot_pds}a, c around the model predictions
for a SSP of 12~Gyr with solar metallicity (boldfaced). The lower table 
displays the different parameters involved in the calculation of the previous
transformations, and the factors leading to the {\it suitability index\/} in
Eq.~(\ref{equation_kappa}).}
\label{table_numbers_from_example}
%\centering
\par
\begin{tabular}{lr|ccc} \hline\hline
\multicolumn{5}{c}{Line-strength indices from BC01}    \\ \hline
         &            & \multicolumn{3}{c}{[Fe/H]}     \\
         &            & $-0.330$ &  $0.093$ &  $0.059$ \\ \hline
         &      8 Gyr & 0.749313 & 0.888150 & 1.062612 \\
$\tilde{\rm D}_{4000}$ &
               12 Gyr & 0.819419 &\bf 0.966693 & 1.135206 \\
         &     20 Gyr & 0.887191 & 1.056064 & 1.197137 \\ \hline
         &      8 Gyr & 0.077433 & 0.069191 & 0.060212 \\
H$\beta$ &     12 Gyr & 0.068105 &\bf 0.060252 & 0.052139 \\
         &     20 Gyr & 0.060212 & 0.051545 & 0.044401 \\ \hline
         &      8 Gyr & 0.039794 & 0.071434 & 0.118949 \\
Fe4668   &     12 Gyr & 0.043234 &\bf 0.076271 & 0.129336 \\
         &     20 Gyr & 0.045004 & 0.081293 & 0.142160 \\ \hline
\multicolumn{5}{c}{ }                                  \\
\end{tabular}
\begin{tabular}{lrrc} \hline
         & \multicolumn{2}{c}{diagram}         &                Eq. \\ 
parameter& H$\beta$--Fe4668 & $\tilde{\rm D}_{4000}$--Fe4668  &reference \\ 
\hline
$p_{10}$ & $-0.430095$      & $-0.430095$      &(\ref{matrixA_bivariate})\\
$p_{11}$ & $ 0.051432$      & $ 0.051432$      &(\ref{matrixA_bivariate})\\
$p_{20}$ & $ 0.040343$      & $ 0.040343$      &(\ref{matrixA_bivariate})\\
$p_{01}$ & $ 0.323917$      & $ 0.323917$      &(\ref{matrixA_bivariate})\\
$p_{02}$ & $-0.014719$      & $-0.014719$      &(\ref{matrixA_bivariate})\\
$q_{10}$ & $-0.058764$      & $ 0.488931$      &(\ref{matrixA_bivariate})\\
$q_{11}$ & $ 0.003947$      & $-0.013260$      &(\ref{matrixA_bivariate})\\
$q_{20}$ & $ 0.002449$      & $-0.013883$      &(\ref{matrixA_bivariate})\\
$q_{01}$ & $-0.717729$      & $ 4.921280$      &(\ref{matrixA_bivariate})\\
$q_{02}$ & $ 0.033402$      & $-0.225252$      &(\ref{matrixA_bivariate})\\
log[age] & $10.079182$      & $10.079182$      &(\ref{matrixA_bivariate})\\
log[Z]   & $ 0.093200$      & $ 0.093200$      &(\ref{matrixA_bivariate})\\
\hline
$a_{11}$ & $ 0.095814$      & $ 0.095814$      & 
                      (\ref{eq_linear_approx}), (\ref{matrixA_bivariate})\\
$a_{12}$ & $ 0.031999$      & $ 0.031999$      &
                      (\ref{eq_linear_approx}), (\ref{matrixA_bivariate})\\
$a_{21}$ & $-0.018528$      & $ 0.352696$      &
                      (\ref{eq_linear_approx}), (\ref{matrixA_bivariate})\\
$a_{22}$ & $-0.044036$      & $ 0.379328$      &
                      (\ref{eq_linear_approx}), (\ref{matrixA_bivariate})\\
\hline
$c(m_1)$ & 0.2757           & 0.1086           &(\ref{equation_kappa})   \\
$c(m_2)$ & 0.2235           & 0.2235           &(\ref{equation_kappa})   \\
$\kappa$ & 16.995           & 0.9685           &(\ref{equation_kappa})   \\
\hline
\end{tabular}
\end{table}