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\end{equation}  \noindent  where $N$ is the number of data points, $a,b$ the two parameters for a linear line and $(x_{i}, y_{i})$ is a given timing measurement at a given epoch.  We have tested the dependence of scatter on the weight used and found no difference in the scatter metrics when applying a weight of one for all measurements. Finally some additional details need to be mentioned. We only inferred new timing uncertainties for data sets with more than two measurements. For a given data set we used the published ephemeris (orbital period) to calculate the eclipse epochs. For the time stamps presented in \cite{BaileyCropper_1991} no ephemeris was stated. We therefore, used their eclipse cycles for the independent variable to calculate a best-fit line. The reference epoch in each fit was placed to be in or near the middle of the data set. Two data points were discarded in the present analysis. We removed one time stamp from \cite{Ferrario_1989} due to a too high timing uncertainty. Another time stamp was removed from the new data presented in Potter et al. (2011), namely the time stamp BJD(TDB) 2,454,857.36480850. This eclipse is duplicated as it was observed also with the much larger SALT/BVIT instrument resulting in a lower timing error. We therefore use only the SALT/BVIT measurement in the present analysis which makes use of a total of 54 timing stamps. The average or mean timing error for the 54 measurements is 5.7 seconds (the standard deviation is 6.5 seconds) with 0.33 seconds as the smallest and 26.5 seconds as the largest error. Also we have rescaled the timing measurements by subtracting the first time stamp from all the others. Rescaling introduces nothing spooky to the analysis and has the advantage to avoid dynamic range problems when carrying out the process of least-squares minimization. The total baseline of the data set spans ~27 years.  \begin{table}   \begin{tabular}{ c c c c c c c c }