compiled in Table 3.

When we did our least-square fitting of quantities extracted at a particular $\beta$, we noticed that the $Q$-value can also be used as a very sensitive measure to obtain an estimate of the critical temperature, whose error is comparable to the one obtained from the fits to the $U_L$ crossing points. Obviously if one extracts data at temperatures away from $\beta_c$ the subleading corrections to the scaling laws become more important and worsen the fit substantially, especially if large lattices are used. In Fig. 8 we plot the quality factor $Q$ obtained from log-log linear least-square fits of the quantities $\frac {dU_L} {d\beta} (\beta), \langle m \rangle (\beta), \chi (\beta)$, and $\chi^{\rm c} (\beta)$ vs the lattice size $L$, extracted at different inverse temperatures. The most striking results come from the $Q(\beta)$-curves of $\langle m \rangle$ and $\chi$, that seem to have the most severe $\beta$-dependence. Their peak locations are exactly at $\beta = 0.6930$ with an estimated error of $\pm 0.0001$, whereas the $Q(\beta)$-curves of $\frac {dU_L}{d\beta} (\beta)$ and $\chi^{\rm c}(\beta)$ do not peak so sharply at some $\beta$ and are asymmetric with respect to their peak location, falling off very slowly on the high-temperature 