cosmological models: $(\Omega_{M},   
\Omega_{\Lambda})=(1.0,0.0)$ and $(0.3,0.7)$. We will call these cosmological models Einstein-de Sitter (hereafter EdS) and $\Lambda$, respectively.

To compute $\xi(r)$ we have used the minimum variance estimator suggested by @ls93: $$\xi(r)=\frac{QQ(r)-2QR(r)+RR(r)}{RR(r)},$$ where $QQ$, $QR$ and $RR$ are the number of QSO-QSO, QSO-random and random-random pairs with a separation $r\pm\Delta r$, respectively. Here $r$ is the comoving distance of two QSOs in the redshift space. We compute the TPCF in bins of $\Delta r=5 h^{-1}$ Mpc, where $h$ is the Hubble constant, in units of 100 km s$^{-1}$ Mpc$^{-1}$. The adopted values for the Hubble constant are $h=0.5$ for the EdS model, and $h=0.65$ for $\Lambda$. We generate 100 random samples and we use the mean values of $QR(r)$ and $RR(r)$ for the estimator.

The correct generation of the random objects is in general the most critical aspect in the clustering analysis. This problem becomes fundamental in the case of a flux-limited sample, the AERQS. The area covered by our survey is not homogeneously distributed in the sky, due to the selection criteria adopted and the variable Galactic extinction. Consequently the “true” apparent magnitude limit of our survey is variable. Fig. \[areatot\] shows the effective area covered by the 