The redshift-space two-point correlation functions of galaxies and groups in the nearby optical galaxy sample

We use the two-point correlation function in redshift space, xi (s), to study the clustering of the galaxies and groups of the Nearby Optical Galaxy (NOG) sample, which is a nearly all-sky (\b\ > 20 degrees), complete, magnitude-limited sample of similar to 7000 bright and nearby optical galaxies (cz less than or equal to 6000 km s(-1)). The correlation function of galaxies is well described by a power law, xi (s) = (s/s(0))(-gamma) with slope gamma similar to 1.5 and s(0) similar to 6.4 h(-1) Mpc (on scales 2.7-12 h(-1) Mpc), in substantial agreement with previous results of several red-shift surveys of optical galaxies. Splitting NOG into different morphological subsamples, we confirm the existence of morphological segregation between early- and late-type galaxies (out to 20 h(-1) Mpc), and, in particular, we find a gradual decreasing of the strength of clustering from the S0 galaxies to the late-type spirals on intermediate scales (around 5 h(-1) Mpc). The relative bias factor between early- and late-type galaxies appears to be substantially constant with scale. Furthermore, luminous galaxies turn out to be more clustered than dim galaxies. The luminosity segregation, which is significant for both early- and late-type objects, starts to become appreciable only for galaxies brighter than M(B)similar to -19.5 + 5 log h (similar to0.6L*) and is independent of scale. The NOG groups identified with the hierarchical and percolation algorithms show similar clustering properties, with a degree of clustering that is intermediate between galaxies and clusters. The group correlation functions are characterized by ranging from similar to8 h(-1) Mpc (for groups with at least three members) to similar to 10 h(-1) Mpc (for groups with at least five members). The degree of group clustering depends on the physical properties of groups. Specifically, groups with greater velocity dispersions, sizes, and masses tend to be more clustered than those with lower values of these quantities.