\[sec2-2\]
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Now we turn our attention to the application of the general method explained above to the analysis of the data in galaxy redshift surveys. The primary data set in redshift surveys is the galaxy distribution in redshift space. In order to directly deal with the galaxy distribution, first we pixelize the redshift space, and then build the data vector ${{\mbox{\boldmath $d$}}}$ from galaxy counts $d_i$ in cells of the volume $V_i$. The expected counts $N_i = \langle d_i \rangle$ are given by an integral of the selection function $n({{\mbox{\boldmath $s$}}})$, i.e., expected number density field in a survey: $$N_i =
   \int d^3s K_i({{\mbox{\boldmath $s$}}}) n({{\mbox{\boldmath $s$}}}),
\label{eq2-7}$$ where $K_i({{\mbox{\boldmath $s$}}})$ is the kernel function of a cell $i$, which is centered at ${{\mbox{\boldmath $s$}}}_i$, and normalized by $\int d^3s K_i({{\mbox{\boldmath $s$}}}) =
V_i$. For example, the top-hat kernel of smoothing radius $R$ is given by $K_i({{\mbox{\boldmath $s$}}}) = {{\mit \Theta}}(R-|{{\mbox{\boldmath $s$}}}-{{\mbox{\boldmath $s$}}}_i|)$ and $V_i = 4\pi
R^3/3 $, where ${{\mit \Theta}}(x)$ is the Heaviside step function and ${{\mbox{\boldmath $s$}}}_i$ is the center of the cell $i$. We require the knowledge of the selection function in a survey. The correlation matrix is given by the moments of the count-in-cells 