the (\[approx\_DTT\]). For a quantitative spectrum description we use only two parameters: a power spectrum index $n$ and quadrupole magnitude $Q^2_{rms}=\langle \Delta T^2_2 \rangle$ as an amplitude.

A correlation function on the sphere $C(\beta)=\langle(\Delta T(\vec q_1)) (\Delta
T(\vec q_2))\rangle $, $\vec q_1 \vec q_2=\cos(\beta)$ may be represented in terms of spherical functions: $$C(\beta)=\frac{1}{4 \pi} \sum P_l(\cos \beta ) \sum_m
(a_l^m)^2$$

Using the spherical harmonic expansion we may calculate the correlation between two maps (for example between 31.5 GHz and 53 GHz maps): $$\rho({\rm MAP1, MAP2}) = \frac{ \sum_{l,m} a_l^m(1) a_l^m(2) }
      {\sqrt{ \sum_{l,m} (a_l^m(1))^2 \sum_{l,m} (a_l^m(2))^2 }}$$

 Spectrum Parameters Analysis.
------------------------------

A cosmological model scenario gives us a prediction of a mean CMB spectrum fluctuation. Experimenters deal with the single sample of the scenario. In addition a noise is always present in a real data. In this connection there is the problem of a signal parameters estimation. The problem was discussed by Bunn et al., 1994 and Sazhin et al., 1995. Here we will not go into the problem, but will touch upon the estimation not only the signal, but the noise parameters too.

The maximum likelihood estimation is known to be the most effective. It may be illustrated if 