In this paper we show how to apply classical probabilistic tools for partial sums
$\sum _{j=0}^{n-1}\varphi \circ \tau ^j$
generated by a skew product
$\tau $
, built over a sufficiently well-mixing base map and a random expanding dynamical system. Under certain regularity assumptions on the observable
$\varphi $
, we obtain a central limit theorem (CLT) with rates, a functional CLT, an almost sure invariance principle (ASIP), a moderate-deviations principle, several exponential concentration inequalities and Rosenthal-type moment estimates for skew products with
$\alpha $
-,
$\phi $
- or
$\psi $
-mixing base maps and expanding-on-average random fiber maps. All of the results are new even in the uniformly expanding case. The main novelty here (in contrast to [2]) is that the random maps are not independent, they do not preserve the same measure and the observable
$\varphi $
depends also on the base space. For stretched exponentially
${\alpha }$
-mixing base maps our proofs are based on multiple correlation estimates, which make the classical method of cumulants applicable. For
$\phi $
- or
$\psi $
-mixing base maps, we obtain an ASIP and maximal and concentration inequalities by establishing an
$L^\infty $
convergence of the iterates
${\mathcal K}^{\,n}$
of a certain transfer operator
${\mathcal K}$
with respect to a certain sub-
${\sigma }$
-algebra, which yields an appropriate (reverse) martingale-coboundary decomposition.