Independent identically distributed When we are talking about the least squares assumptions, one of the assumpions is that (X,Y) are i.i.d. What bothers me is that if we take an example population and research distributions of age and salaries and lets imagine that distribution of salaries (Y) in the population looks somewhat like chi squared distribution with df=10 and distribution of age (X) resembles exponential distribution. So how can we have an i.i.d. variable when sampling?
The "identically - independently distributed" assumption (or any restriction thereof) on a sample, refers to the relation between random variables in different observations, not between the variables forming the same observation.  

So in a multidimensional sample "of size $n$"

$$\{(y_1, x_{11}, x_{21}),(y_2, x_{12}, x_{22}),...,(y_n, x_{1n}, x_{2n})\}$$

the assumption applies between the variables of observation $i$ and all variables in any other observation. So $y_1$ is assumed independent from, say $y_2$, but it is also assumed indepedent from $x_{12}$. Also $x_{11}$ is assumed independent from $x_{12}$ but also from $x_{22}$, etc.

Certainly, $y_i$ is assumed stochastically dependent with $x_{1i}, x_{2i}$, while also $x_{1i}$ and $x_{2i}$ can be stochastically dependent for the same $i$, i.e. inside each observation.