If algorithm runs $\theta(n)$ in time T, doubling input size has what effect on time T?
There's no clear relationship, except that bigger inputs will tend to take longer.  What we call the "running time" in complexity theory is the number of basic operations but we just choose some set of basic operations and assume that they all have the same cost.  That's fine for asymptotic analysis but it doesn't correspond to the actual running time, as measured by a clock.

For example, imagine your algorithm consists of an initialization phase that takes one second, regardless of the input, plus a quadratic time loop that takes $n^2$&nbsp;milliseconds to process $n$&nbsp;items.  Complexity theory just says that your algorithm has running time $\Theta(n^2)$, so doubling the size of the input multiplies the running time by four.  Asymptotically, this is true but, if you double the input size from $10$ to $20$, the running time only changes from $1.1\,\mathrm{s}$ to&nbsp;$1.4\,\mathrm{s}$, which is nothing like a factor of four (it's less less than $30\%$).

Another problem is that complexity theory doesn't model the sheer... well, complexity of modern computers.  A simple example: suppose your quadratic algorithm runs in half a second on an input of length&nbsp;$\ell$.  Great, it's going to run in just over half a second on a similar input of length $\ell+1$, right? Maybe.  Or maybe it takes ten seconds because your data doesn't fit in the cache any more.