I recently had to teach the formulae for calculating the number of combinations and permutations, when selecting \(r\) items out of a pool of \(n\). In case you're ever in the same boat, here's a refresher. There are four scenarios:
Combinations (order doesn't matter): 
Permutations (order does matter): 

Selected items are replaced:  $$ \frac{\left(n + r  1\right)!}{ r!\left(n  1\right)!} $$  $$ n^r $$ 
Selected items are not replaced:  $$ \frac{n!}{r!\left(nr\right)!} $$  $$ \frac{n!}{\left(nr\right)!} $$ 
The exclamation mark represents the factorial:
For example:
You can invoke it in Python like this:
$ python
>>> import math
>>> math.factorial(5)
120
or perhaps, if you're going to use it a lot:
$ python
>>> from math import factorial as fac
>>> fac(4)
24
In Julia:
$ julia
julia> factorial(7)
5040
One last thing to know about the factorial function, is that \(0! = 1\).
To get the formulae right, there are two choices to make. First, we have to decide whether or not the order that items are selected in matters to us. The various different arrangements of some group of items are called permutations when the order matters; e.g. if the arrangement \((1, 2, 3)\) is considered different from \((2, 3, 1)\). If the order doesn't matter  e.g. if the arrangements \((a, b, c)\), \((b, c, a)\), \((b, a, c)\), etc., are considered to be the same  then the arrangements are referred to as combinations. This depends on the application.
The second thing we have to consider is whether or not each item that gets selected is put back into the pool before the next selection is made. For example, in a lottery, when a ball is selected, it can't get selected again  this scenario is selection without replacement. For a group of kids playing pass the parcel, as far as they're concerned the same individual can get selected any number of times  you stay in the game even after you've been selected and you hope you might get chosen again. So this is selection with replacement. (NB in practice that's not a very sensible way to play pass the parcel if you want an easy time, so adults running the game are more likely to see it as selection without replacement...)
This leads to our four scenarios. Now let's work out the equations.
You have \( n\) distinct items and you have to select \( r\) of them. The order doesn't matter, and the items aren't replaced in the pool once selected. There are \( n\) possibilities for the first item, but because there is no replacement, there are only \(n1\) possibilities for the second item, then only \( n2\) possibilities for the third, etc. Since, to get the total number, we're going to multiply these together, we start to see that the formula for the total number of combinations, say \( M\), is going to look something like:
If we continued this series of terms right to the end, down to 1, then that would be the same as taking the factorial of \( n\). However, the number of terms we need to multiply together is the same as the number of selections in whatever game we're playing. We need to remember we're only selecting \( r\) items, so there are going to be only \( r\) terms:
So just taking the factorial of \( n\) will only give the right answer if we want to select all the items eventually, i.e. if \( r = n\). In general we need to truncate the series after \( r\) terms. How do we do this? The answer is going to be \( n!/\left(nr\right)!\), and this will be easiest to see using an example. Let \( n = 6\) and \( r = 3\). We can write the answer out the long way:
and compare this with the formula for \( n!\):
Clearly:
which of course is the same as
After working out another couple of simple examples like this, you will see that the denominator always needs to be \( \left(nr\right)!\), to cancel out the last \( \left(nr\right)!\) terms of the factorial of \( n\), leaving just the first \( r\) terms that we need. Hence:
The difference here from the last example is that each time we make a selection, we replace the item afterwards. Every selection is made from the same original pool, rather than from a gradually decreasing pool. So in this case the formula is going to look something like:
The logic above about there only being \( r\) terms is exactly the same here, so we end up with:
Simple!
Now we take the first scenario  permutations without replacement  and adapt it to the scenario where we don't distinguish between different orderings of the selected items. Clearly, we're going to have reduce the number  there will be fewer combinations than there are permutations. Let's assume \( n = 5\) and \( r = 3\). Imagine the items are numbered balls like in a lottery, and imagine the selected balls are \((1, 3, 4)\). If these were your numbers, you'd win no matter what order they came out in, so the permutations of the \( r\) selected items  \((1, 3, 4)\), \((1, 4, 3)\), \((3, 1, 4)\), \((3, 4, 1)\), \((4, 1, 3)\) and \((4, 3, 1)\)  are the same, and should be considered just one distinct arrangement. Of course, we already know that there are \( r!\) permutations of \( r\) selected items, so if we care about the order of the selected items, then there are going to be \( r!\) more possibilities than if we don't. So, all we have to do to get the number of combinations is reduce the number of permutations by a factor of \( r!\). Hence:
This scenario is slightly more complicated. Without replacement, to
get from the number of permutations to the number of combinations, we
just reduced by a factor of \( r!\). You might
therefore think that the answer here is going to be the number of
permutations with replacement, similarly reduced by a factor of
\( r!\); but it isn't. Let's take a simple example:
a pool of three numbers, and selection of two items. The permutations
without replacement are:
(1,2)
(2,1)
(1,3)
(3,1)
(2,3)
(3,2)
and the additional ones if replacement is allowed are:
(1,1)
(2,2)
(3,3)
The permutations highlighted in red are the ones you'd get rid of if you were interested only in combinations. Notice that there aren't any red ones in the second batch  the "extra" possibilities that arise when replacement is allowed are the same for permutations as for combinations (in this special case of selecting two items). When you generalise this idea to larger numbers, you do end up with some red items in the second batch, but not as high a proportion as in the first batch. So we have to reduce the second batch by some different factor, not \( r!\).
This shows us why reducing the total number of permutations by \( r!\) doesn't work with replacement, but it doesn't actually help us do the calculation, because we can't easily work out how big the second batch is, nor what factor to reduce it by. However, what if we pretend that, whenever we get repetition due to the items being replaced, an item being selected a second time is actually a new item? So instead of \((1,1)\) we could perhaps write \((1,1′)\), with the prime on the second \(1\) indicating our pretense that it's a different item. If we had \((1,1,2,1)\) for an \( r = 4\) trial, we'd call it \((1,1′,2,1′′)\), indicating that the three instances where \(1\) was selected should all be treated as if they were different items. We're now back in the realm of permutations without replacement, for which the formula is \( m! /r!\left(mr\right)!\), where now we're using \( m\), for the "expanded" number of items to choose from as a consequence of replacement being allowed, rather than \( n\), the true number of distinct items. Now we just need to work out \( m\). It turns out that:
That is, selecting with replacement from a pool of \( n\) is like selecting without replacement from a pool of \( n + r  1\). There are \( r  1\) extra possibilities to select from, because once we have selected the first item, the remaining \( r  1\) items could be (in reality) the same item every time, but we are pretending that they are different. Hence, substituting for \( m\):
This gives us the number of permutations, which we can now reduce by \( r!\) to get the number of combinations. Our final formula is therefore: