Inconsistency in return value of mean, median, and quantile on timedelta64[ns] series
If I have a Series of `timedelta64[ns]` (the result of a `diff()` on a Timestamp Series in my case), doing mean, median, and quantile operations on that series returns inconsistent values. Example:

```
In [68]: s
Out[68]: 
0   00:00:14.020705
1   00:00:14.020705
2   00:00:14.020705
3   00:00:28.041410
4   00:00:28.041410
5   00:00:42.062116
6   00:00:42.062116
7   00:00:42.062116
8   00:00:56.082821
9   00:02:20.207052
dtype: timedelta64[ns]

In [69]: s.mean()
Out[69]: 42062115618.0

In [70]: type(s.mean())
Out[70]: numpy.float64

In [71]: s.median()
Out[71]: 35051763015.0

In [72]: type(s.median())
Out[72]: float

In [73]: s.quantile(.95)
Out[73]: numpy.timedelta64(102351148003,'ns')

In [74]: type(s.quantile(.95))
Out[74]: numpy.timedelta64
```

As you can see, `mean` and `median` return a float (although the printed Out is a little different), but `quantile` returns a single `timedelta64[ns]`.

This is with versions pandas==0.12.0 and numpy==1.7.1
