WIP/BUG: fixed ProbPlot class and tests to work with general distributions

Unsurprisingly, most of what I did to get this point was to simplify things.

~~[Here's a notebook](http://nbviewer.ipython.org/gist/phobson/9233257)~~ [New Notebook](http://nbviewer.ipython.org/gist/phobson/b3bf593a3b6bb631b921) showing how things are working so far. It shows off creating `ProbPlot` objects with three different distributions:
 1. Lognormal
 1. beta
 1. t

It also creates the object three different ways:
 1. Simple: provide data, unfrozen distribution, minimal distribution parameters (`distargs`)
 1. More specific: provide data, frozen distribution, minimal distribution parameters (`distargs`)
 1. "Automatic": provide data, unfrozen distribution, tell the class to fit the distribution

Not all the plots are perfect. But we're getting close.

 - [ ] Add option to use `ax.step` instead of `ax.plot`
 - [x] confirm that `distargs` are not needed with a frozen distribution
 - [x] look into the "binary" nature of `t` probplots when `fit=False`
 - [ ] beef up the testing and examples for the `qqlines`

[Updated notebook](http://nbviewer.ipython.org/gist/phobson/b3bf593a3b6bb631b921)
Was this in a release? If so, should/can we change the order of keyword args? Do we need to deprecate loc and scale?