[rpicard]

1) Yes, there were far fewer tickets in the data for 2009 and 2010 than 2011.

2) The fact that the normalized numbers were so small was very unintuitive to me at first too, but the important thing to realize is that in that formula, you're dividing the difference, not actual value for the given day, by the total number for the year. When I first ran those numbers I was so confused by the output. I was originally thinking that I'd normalize it by saying "X percent of the total for that year," but since I was working with the differences, and not the actual values, the numbers were too small a fraction.

Either that or I made some huge mistake in my logic...

WRT the use of standard deviations, like I said in the post, I'm not a statistician, so I wasn't really sure what the canonical way of normalizing data was. I pretty much just made one up. Thanks for pointing that out. I'll look into using standard deviation for the next one. :)

[beder]

In that case, you should divide by "expected", so you get a percentage difference for each day. (Normalizing by total for the year doesn't make sense, since imagine that there were 300 days per month instead of 30 - your numbers would be divided by 10 again, but the data you want to visualize would stay the same.)

[rpicard]

I made an update using standard deviation and Z-score charts instead of my homebrew normalization function (see "Update"): http://robert.io/posts/4.html

[rpicard]

Great point! Dividing by expected would have been a much nicer solution. I think the method I used still works though, just not as nicely. Am I wrong?

[Kaizyn]

Z-Score is your friend for this kind of data normalization.

http://en.wikipedia.org/wiki/Standard_score

[rpicard]

Thanks. It looks like I need to do a little self-study in statistics.