Login Quote Rating MatchMaker August 27, 2008 03:51 PM (PDT)





Find your Mokey soulmate...

  chris ross charlie heather ryan deza
chris μ = 0.896 μ = 0.911 μ = 0.836 μ = 0.85 μ = 1.107
ross μ = 0.896 μ = 0.9 μ = 0.894 μ = 0.676 μ = 0.996
charlie μ = 0.911 μ = 0.9 μ = 0.805 μ = 0.74 μ = 1.178
heather μ = 0.836 μ = 0.894 μ = 0.805 μ = 0.86 μ = 1.115
ryan μ = 0.85 μ = 0.676 μ = 0.74 μ = 0.86 μ = 0.987
deza μ = 1.107 μ = 0.996 μ = 1.178 μ = 1.115 μ = 0.987

What the hell do these numbers mean?

In plain english, the smaller the value of μ, the more similar one mokey is to another in terms of quote ratings. μ is a mean (read: average) of the differences between quote ratings. As an example, let's say you want to see how similarly Ryan and Ross rate quotes. You would simply look at the ryan row and scan across it until it intersects with the ross column. You would then read μ in the cell and find that it is 0.676. To help understand the significance of this number, you should understand that quote ratings are stored as numbers. So if you rated a quote as 'Excellent', this is stored as the number 4. If you rated it as 'Good', the number 3 is used. 'So-So' is a 2, and so on. Going back to the Ross/Ryan example, if we see that Ryan rated a particular quote as a 3, then Ross's rating will be, on average, 0.676 away from 3.

If you hold your mouse over one of the cells, a little Title box will pop up with some additional numbers. In this box, you will find values for n and σ. n is simply the number of quotes in common that the two mokeys have rated. As for σ, one could say that the smaller this value, the more accurate is the mean. &sigma is the standard deviation of the mean. If σ were 0, then Ryan and Ross's ratings will always be exactly 0.676 away from each other, rather than being an average of 0.676 away. So if you knew what Ryan rated a quote, you could know with some level of certainty that Ross's quote was 0.676 away from Ryan's rating, give or take a standard deviation, 0.747. This is a somewhat high-level view of the standard deviation. You could probably look at Chapter One of any statistics book for a more in-depth definition.