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00
The extra data doesn't seem to make much difference:
R> karma <- read.table("http://people.mokk.bme.hu/~daniel/rationality_quotes_2012/scores")
R> karma <- sort(karma$V2)
R> summary(karma)
Min. 1st Qu. Median Mean 3rd Qu. Max.
-8.0 4.0 8.0 10.7 15.0 105.0
...
Nonlinear regression model
model: y ~ exp(a + b * x)
data: temp
a b
-0.01088 0.00134
residual sum-of-squares: 22772
Number of iterations to convergence: 7
Achieved convergence tolerance: 3.59e-06
It is roughly exponential in the range between 3 and 60 karma.
Eyeballing it, looks like the previous fit crosses around 40.
R> karma <- karma[karma<40]
...
Nonlinear regression model
model: y ~ exp(a + b * x)
data: temp
a b
-0.01088 0.00134
residual sum-of-squares: 22772
Number of iterations to convergence: 7
Achieved convergence tolerance: 3.59e-06
The fit looks much better:
00
I am afraid I don't understand your methodology. How is a rank versus value function supposed to look like for an exponentially distributed sample?
I finished creating the 2012 edition of the Best of Rationality Quotes collection. (Here is last year's.)
Best of Rationality Quotes 2012 (500kB page, 434 quotes)
and Best of Rationality Quotes 2009-2012 (1200kB page, 1140 quotes)
The page was built by a short script (source code here) from all the LW Rationality Quotes threads so far. (We had such a thread each month since April 2009.) The script collects all comments with karma score 10 or more, and sorts them by score. Replies are not collected, only top-level comments.
As is now usual, I provide various statistics and top-lists based on the data. (Source code for these is also at the above link, see the README.) I added these as comments to the post: