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If you must insist on treating these three points as independent - which I can see little justification for doing, but maybe that's just me - you do a (casualties, indictments, convictions) plot and run a linear fit against all three points in a given series at the same time. This way you get some more meaningful (implicit) assumptions about the way the uncertainties look.
- Jake Friends come and go. Enemies accumulate.
Or, rather, three fits.
One for all six rows in Vladimir's table - that's the "null hypothesis".
One for the 3 Serb rows and the 3 non-serb rows. That's more or less equivalent to was was done in the diary.
Or you could do a test on whether the 3 Serb and 3 non-Serb points fall above or below the "null hypothesis" regression line. The trouble is, with only 6 points you probably can't say anything with 95% confidence. Most economists teach a theoretical framework that has been shown to be fundamentally useless. -- James K. Galbraith
I would be opposed to fitting the slope as well as the intercept in your model, because we already only have three points for every fit parameter - and you fit a number of parameters comparable to your number of data points at the peril of talking nonsense...
This actually makes it look worse for Vladimir's hypothesis. The fit is this:
Coefficients: (Intercept) -6.095 Response: log(indicted) - log(casualties) Df Sum Sq Mean Sq F value Pr(>F) Residuals 5 1.24772 0.24954
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