To measure the

correlation between two possibly related

dichotomous events (E1 and E2), you use

Yule's Q, given by the

formula:

**((ad - bc) / (ad + bc))**
where, after running an

experiment a certain number of times...

a = the number of times E1 happened and E2 happened

b = the number of times E1 did not happen and E2 happened

c = the number of times E1 happened and E2 did not happen

d = the number of times E1 did not happen and E2 did not happen

The result will be a real number between -1 and 1.

When Q=1, there is a perfect

positive correlation between the two events--what
happens in E1 always happens in E2 and vice versa. If E1 happens, E2

**always **happens. If E2 happens, E1

**always **happens. If E1 does not happen, E2

**never **happens. If E2 does not happen, E1

**never **happens.

When Q=-1, there is a perfect

negative correlation between the two events,
and the occurance of one event invariably leads to the non-occurance of the
other (and

vice-versa). If E1 happens, E2

**never **happens. If E2 happens, E1

**never **happens. If E1 does not happen, E2

**always **happens. If E2 does not happen, E1

**always **happens.

When Q = 0, there is absolutely

no correlation between the two events--one
event happening or not happening does not influence the other event at all. Total

statistical independence.

Of course, those are just the ideal conditions. A Q value of 0.2, for
instance, would indicate that there's a relatively weak positive correlation
between E1 and E2--if E1 happens, it is more likely than not that E2 will
happen--but you probably wouldn't want to

bet the farm on it.

It's also worth noting that

Yule's Q is a

symmetric measure--it couldn't care less whether E1 occured first or second

chronologically.

Yule's Q was first put forth in 1957, by G. V. Yule and M. G. Kendall.