How to compare the variance of two conditions with different means? I have a series of vectors, which are measurements from one sample at two time points.
First time point:
1.[5,3,2,4,4,3,6,5] (mean=4.000)
2.[5,6,3,3,4,3,4,5] (mean=4.125)
3.[6,3,4,2,5,3,5,7] (mean=4.375)
... etc.
grand mean=4.17
Second time point:
1.[1,2,1,2,1,3,4,5] (mean=2.375)
2.[2,2,3,1,1,3,3,5] (mean=2.500)
3.[1,3,1,2,2,3,5,4] (mean=2.625)
.... etc.
grand mean=2.5
I want to see if the variance for each measurement/vector is significantly different between the two time points. 
However, the second measurement has a lower mean, which can therefore drive overall variance. How do you compare the variance of two conditions when the means differ?
 A: It is also important to keep in mind that a strong assumption in the usual F test for equality of variances is that of normality -- the test is very sensitive to the assumption of normality so the resulting p-vlaues can be very distorted.  Levene suggested a simple alternative and Brown-Forsythe followed with a test statistic that aims at increasing robustness.
You can start in Wikipedia
 http://en.wikipedia.org/wiki/Brown%E2%80%93Forsythe_test
A: However, the second measurement has a lower mean, which can therefore drive overall variance. 
Do you know this for sure?  If that's so, you need to figure out what the relationship is and transform the variance to a more stable scale (e.g. log).
Otherwise, try the naive approach and use a folded F test, dividing the larger sample variance by the smaller one, and find the p-value for the resulting F statistic.  This approach assumes the samples are independent, which might be reasonable if your overall sequence is stationary or the sample times are widely separated.  If not, you probably will need to fit some sort of time series model to the data (ARIMA maybe), and compare the variance of the residuals.
