I would like to know if there is a solution for computing MD (mean deviation), which would allow me to compute it in one pass. i.e., w/o having to compute <x> the mean first.
MD = 1/(N-1) * Sum(i=1 to N)||(xi - <x>)|| I can do what I'm asking for for the case of SD (standard deviation) SD = sqrt( 1/(N-1) * Sum(i=1 to N)(xi - <x>)^2 ) expanding and using the fact that N * <x> = Sum(i=1 to N)(xi) SD = sqrt( 1/(N-1) * Sum(i=1 to N)(xi^2) - (1/(N-1) * Sum(i=1 to N)(xi))^2 ) but unfortunately the same trick does not work w/ the absolute value in the MD. I already consulted http://mathworld.wolfram.com/MeanDeviation.html w/o any real help. Thanks in advance, Michael . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
