- #1
mattkunq
- 14
- 0
Can someone please help me with this. I read on wikipedia that the coefficient of Variance can be used when the measurements are of ratio scales but cannot be used on interval scales.
I know it is useful to use it to compare samples with completely units like the variance in your mass measurement variation to your volume measurements.
The part i am confused about is whether the follow is alright.
So let's say I have 2, 6 sided dice that is rigged to fall on 6 more. So i roll it a lot and i get a distribution of the sumed values, the sample mean will be let's say x1 with sample variance S1.
now if i were to relabeled those dice so that it falls one 1 more than the new sample mean would be x2 but the sample variance would stay the same, ie the width of the normal distrubition does not change.
So based on this i would think taking a Coefficient of variance which is defined as the sample variance divided by the sample mean would be wrong representation of the variation of the dice to compare with other non related experiments ie (variance in drawing from two decks of cards )
the Coefficient of Variance of X1 will not equal to that of X2 making a variance comparison to other non related experiments kinda meaningless as the coefficient of variance depend so much on the mean.
I know it is useful to use it to compare samples with completely units like the variance in your mass measurement variation to your volume measurements.
The part i am confused about is whether the follow is alright.
So let's say I have 2, 6 sided dice that is rigged to fall on 6 more. So i roll it a lot and i get a distribution of the sumed values, the sample mean will be let's say x1 with sample variance S1.
now if i were to relabeled those dice so that it falls one 1 more than the new sample mean would be x2 but the sample variance would stay the same, ie the width of the normal distrubition does not change.
So based on this i would think taking a Coefficient of variance which is defined as the sample variance divided by the sample mean would be wrong representation of the variation of the dice to compare with other non related experiments ie (variance in drawing from two decks of cards )
the Coefficient of Variance of X1 will not equal to that of X2 making a variance comparison to other non related experiments kinda meaningless as the coefficient of variance depend so much on the mean.