Intuition and uses for coefficient of variation I'm currently attending the An Introduction to Operations Management course in Coursera.org. At some point in the course, the professor started to deal with variation in the operations' time. 
The measurement he uses is the Coefficient of Variation, the ratio between the standard deviation and the mean:
$c_v = \frac{\sigma}{\mu}$ 
Why would this measurement be used? What are the advantages and disadvantages of working with CV besides working with, say, standard deviation? What is the intuition behind this measurement?
 A: Coefficient of variation is effectively a normalized or relative measure of the variation in a data set, (e.g.  a time series) in that it is a proportion (and therefore can be expressed as a percentage). Intuitively, if the mean is the expected value, then the coefficient of variation is the expected variability of a measurement, relative to the mean.
This is useful when comparing measurements across multiple heterogeneous data sets or across multiple measurements taken on the same data set - the coefficient of variation between two data sets, or calculated for two sets of measurements can be directly compared, even if the data in each are measured on very different scales,  sampling rates or resolutions.
In contrast, standard deviation is specific to the measurement/sample it is obtained from, i.e. it is an absolute rather than a relative measure of variation.
A: I think of it as a relative measure of spread or variability in the data. If you think of the statement, "The standard deviation is 2.4" it really tells you nothing without respect to the mean (and thus the unit of measure, I suppose). If the mean is equal to 104, the standard deviation of 2.4 communicates quite a different picture of spread than if the mean were 25,452 with a standard deviation of 2.4.. 
The same reason you normalize data (subtract the mean and divide by the standard deviation) to place data expressed in different units on a comparable or equal footing—so too this measure of variability is normalized—to aid in comparisons. 
