A coefficient of variation (CV)

A statistical measure of the dispersion of data points in a data series around the mean. It is calculated as follows:

The coefficient of variation represents the ratio of the standard deviation to the mean, and it is a useful statistic for comparing the degree of variation from one data series to another, even if the means are drastically different from each other.

A coefficient of variation (CV) can be calculated and interpreted in two different settings: analyzing a single variable and interpreting a model.  The standard formulation of the CV, the ratio of the standard deviation to the mean, applies in the single variable setting. In the modeling setting, the CV is calculated as the ratio of the root mean squared error (RMSE) to the mean of the dependent variable. In both settings, the CV is often presented as the given ratio multiplied by 100. The CV for a single variable aims to describe the dispersion of the variable in a way that does not depend on the variable's measurement unit. The higher the CV, the greater the dispersion in the variable. The CV for a model aims to describe the model fit in terms of the relative sizes of the squared residuals and outcome values.  The lower the CV, the smaller the residuals relative to the predicted value.  This is suggestive of a good model fit. 

Advantages

The advantage of the CV is that it is unitless.  This allows CVs to be compared to each other in ways that other measures, like standard deviations or root mean squared residuals, cannot be. 

In the variable CV setting: The standard deviations of two variables, while both measure dispersion in their respective variables, cannot be compared to each other in a meaningful way to determine which variable has greater dispersion because they may vary greatly in their units and the means about which they occur. The standard deviation and mean of a variable are expressed in the same units, so taking the ratio of these two allows the units to cancel.  This ratio can then be compared to other such ratios in a meaningful way: between two variables (that meet the assumptions outlined below), the variable with the smaller CV is less dispersed than the variable with the larger CV.

In the model CV setting: Similarly, the RMSE of two models both measure the magnitude of the residuals, but they cannot be compared to each other in a meaningful way to determine which model provides better predictions of an outcome. The model RMSE and mean of the predicted variable are expressed in the same units, so taking the ratio of these two allows the units to cancel.  This ratio can then be compared to other such ratios in a meaningful way: between two models (where the outcome variable meets the assumptions outlined below), the model with the smaller CV has predicted values that are closer to the actual values.  It is interesting to note the differences between a model's CV and R-squared values.  Both are unitless measures that are indicative of model fit, but they define model fit in two different ways: CV evaluates the relative closeness of the predictions to the actual values while R-squared evaluates how much of the variability in the actual values is explained by the model.