Introduction:
The Coefficient of Variation (CV) is a statistical measure used to measure the relative variability or dispersion of a data set. It is typically expressed as a percentage and is calculated by dividing the standard deviation of the data by the mean of the data and then multiplying by 100. The CV is commonly used to compare the variability of different data sets, especially when the means are different.

Calculation of CV:
To calculate the CV, we need to know the standard deviation (SD) and the mean of the data set. Given that the variance is 12 and the mean is 40, we can use these values to calculate the CV.

Step 1: Calculate the standard deviation (SD):
The standard deviation is the square root of the variance. In this case, the variance is 12, so the standard deviation can be calculated as follows:

SD = sqrt(variance) = sqrt(12) = 3.4641

Step 2: Calculate the CV:
The CV is calculated by dividing the standard deviation by the mean and multiplying by 100. Using the given values, the CV can be calculated as follows:

CV = (SD / mean) * 100 = (3.4641 / 40) * 100 ≈ 8.66%

So, the coefficient of variation (CV) for the given data set is approximately 8.66%.

Interpretation:
The CV provides a measure of relative variability, allowing us to compare the dispersion of different data sets. In this case, the CV of approximately 8.66% indicates that the data set has a relatively low variability compared to the mean. This means that the values in the data set are relatively close to the mean, with less dispersion.

Conclusion:
The Coefficient of Variation (CV) is a useful statistical measure to assess the relative variability of a data set. It is calculated by dividing the standard deviation by the mean and multiplying by 100. In this case, the CV for the given data set is approximately 8.66%, indicating low variability relative to the mean value of 40.