## What does coefficient of variation mean?

The coefficient of variation (CV) is the ratio of the standard deviation to the mean. The higher the coefficient of variation, the greater the level of dispersion around the mean. It is generally expressed as a percentage. The lower the value of the coefficient of variation, the more precise the estimate.

## What does a coefficient of variation of 1 mean?

The standard deviation of an exponential distribution is equal to its mean, so its coefficient of variation is equal to 1. Distributions with CV 1 (such as an Erlang distribution) are considered low-variance, while those with CV > 1 (such as a hyper-exponential distribution) are considered high-variance.

## What exactly is variance?

The term variance refers to a statistical measurement of the spread between numbers in a data set. More specifically, variance measures how far each number in the set is from the mean and thus from every other number in the set.

## What is the symbol of variance?

σ²

## What is the difference between variance and standard deviation?

Key Takeaways. Standard deviation looks at how spread out a group of numbers is from the mean, by looking at the square root of the variance. The variance measures the average degree to which each point differs from the mean—the average of all data points.

## What is the variance of a sample?

Definition of Sample Variance The variance is mathematically defined as the average of the squared differences from the mean.

## Why is variance squared?

The variance of a data set is calculated by taking the arithmetic mean of the squared differences between each value and the mean value. Squaring adds more weighting to the larger differences, and in many cases this extra weighting is appropriate since points further from the mean may be more significant.