Variance or mean squared deviation is a measure of the variability of a random variable, indicating how much the studied quantity varies. A higher Variance indicates greater differences between the values in the observed data set.
Variance Formula
$$\sigma^{2} = E(X - E(X))^{2}$$
where,
E(X)— the expected value (mean) of the random variable X.
Variance of a finite data set can be expressed by the formula:
$$\sigma^{2} = \frac{1}{n}\sum_{i=1}^{n}(x_{i} - \bar{x})^{2}$$
where,
x̄— the mean of random variables xi.
Sample Variance can be expressed by the formula (Bessel's correction):
$$\sigma^{2} = \frac{1}{n-1}\sum_{i=1}^{n}(x_{i} - \bar{x})^{2}$$
Variance may also be denoted by D(X). Often, especially in English literature, Variance is denoted by V(X) or var(X).
See also:
Variance FAQ
What is variance?
Variance measures how far data values spread from the mean.
What is the difference between population variance and sample variance?
Sample variance uses Bessel's correction and divides by n minus 1 instead of n.
