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.
$$\sigma^{2} = E(X - E(X))^{2}$$
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).