# Variance and Standard Deviation of a Random Variable

We have already looked at Variance and Standard deviation as measures of
dispersion
under the section on
Averages
. We can also measure the dispersion of Random variables across
a given distribution using Variance and Standard deviation. This allows us to better
understand whatever the distribution represents.

The Variance of a random variable X is also denoted by σ;2
but when sometimes can be written as Var(X). Variance of a random variable can be defined as the expected value of the square
of the difference between the random variable and the mean.

Given that the random variable X has a mean of μ, then the variance
is expressed as: In the previous section on
Expected value
of a random variable, we saw that the method/formula for
calculating the expected value varied depending on whether the random variable was
discrete or continuous. As a consequence, we have two different methods for calculating
the variance of a random variable depending on whether the random variable is discrete
or continuous.

• For a Discrete random variable, the variance σ2 is
calculated as: • For a Continuous random variable, the variance σ2
is calculated as: In both cases f(x) is the probability density function.

The Standard Deviation σ in both cases can be found by taking
the square root of the variance.

Example 1

A software engineering company tested a new product of theirs and found that the
number of errors per 100 CDs of the new software had the following probability distribution:

x f(x)
2 0.01
3 0.25
4 0.4
5 0.3
6 0.04

Find the Variance of X

Solution

The probability distribution given is discrete and so we can find the variance from
the following: We need to find the mean μ first:   Then we find the variance:   Example 2

Find the Standard Deviation of a random variable X whose probability density function
is given by f(x) where: Solution

Since the random variable X is continuous, we use the following formula to calculate
the variance: First we find the mean μ     Then we find the variance as:     ### Simplifying the Variance formula

We have seen that variance of a random variable is given by: We can attempt to simplify this formula by expanding the quadratic in the formula
above as follows: We shall see in the next section that the expected value of a linear combination
behaves as follows: Substituting the expanded form into the variance equation:  Remember that after you’ve calculated the mean μ, the result is a constant
and the expected value of a constant is that same constant. This simplifies the formula as shown below: but which means that;  The above is a simplified formula for calculating the variance.

We can also derive the above for a discrete random variable as follows:   but since the total probability is 1 and Therefore, where by; Hence For a continuous random variable:   whereby which means that  ## Variance of an Arbitrary function of a random variable g(X)

Consider an arbitrary function g(X), we saw that the expected value of this function
is given by:

• For a discrete case • For a continuous case The variance of this functiong(X) is denoted as σg(X)
and can be found as follows: • For X is a discrete random variable • For X is a continuous random variable ## Covariance

In the section on
probability distributions
, we saw that at times we might have to deal with
more than one random variable at a time, hence the need to study Joint Probability
Distributions.

Just as we can find the Expected value of a joint pair of random variables X
and Y, we can also find the variance and this is what we refer to as the
Covariance.

The Covariance of a joint pair of random variables X and Y is denoted
by:

Cov(X,Y).

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