# How to Measure Variability in a Data Set

In this lesson, we discuss three measures that are used to quantify the amount of variation in a data set - the range, the variance, and the standard deviation.

For example, consider a population of elements {5, 5 ,5, 5}. Here, each of the values in the data set are equal, so there is no variation. The set {3, 5, 5, 7}, on the other hand, has some variation since some some elements in the data set have different values.

## Notation

The following notation is helpful, when we talk about variability.

- σ
^{2}: The variance of the population. - σ: The standard deviation of the population.
- s
^{2}: The variance of the sample. - s: The standard deviation of the sample.
- μ: The population mean.
- x: The sample mean.
- N: Number of observations in the population.
- n: Number of observations in the sample.
- P: The proportion of elements in the population that has a particular attribute.
- p: The proportion of elements in the sample that has a particular attribute.
- Q: The proportion of elements in the population that does not have a specified attribute. Note that Q = 1 - P.
- q: The proportion of elements in the sample that does not have a specified attribute. Note that q = 1 - p.

Note that capital letters refer to population parameters, and lower-case letters refer to samplestatistics.

## The Range

The

**range**is the simplest measure of variation. It is difference between the biggest and smallest random variable.
Range = Maximum value - Minimum value

Therefore, the range of the four random variables (3, 5, 5, 7} would be 7 minus 3 or 4.

## Variance of the Mean

It is important to distinguish between the variance of a population mean and the variance of a sample mean. They have different notation, and they are computed differently. The variance of a population mean is denoted by σ

^{2}; and the variance of a sample mean, by*s*^{2}.
The

**variance**of a population mean is the average squared deviation from the population mean, as defined by the following formula:
σ

^{2}= Σ ( X_{i}- μ )^{2}/ N
where σ

^{2}is the population variance, μ is the population mean, X_{i}is the*i*th element from the population, and N is the number of elements in the population.
The variance of a sample mean is defined by slightly different formula:

*s*

^{2}= Σ ( x

_{i}- x )

^{2}/ ( n - 1 )

where

*s*^{2}is the sample variance, x is the sample mean, x_{i}is the*i*th element from the sample, and n is the number of elements in the sample. If you are working with a simple random sample, the sample variance can be considered an unbiased estimate of the true population variance. Therefore, if you want to estimate the unknown population variance, based on known data from a simple random sample, use this formula.**Example 1**

A population consists of four observations: {1, 3, 5, 7}. What is the variance?

*Solution:*First, we need to compute the population mean.

μ = ( 1 + 3 + 5 + 7 ) / 4 = 4

Then we plug all of the known values in to formula for the variance of a population, as shown below:

σ

^{2}= Σ ( X_{i}- μ )^{2}/ N
σ

^{2}= [ ( 1 - 4 )^{2}+ ( 3 - 4 )^{2}+ ( 5 - 4 )^{2}+ ( 7 - 4 )^{2}] / 4
σ

^{2}= [ ( -3 )^{2}+ ( -1 )^{2}+ ( 1 )^{2}+ ( 3 )^{2}] / 4
σ

^{2}= [ 9 + 1 + 1 + 9 ] / 4 = 20 / 4 = 5**Example 2**

A simple random sample consists of four observations: {1, 3, 5, 7}. What is the best estimate of the population variance?

*Solution:*This problem is handled exactly like the previous problem, except that we use the formula for calculating sample variance, rather than the formula for calculating population variance.

*s*

^{2}= Σ ( x

_{i}- x )

^{2}/ ( n - 1 )

*s*

^{2}= [ ( 1 - 4 )

^{2}+ ( 3 - 4 )

^{2}+ ( 5 - 4 )

^{2}+ ( 7 - 4 )

^{2}] / ( 4 - 1 )

*s*

^{2}= [ ( -3 )

^{2}+ ( -1 )

^{2}+ ( 1 )

^{2}+ ( 3 )

^{2}] / 3

*s*

^{2}= [ 9 + 1 + 1 + 9 ] / 3 = 20 / 3 = 6.667

## Standard Deviation of the Mean

The

**standard deviation**is the square root of the variance. It is important to distinguish between the standard deviation of a population and the standard deviation of a sample. They have different notation, and they are computed differently. The standard deviation of a population is denoted by σ; and the standard deviation of a sample, by*s*.
The standard deviation of a population mean is defined by the following formula:

where σ is the population standard deviation, μ is the population mean, X

_{i}is the*i*th element from the population, and N is the number of elements in the population.
The standard deviation of a sample mean is defined by slightly different formula:

where

*s*is the sample standard deviation, x is the sample mean, x_{i}is the*i*th element from the sample, and n is the number of elements in the sample.## Variance of a Proportion

The variance formulas introduced in the previous section can be used with confidence for any random variable - even proportions. However, for proportions the formulas can be expressed in a form that is easier to compute.

When all of the elements of the population are known, the variance of a population proportion is defined by the following formula:

σ

^{2}= PQ
where P is the population proportion and Q equals 1 - P.

When the population proportion is estimated from sample data, the variance of the sample proportion is estimated by slightly different formula:

*s*

^{2}= pq

where p is the sample estimate of the true proportion, and q is equal to 1 - p. Given a simple random sample, this sample variance can be considered an unbiased estimate of the true population variance. Therefore, if you need to estimate the unknown population variance, based on known data from a simple random sample, this is the formula to use.

## Standard Deviation of a Proportion

The standard deviation of a proportion is the square root of the variance of the proportion. Thus, the standard deviation of a population proportion is:

where P is the population proportion and Q equals 1 - P.

And, using sample data, the standard deviation of a population proportion can be estimated from the following formula:

where p is the sample proportion and q equals 1 - p.

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