Calculating a quintile can help you zero in on interesting and informative patterns in a data set. A quintile is a group of numbers that represents 20 percent of the values that reside in a larger set. A company, for example, may calculate quintiles to discover how much its lowest selling items contribute to the company's total sales.The government, on the other hand, might calculate quintiles to discover how income is distributed between different five different age groups.

You can calculate quintiles faster by working in a spreadsheet program. Sort a column of Excel worksheet cells, for instance, by clicking any cell in the column, clicking “Home,” and then clicking “Sort & Filter.” Click “Sort Smallest to Largest” to sort the cells in ascending order.

You may find it useful to learn the ratio between sum of the values in the lowest quintile and the sum of the values in the highest quintile. Perform that calculation by dividing the last quintile's total value by the first quintile's total value.

Sort a list of at least five numbers in ascending order and place them in a column, as seen in the following example:

100 500 700 1,200 1,300 20,000 40,000 55,000 58,000 61,000

Calculate the sum of the values in your data set. The sum of the values in the above example is 237,800.

Divide the numbers into fifths by drawing lines that separate the quintiles. If you perform this task using the sample data, you’ll see the following:

100 500 -------- 700 1,200 -------- 1,300 20,000 -------- 40,000 55,000 -------- 58,000 61,000

The numbers above each line represent a quintile. In the above example, the numbers in the second quintile are 700 and 1,200. The values 58,000 and 61,000 make up the fifth quintile.

Divide the fifth quintile’s sum by the data-set sum and multiply the result by 100, as shown in the following example:

(119,000 / 237,800) * 100 = 50.04.

The result represents the percentage that the fifth quintile contributes to the data set’s total value. In this example, the fifth quintile accounts for over 50 percent of the data set’s total value.

Repeat this calculation to determine the contribution percentages of the other four percentiles.