Six Sigma graduates should be familiar with the various statistical parameters in the Measure phase of DMAIC’s Six Sigma process. Measures of Central Tendency are used to locate the center point of the distribution. They do not show how items are distributed on either side. Lean Six Sigma practitioners must understand both relative and absolute dispersion measures to understand the spread of data. This characteristic of frequency distribution is often referred to as “Dispersion”. The term ‘Dispersion’ is used to describe the lack of uniformity in sizes or quantities among items in a group or series. The term ‘Dispersion’ can also be used to indicate data spread. This is a great way to show how quantitative data has spread relative to the central point.

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Let’s take a closer look at absolute dispersion measures and how they are used within Six Sigma practices.

Uniformity and dispersion

All items or observations in a collection of data are not equal. There is variation or difference between the values. Different relative and absolute measures of dispersion can be used to determine the degree of variation. A small dispersion can indicate high uniformity, while a large dispersion can indicate less uniformity.

Absolute and relative measures of dispersion difference

There are two types of dispersion measures:

Absolute measures for dispersion

Relative measures for dispersion

Absolute measures of dispersion are the percentage of variation in a given set of values. They can be expressed in units of observations. If rainfall data is available for different days in millimeters, any absolute measures can be used to measure the variation in rainfall in millimeters. Relative measures of dispersion, on the other hand are not dependent on the units of measurements. They are pure numbers. They can be used to compare variation between two or more sets that have different units of measurement of observations. Six Sigma teams can use both absolute and relative measures of dispersion.

Absolute Measures Of Dispersion

These are the absolute measures of dispersion:

Range

Quartile deviation

Mean deviation

Standard deviation

Range

This is the simplest of all the absolute measures of dispersion. It is the difference between the largest value and the smallest value of the variable. The formula for range is the largest value minus the smallest. It is symbolically read as L minus S. 11. is the largest value in this data set. 4. The range is 11 minus 4, which makes 7. This is an example for one of the absolute measures dispersion.

Absolute Measures Of Dispersion: Quartile Deviation

What are Quartiles?

Before we get to the absolute measure of dispersion, quartile deviation, let us first define what quartiles are. Quartiles are measures that divide data into four equal parts. Each portion contains the same number of observations. There are three quartiles. Q1 denotes the first quartile. It is also known as the lower quartile. It contains 25% of the items in the distribution below it, and 75% of items are greater than it. Q2 denotes the second quartile. It is the median of all data. It contains 50% of items below and 50% of observations above it. Q3 denotes the third quartile. It is also known as the upper quartile. It contains 75% of items below it and 25% above it. Q1 and Q3 are the two limits within the central 50% of data. Also, the medium of the data is equal to the third quartile minus the first quartile. That’s it!

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