Distribution Analysis for variables

Distribution analysis for variable data is one of the critical analysis that help us understand how much
variation exists in the data. This in turn helps us understand how wide the process is and whether it falls within the specification limits.

There are several tools for studying distribution of variable data. The tools listed below are some of the popular ones:
1. Histogram
2. Stem and Leaf plot
3. Dot plot
4. Box plot

Histogram being the most popular to understand the nature of distribution of variable data. Dot and Stem & Leaf plot are more detailed versions for analyzing data distribution.
Box plots (also known as Box and Whisker plot) are used to compare distributions. Example for using a box plot would be to compare machine performance.

The figure below shows a comparison of the 4 tools.

We would love to hear which of the above tools have you used for analyzing distribution of variable data. Write to us at info@sybeq.com

Analytical tools for variable, attribute data

We have looked in the data analysis piece of Lean Six Sigma projects.
By now we know that data analysis is key in a Lean Six Sigma project. At the same time it is equally important to avoid analysis paralysis. A Green or Black Belt must know that there are different analytical tools for the type of data.

There are 2 data types
1. Variable or Continuous : Data that is measured and readings can have decimals. Eg. Weight, length, diameter. 3.56lbs, 4.5mm
2. Attribute or Discrete: Data that is counted and readings are integers. Eg. number of rejects, number or cracks

In attributes, there are 2 categories specific to the type of count.
Attribute types:
1. Defectives - The number of parts that are rejected are defectives. Also called non conforming units.
2. Defects - Number of specific problems with parts eg. burred, cracked, scratched parts. Defects could be fixed by repairing them. These parts are called non conformities.

For every data type there is a set of analytical tools. The figure below shows the commonly used tools by the type of data.