Histogram & capability analysis

In the last post we looked at tools for analyzing distribution of variable data. Histogram is the most popular distribution analysis tool. Using the histogram we can also predict the capability of the process.

Histogram shows the frequency distribution of the data. The curve shows shape of the distribution. Skew measures asymmetry of the distribution. A positive skew indicates a histogram with a longer right side tail whereas a negative skew has a longer left side tail.

So where is the tie in between the histogram and capability analysis?
Process capability analysis compares process performance against specifications. Histogram shows the distribution of process data. If the histogram graphically showed the specification limits then it is showing if the process is performing within specs or not. So visually the histogram would show if the process is producing good parts or not.

Numerically capability of a process can be analyzed by calculating the capability indices. There are several capability indices each showing an aspect of process capability. Most popular capability indices are Cp, Cpk, Pp, Ppk.

Read our White Paper on different capability indices and their uses.

What capability indices do you to determine process capability? How do you use the histogram for distribution analysis?

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