Hello friends! This is the 3rd and final installment of all you wanted to know about control charts but were afraid to ask.

In **part 1** of the series we talked about the history and purpose of control charts. In **part 2** we discussed three different control charts that are commonly used with attribute data. Tonight we will be discussing two different control charts that are commonly used when we are dealing with continuous data, also referred to as variable data.

**Continuous Data**

If you remember from last night, attribute data are either counted (Poisson) or categorized (Binomial). Data that can be measured on a continuum or scale (weight, distance, volume, height, etc.) are what we call continuous or variable data. This is the most powerful type of data available so do all you can in order to collect it instead of attribute data.

**I MR Chart**

The first type of continuous data control chart we will discuss is the Individuals and Moving Range chart, commonly referred to as an I MR chart.

Typically we see the Individuals chart, which is simply each data point graphed individually, in the upper portion of the graph. The Moving Range chart, which is simply the difference between two consecutive individual points, is seen below the Individuals chart.

Any off the shelf statistical software will calculate the control limits for you. However, if you want to construct your own I MR chart I will refer you to an excellent book entitled Understanding Variation – The Key to Managing Chaos by Donald J. Wheeler. The book is easy to read and offers an outstanding summary of all things related to control charts.

The I MR chart is very robust. You can use it to track anything from the number of home runs hit by your favorite baseball player to the OTD of your manufacturing plant. Now before you ask why you would use an I MR chart for OTD and not a p chart I will refer you back to Wheeler’s book for an excellent explanation (see page 138).

**Xbar R Chart**

The last type of control chart to discuss is the ever powerful Xbar R chart. The main difference with the Xbar R chart compared to the I MR chart deals with what we call subgroups.

Let’s assume you have a collected 1 continuous data point each day for one year. Let’s also assume there 250 working days (5 days x 50 weeks). This means we have 250 data points. Well plotting each of these data points individually may be a bit arduous so an alternative may be to use a subgroup of 5. All this means is data points 1 to 5 are averaged, then data points 6 – 10, etc. meaning we would have 50 points plotted instead of 250.

The Range portion of the chart is calculated by taking the difference between the highest and lowest value in each subgroup.

The Xbar portion of the graph is normally shown on top while the R portion of the graph is shown beneath it.

If you have access to a statistical software package like Minitab or JMP you are in luck as the software does the boring math (calculating control limits, etc.) for you. Most of these companies also offer trial versions of their software.

**Summary**

The whole point of using control charts is to add context and to visualize both the centering and variation of your data which enables us to determine if our process is in statistical control or not.

The hardest Six Sigma project is one where you asked to “move the mean” of a process that is in statistical control. The reason it is so hard is you must make a fundamental change to the process in order to succeed. If, on the other hand, you find some special causes via your initial control chart you can investigate them and find out the reason they occurred. If you can then Poka-Yoke (error proof) the process ensuring the problems don’t come back you may have a quick win!

Well that is about it for this series on control charts. I hope it was helpful. If you have any questions or want clarification on anything please post a comment or shoot me an email.

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## 7 Comments

## LINKSHARE LEADS

March 13, 2007 - 4:24 amInformative posting, You may want to check out some interesting posts on STATISTICAL PROCESS CONTROL at bizleadsnet blog directoryThanks.

## Ron Pereira

March 13, 2007 - 7:04 pmThanks for the link!