Control charts are one of the hardest things for those studying six sigma to understand. Here’s an easy Control Charts Study Guide for you. When I was studying for the Six Sigma Black Belt Exam I noticed there were a lot of questions on control charts. Besides that, I noticed that there were a lot of different types of control charts. You had to use one for certain kinds of data or number of units in the sample and then you had to use other control charts in different instances.

Beyond that, different distribution types associated with several of the data sets. Something you take for granted when asked a question specifically geared to data, but I found it was easy to get tripped up on the exam when combining with control chart questions.

Very confusing????

So, in the spirit of continuous improvement, we made the following control charts study guide.

## Control Charts Study Guide Overview

Control charts : A Control chart is one of the primary techniques of statistical process control (SPC). The control chart is a graphical display of quality characteristics that have been measured or computed from a sample versus the sample number or time. The control chart was invented by Walter Shewhart at Bell labs in 1920.

Statistical Process Control – Statistical Process Control (SPC) is a statistical method to measure, monitor, and control a process. In other words, SPC is a method of quality control which employs statistical methods to measure, monitor, and control a process.

Common Cause: A cause of variation in the process is due to chance, but not assignable to any factor. It is the variation that is inherent in the process. Process under the influence of common cause will always be stable and predictable.

Assignable Cause(“special cause”): The variation in a process that is not due to chance, therefore, can be identified and eliminated. Process under influence of special cause will not be stable and predictable.

Rational Sub Grouping: Rational sub grouping is the process of organizing the data into groups which were produced basically under the same conditions. Rational subgroups help in the estimation process of the short term variations. Thus, rational sub grouping is the basis for operating control charts in a successful manner. These variations later help us predict the long term variations and their control limits, depending o the type of causes for the variation (special or common).

## Control Charts by Data Type

The control chart is a graph used to study how process changes over time. A control chart always has a central line for average, an upper line for upper control limit, and lower line for the lower control limit. The control limits are ±3σ from the centerline.

Selection of appropriate control chart is very important in control charts mapping, otherwise ended up with inaccurate control limits for the data. Not all control charts are the same. Different Data Types require different charts.

### Control Charts for Continuous Data

Measure the output on a continuous scale. It is possible to measure the quality characteristics of a product. Once you know that you are making a control chart for continuous data, you need to determine if your population is normal or not and the sample size (n) you are charting.

No matter what you decide, I think you could start off just making a basic Run chart and seeing where that brings you. It is Just a basic graph that displays data values in a time order. Can be useful for identifying trends or shifts in process but also allows you to test for randomness in the process.

#### The Data is Normal

**Subgroup size n = 1, then use: **X-MR or I-MR chart:. An Individual moving range (I-MR ) chart is used when data is continuous and not collected in subgroups. In other words collect the single observation at a time. An I-MR chart provides process variation over time in graphical method.

**If 1 < n < 10, then use **X bar-R chart. It is used to monitor the process performance of a continuous data and the data to be collected in subgroups at set time periods. Since n is small, use the range to estimate the process variation. It consists of two plots to monitor the process mean and the process variation over the time.

Else, **If n >= 10, then use** X bar-S chart. It is often used to examine the process mean and standard deviation over time. Use X bar-S chart when the subgroups have a large sample size and also S chart provides a better understanding of the spread of subgroup data than range.

#### Special Note: If your data is Not Normal

Note that there is some robust conversation in the industry on this. For your application purposes, I suggest you read the following and make your own decisions (if you’re just studying for certification, you can probably skip):

- https://www.spcforexcel.com/knowledge/variable-control-charts/control-charts-and-non-normal-data
- https://www.qimacros.com/free-excel-tips/control-charts-and-normality/
- https://www.isixsigma.com/tools-templates/control-charts/non-normal-data-needs-alternate-control-chart-approach/

### Control Charts for Discrete / Attribute Data

Attribute control charts are used for attribute data. In other words, the data that counts the number of defective items or the number of defects per unit. For example number of tubes failed on a shop floor. Unlike variable charts, only one chart is plotted for attributes.

Four types of control charts exist for attribute data. p chart plots the proportion of defective items, and np chart is for the number of defectives. u chart is for the average number of defects per unit and c chart is for the number of defects.

#### When there are Multiple Defects (i.e. chances to be defective): It computes control limits based on the Poisson distribution.

c chart – control chart for defects. It is generally used to monitor the number of defects in constant size units.

u chart -control chart for defects per unit. It is generally used to monitor the count type of data where the sample size is greater than one. Plot the average number of defects per sample unit.

#### When there are NOT Multiple Defects:

This population sorts defects into 2 piles (it’s Binomial). Samples are either good or bad, positive or negative, right or wrong.

np chart – control chart for defectives. It is generally used to monitor the number of non-conforming or defective items in the measurement process.

p chart -control chart for proportions. It is generally used to analyze the proportions of non-conforming or defective items in a process.

**Control Chart Analysis**

The main purpose of control chart is to improve the process. It provides “ Voice of the process” that helps teams to identify special causes of variation in the process. By removing the special cause from the process, process becomes more stable and consistent process.

**Out-of-control:** A process is “out-of-control,” means special causes exists in either the average chart or range chart, or both. Teams must identify special causes and try to eliminate to achieve a stable process. We can say proces is out-of-control if any point in the control chart is out of control limits or having abnormal patterns of variability.

Control limits are 3 standard deviations above and below the mean. If the process is in control, 99.73% of the averages will fall inside these limits. The same is true for the range control limits. Because there are two components to every control chart- the average and the range.

#### Four possible conditions may occur in any process.

**Average stable, Variation changing (Example)**

#### Five Common Rules for **Control Chart Interpretation**

**Control Chart Rules**

## Control Charts Study Guide Videos

Other Helpful Articles

I love MoreSteam’s visual that shows the layering of multiple tools here.

## Comments (7)

YOU ARE A BLESSING! THANK YOU!!!

I’m taking the green belt test next week and the class was an accelerated class (6 weeks)! Ninety percent lecture. I have yet determined a project.

Glad to be of help, ddw! Let me know how else I can assist. Be sure to start taking practice exams to prepare for the test.

Please correct this :

If your data is Not Normal

Use a Run chart.

If your data is “Not” Normal You want to said : Your data is Normal

Thank you

Thanks for bringing this up, Rachid. There were a number of errors on the page that I’ve found and updated thanks to your comment.

Please check out the new section on non-normal control charts and let me know your thoughts.

Hello Ted,

I like your website and I am visiting it from time to time.

Well, I have a small question, I can’t find its answer either in your website or in other books.

Could you help me to answer it?

Select the best option

Question- A percent defective control chart designed for

Option One- Reduce variation of project

Option Two- Identify the cause of variation that lies out the system

Option Three- Ensure that variation of output is due to common causes

Option Four- Detect the existence at special causes of variation

BR,

Icomon

Icom,

Thank you for submitting the question. We have thousands of practice questions and exams available in our membership program where we walk through exactly how to solve each.

We don’t support questions outside our test bank, but this article should direct you to where you need to go to answer this question.

Best, Ted

Icom,

Thank you for submitting the question. We have thousands of practice questions and exams available in our membership program where we walk through exactly how to solve each.

We don’t support questions outside our test bank, but this article should direct you to where you need to go to answer this question.

Best, Ted