Using and Producing a Control Chart in R for Statistical Process Control – An Application in Analytical Chemistry
August 2, 2015 6 Comments
Many processes in chemistry, especially in synthesis, require attaining a certain target value for a property of interest. For example, when synthesizing drug capsules that contain a medicine, a chemist has to ensure that the concentration of the medicine meets a target value. If the concentration is too high or too low, then the patient ingesting the drug capsules could suffer catastrophic health problems. Thus, monitoring this attainment is a very important part of analytical chemistry.
Of course, natural variation in any chemical process will result in some variation in the output, so the target value will rarely be attained exactly. There is usually an acceptable range of values, but any deviation of the output beyond this acceptable range must be discovered and treated with alarm, as the underlying process for generating that output may be inherently faulty. The process should be stopped, examined, and repaired before any more output can be generated. From a statistical perspective, there needs to be some mechanism to monitor for outliers as the process unfolds.
A control chart is a useful tool for monitoring chemical processes to detect outliers. In this tutorial, I will
- explain the underlying concepts of a simple but common type of control charts
- demonstrate how to produce control charts with an example data set in R
Read the rest of this blog post to learn how to build the above control chart in R!