Statistical Quality Control Charts | AIChE

Statistical Quality Control Charts

Improve quality control of your processes and save time and money by avoiding unnecessary operational changes. This eLearning course will equip you with the skills to use statistical metrics to monitor your processes and determine if they are in or out of control.

Even if you’re new to statistics, learn a technique based on a statistical analysis of the data that is not limited to just quality, but can be used with other numerical measurements. Originally called “quality control charts”, they now are also referred to as “process control charts.”  In eight one-hour lectures, you’ll explore basic statistical analyses and how to construct control charts based on the appropriate error distribution of a process, control and interpret those charts. In addition, you’ll learn how to validate data through the Analysis of Variance (ANOVA) and on statistically designed experiments. This knowledge can be used when trouble-shooting a process determined to be statistically out of control.

Learning Outcomes

  • Define basic statistical terms including mean, standard deviation, the normal (Gaussian) distribution, the binomial distribution, significance tests, and confidence limits.

  • Contrast the difference between common cause and special cause variation and how both affect the choice of which control chart should be used.

  • Explain why making an unwarranted adjustment to a process is counter-productive resulting in an increase of variance and poorer quality.

  • Discuss the necessity of monitoring run charts to determine appropriate process index (potential, capability or performance) to use along with the measurement capability index.

  • Compare and contrast the proper type of control chart including Shewart type control charts for Gaussian data, attribute type control charts for binomial data, and time-series type charts that are independent of the data distribution.

  • Calculate the lower and upper control limits for both the quality (process) metric and the variation of that metric.

Chemical engineers and other professionals who monitor the quality or any other numerical metric of a process and determine if a process is statistically in or out of control. No background in statistics is required. 

  • Introduction
  • Basic Statistics
  • Run Charts, Process Indexes and the Box-Jenkins Time Series
  • Variables Control Charts, Part I
  • Variables Control Charts, Part II
  • Attributes Control Charts
  • Sampling and ANOVA
  • The Real World

This course is approved by RCEP (Registered Continuing Education Program).

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    8 hours
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