• Specialization: Data Science Methods for Quality Improvement
  • Instructor: Wendy Martin, Instructor, W. Edwards Deming Professor of Management
  • Prior knowledge needed: Managing, Describing and Analyzing Data

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Learning Outcomes

  • Understand how to use, select, and interpret process control charts to identify special causes of variation
  • Create and interpret control charts for discrete data
  • Create and interpret control charts for normal and non-normal distributions
  • Analyze the capability of a process to meet customer specifications

Course Content

Duration: 2h

In this module, you will learn how to define a process and break it down into components for the purpose of identifying potential sources of variation. You will learn how to classify variation into common and special causes through the use of a control chart. You’ll discover the Taguchi Loss function, and how it relates to the philosophy of quality, and its association to the product control and process control cycles. You will learn the basic anatomy of a control chart as well as the process used to create a control chart, and common errors encountered when using a control chart in practice. You will be able to calculate an appropriate sample size, as well as determine when a process is in control or out of control based on statistical rules.

Duration: 2h

In this module, you will learn how to select the appropriate chart given information on sample size and data type. You’ll learn how to create and interpret control charts with subgroups for variables data, as well as how to create them in R. You will also create and interpret control charts with a sample size of one data that is normally distributed. You'll learn how to monitor other statistics using the Individuals and Moving Range Chart. Finally, you will interpret the control charts for statistical control / stability.

Duration: 2h

In this module, you will learn how to create an X and Moving Range Chart when the underlying distribution is not normally distributed. You’ll learn how to calculate control limits for the X and MR Charts with LogNormal transformed distribution and exponential distribution. Additionally, you will learn how to fit a distribution to the data and calculate control limits associated with the selected distribution. Finally, you will interpret the control charts for statistical control / stability.

Duration: 2h

In this module, you will learn how to compare process variation to customer specifications. You’ll learn the three indices associated with capability measures and the three indices associated with performance measures. Additionally, you will learn to assess capability and performance when the data are not normally distributed.

Duration: 2h

In this module, you will learn how to create and analyze control charts for discrete data. You will learn how to differentiate between data that are Binomial and data that are Poisson distributed in order to select the appropriate control chart. Additionally, you will learn to assess capability using an appropriate discrete probability model.

Duration: 30m

You will complete a multiple choice exam worth 20% of your grade. You must attempt the final in order to earn a grade in the course. If you've upgraded to the for-credit version of this course, please make sure you review the additional for-credit materials in the Introductory module and anywhere else they may be found.

Note: This page is periodically updated. Course information on the Coursera platform supersedes the information on this page. Click View on Coursera button above for the most up-to-date information.