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Stability and Capability in Quality Improvement

Stability and Capability in Quality Improvement

This course is part of Data Science Methods for Quality Improvement Specialization

Instructor: Wendy Martin

2,565 already enrolled

Included with

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5 modules

Gain insight into a topic and learn the fundamentals.

17 reviews

Intermediate level

Recommended experience

Flexible schedule

1 week at 10 hours a week

Learn at your own pace

Build toward a degree

Learn more

5 modules

Gain insight into a topic and learn the fundamentals.

17 reviews

Intermediate level

Recommended experience

Flexible schedule

1 week at 10 hours a week

Learn at your own pace

Build toward a degree

Learn more

What you'll learn

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

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

5 assignments

Taught in English

91%

of learners achieved a positive career outcome

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Build your subject-matter expertise

This course is part of the Data Science Methods for Quality Improvement Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 5 modules in this course

In this course, you will learn to analyze data in terms of process stability and statistical control and why having a stable process is imperative prior to perform statistical hypothesis testing. You will create statistical process control charts for both continuous and discrete data using R software. You will analyze data sets for statistical control using control rules based on probability. Additionally, you will learn how to assess a process with respect to how capable it is of meeting specifications, either internal or external, and make decisions about process improvement.

This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.

Module details

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.

What's included

13 videos3 readings1 assignment2 discussion prompts

13 videosTotal 80 minutes

Working in RStudio10 minutes
Process Variation6 minutes
Common and Special Cause Variation7 minutes
Purpose of a Control Chart5 minutes
Conformance Quality6 minutes
The Product and Process Control Cycles6 minutes
Process Dominance5 minutes
Control Chart Basics5 minutes
Creating a Control Chart - Steps 1 and 25 minutes
Creating a Control Chart - Step 2 (continued)6 minutes
Creating a Control Chart - Step 35 minutes
Creating a Control Chart - Step 46 minutes
Creating a Control Chart - Steps 5, 6 and 78 minutes

3 readingsTotal 21 minutes

Course Updates and Accessibility Support1 minute
Earn Academic Credit for your Work!10 minutes
Course Support10 minutes

1 assignmentTotal 30 minutes

Process Variation, Process Control and Control Charts30 minutes

2 discussion promptsTotal 20 minutes

Introduce Yourself! 10 minutes
Process Variation, Process Control, and Control Charts10 minutes

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.

What's included

11 videos1 reading1 assignment1 discussion prompt

11 videosTotal 66 minutes

Mean and Range Charts - Part 14 minutes
Mean and Range Charts - Part 25 minutes
Mean and Range Charts - Part 37 minutes
Mean and Standard Deviation Charts - Part 15 minutes
Mean and Standard Deviation Charts - Part 25 minutes
Individuals and Moving Range Charts - Part 15 minutes
Individuals and Moving Range Charts - Part 25 minutes
Individuals and Moving Range Charts - Part 38 minutes
Individuals and Moving Range Charts - Part 45 minutes
Setup Dominant Processes8 minutes
Machine Dominant Processes8 minutes

1 readingTotal 5 minutes

READ ME FIRST5 minutes

1 assignmentTotal 30 minutes

Xbar and R / Xbar and S Charts / X and MR Charts30 minutes

1 discussion promptTotal 10 minutes

Xbar and R / Xbar and S Charts / X and MR Charts10 minutes

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.

What's included

12 videos1 assignment1 discussion prompt

12 videosTotal 58 minutes

Introduction4 minutes
Log Transformed Data - Part 15 minutes
Log Transformed Data - Part 25 minutes
Exponential Data - Part 15 minutes
Exponential Data - Part 24 minutes
Exponential Data - Part 39 minutes
Introduction to Distribution Fitting4 minutes
Goodness of Fit Testing - One Distribution4 minutes
Goodness of Fit Testing - Multiple Distributions5 minutes
The Johnson Distribution - Part 14 minutes
The Johnson Distribution - Part 26 minutes
Selecting the Best Fit and Creating the Control Chart3 minutes

1 assignmentTotal 30 minutes

X and Moving Range Charts for Non-Normally Distributed Data30 minutes

1 discussion promptTotal 10 minutes

X and Moving Range Charts for Non-Normally Distributed Data10 minutes

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.

What's included

16 videos1 assignment1 discussion prompt

16 videosTotal 73 minutes

Process Control vs Process Capability3 minutes
Capability Indices5 minutes
Cpm and the Taguchi Loss Function3 minutes
Capability vs Performance Measures3 minutes
Capability / Performance - Xbar and R chart Part 16 minutes
Capability / Performance - Xbar and R chart Part 25 minutes
Capability / Performance - Xbar and s chart Part 15 minutes
Capability / Performance - Xbar and s chart Part 26 minutes
Capability / Performance - X and MR chart4 minutes
Capability / Performance - Transformed Data Part 16 minutes
Capability / Performance - Transformed Data Part 26 minutes
Capability / Performance - Transformed Data Part 32 minutes
Capability / Performance - Exponential Part 16 minutes
Capability / Performance - Exponential Part 25 minutes
Capability / Performance - Distribution Fitting Part 14 minutes
Capability / Performance - Distribution Fitting Part 23 minutes

1 assignmentTotal 30 minutes

Process Capability30 minutes

1 discussion promptTotal 10 minutes

Process Capability10 minutes

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.

What's included

12 videos1 assignment1 discussion prompt

12 videosTotal 67 minutes

Introduction to Attribute Control Charts4 minutes
p Charts - Part 16 minutes
p Charts - Part 25 minutes
p Charts - Part 38 minutes
np Charts - Part 14 minutes
np Charts - Part 25 minutes
np Charts - Part 310 minutes
c Charts - Part 15 minutes
c Charts - Part 24 minutes
c Charts - Part 34 minutes
u Charts - Part 14 minutes
u Charts - Part 26 minutes

1 assignmentTotal 30 minutes

Attribute / Discrete Control Charts30 minutes

1 discussion promptTotal 10 minutes

Attribute / Discrete Control Charts10 minutes

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Build toward a degree

This course is part of the following degree program(s) offered by University of Colorado Boulder. If you are admitted and enroll, your completed coursework may count toward your degree learning and your progress can transfer with you.¹