Stability and Capability in Quality Improvement
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Stability and Capability in Quality Improvement
This course is part of Data Science Methods for Quality Improvement Specialization
Instructor: Wendy Martin
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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
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Tools you'll learn
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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.
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 videos•Total 80 minutes
- Working in RStudio•10 minutes
- Process Variation•6 minutes
- Common and Special Cause Variation•7 minutes
- Purpose of a Control Chart•5 minutes
- Conformance Quality•6 minutes
- The Product and Process Control Cycles•6 minutes
- Process Dominance•5 minutes
- Control Chart Basics•5 minutes
- Creating a Control Chart - Steps 1 and 2•5 minutes
- Creating a Control Chart - Step 2 (Continued)•6 minutes
- Creating a Control Chart - Step 3•5 minutes
- Creating a Control Chart - Step 4•6 minutes
- Creating a Control Chart - Steps 5, 6 and 7•8 minutes
3 readings•Total 21 minutes
- Course Updates and Accessibility Support•1 minute
- Earn Academic Credit for your Work!•10 minutes
- Course Support•10 minutes
1 assignment•Total 30 minutes
- Process Variation, Process Control and Control Charts•30 minutes
2 discussion prompts•Total 20 minutes
- Introduce Yourself! •10 minutes
- Process Variation, Process Control, and Control Charts•10 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 videos•Total 66 minutes
- Mean and Range Charts - Part 1•4 minutes
- Mean and Range Charts - Part 2•5 minutes
- Mean and Range Charts - Part 3•7 minutes
- Mean and Standard Deviation Charts - Part 1•5 minutes
- Mean and Standard Deviation Charts - Part 2•5 minutes
- Individuals and Moving Range Charts - Part 1•5 minutes
- Individuals and Moving Range Charts - Part 2•5 minutes
- Individuals and Moving Range Charts - Part 3•8 minutes
- Individuals and Moving Range Charts - Part 4•5 minutes
- Setup Dominant Processes•8 minutes
- Machine Dominant Processes•8 minutes
1 reading•Total 5 minutes
- READ ME FIRST•5 minutes
1 assignment•Total 30 minutes
- Xbar and R / Xbar and S Charts / X and MR Charts•30 minutes
1 discussion prompt•Total 10 minutes
- Xbar and R / Xbar and S Charts / X and MR Charts•10 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 videos•Total 58 minutes
- Introduction•4 minutes
- Log Transformed Data - Part 1•5 minutes
- Log Transformed Data - Part 2•5 minutes
- Exponential Data - Part 1•5 minutes
- Exponential Data - Part 2•4 minutes
- Exponential Data - Part 3•9 minutes
- Introduction to Distribution Fitting•4 minutes
- Goodness of Fit Testing - One Distribution•4 minutes
- Goodness of Fit Testing - Multiple Distributions•5 minutes
- The Johnson Distribution - Part 1•4 minutes
- The Johnson Distribution - Part 2•6 minutes
- Selecting the Best Fit and Creating the Control Chart•3 minutes
1 assignment•Total 30 minutes
- X and Moving Range Charts for Non-Normally Distributed Data•30 minutes
1 discussion prompt•Total 10 minutes
- X and Moving Range Charts for Non-Normally Distributed Data•10 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 videos•Total 73 minutes
- Process Control vs Process Capability•3 minutes
- Capability Indices•5 minutes
- Cpm and the Taguchi Loss Function•3 minutes
- Capability vs Performance Measures•3 minutes
- Capability / Performance - Xbar and R chart Part 1•6 minutes
- Capability / Performance - Xbar and R chart Part 2•5 minutes
- Capability / Performance - Xbar and s chart Part 1•5 minutes
- Capability / Performance - Xbar and s chart Part 2•6 minutes
- Capability / Performance - X and MR chart•4 minutes
- Capability / Performance - Transformed Data Part 1•6 minutes
- Capability / Performance - Transformed Data Part 2•6 minutes
- Capability / Performance - Transformed Data Part 3•2 minutes
- Capability / Performance - Exponential Part 1•6 minutes
- Capability / Performance - Exponential Part 2•5 minutes
- Capability / Performance - Distribution Fitting Part 1•4 minutes
- Capability / Performance - Distribution Fitting Part 2•3 minutes
1 assignment•Total 30 minutes
- Process Capability•30 minutes
1 discussion prompt•Total 10 minutes
- Process Capability•10 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 videos•Total 67 minutes
- Introduction to Attribute Control Charts•4 minutes
- p Charts - Part 1•6 minutes
- p Charts - Part 2•5 minutes
- p Charts - Part 3•8 minutes
- np Charts - Part 1•4 minutes
- np Charts - Part 2•5 minutes
- np Charts - Part 3•10 minutes
- c Charts - Part 1•5 minutes
- c Charts - Part 2•4 minutes
- c Charts - Part 3•4 minutes
- u Charts - Part 1•4 minutes
- u Charts - Part 2•6 minutes
1 assignment•Total 30 minutes
- Attribute / Discrete Control Charts•30 minutes
1 discussion prompt•Total 10 minutes
- Attribute / Discrete Control Charts•10 minutes
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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
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Reviewed on Aug 1, 2022
Good case study for the proactice of the SPC with the R programing! It is quite challendge but happy to pass the course finally!
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