Coursera

Stability and Capability in Quality Improvement (Coursera)

Offered by University of Colorado Boulder,
Stability and Capability in Quality Improvement (Coursera)

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.

Class Deals by MOOC List - Click here and see Coursera's Active Discounts, Deals, and Promo Codes.

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.

What You Will 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

Syllabus

WEEK 1
Understanding Process Variation, Process Control and Control Charts
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.

WEEK 2
Xbar and R / Xbar and S Charts / X and MR Charts
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.

WEEK 3
X and Moving Range Charts for Non-Normally Distributed Data
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.

WEEK 4
Process Capability
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.

WEEK 5
Control Charts for Discrete Data
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.

Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Six Sigma and the Organization (Advanced) (Coursera) Coursera
University System of Georgia

Six Sigma and the Organization (Advanced) (Coursera)

Management & Leadership

This course is for you if you are looking to dive deeper into Six Sigma or strengthen and expand your knowledge of the basic components of green belt level of Six Sigma and Lean. Six Sigma skills are widely sought by employers both nationally and internationally. These skills have been proven to help improve business processes and performance. This course will introduce you to the purpose of Six Sigma and its value to an organization. You will learn about the basic principles of Six Sigma and Lean. Your instructors will introduce you to, and have you apply, some of the tools and metrics that are critical components of Six Sigma.

Jun 22nd 2026
5-12 Weeks
Business Organization Lean Six Sigma
Details
Variable Selection, Model Validation, Nonlinear Regression (Coursera) Coursera
Illinois Tech

Variable Selection, Model Validation, Nonlinear Regression (Coursera)

Statistics & Data Analysis Data Science

If you have a technical background in mathematics/statistics/computer science/engineering and or are pursuing a career change to jobs or industries that are data-driven, this course is for you. Those industries might be finance, retail, tech, healthcare, government, or many others. The opportunity is endless.

Jun 22nd 2026
4 Weeks
Statistics Probability Logistic Regression
Details
Deep Learning and Reinforcement Learning (Coursera) Coursera
IBM

Deep Learning and Reinforcement Learning (Coursera)

Data Science

This course introduces you to two of the most sought-after disciplines in Machine Learning: Deep Learning and Reinforcement Learning. Deep Learning is a subset of Machine Learning that has applications in both Supervised and Unsupervised Learning, and is frequently used to power most of the AI applications that we use on a daily basis. First you will learn about the theory behind Neural Networks, which are the basis of Deep Learning, as well as several modern architectures of Deep Learning.

Jun 22nd 2026
5-12 Weeks
Calculus Python Statistics
Details
Hypothesis Testing with Python and Excel (Coursera) Coursera
Tufts University

Hypothesis Testing with Python and Excel (Coursera)

Statistics & Data Analysis Data Science

In today's job market, leaders need to understand the fundamentals of data to be competitive. An essential procedure to understand business and analytics is hypothesis testing. This short course, designed by Tufts University expert faculty, will teach the fundamentals of hypothesis testing of a population mean and a population proportion, using Excel and Python for calculations. You'll also discover the central limit theorem, which is essential for hypothesis testing. To conclude the course, you will apply your newfound skills by creating a plan for an experiment in your own workplace that uses hypothesis testing.

Jun 23rd 2026
1 Week
Python Excel Python Programming
Details
The DMAIC Framework: Analyze, Improve, and Control Phase (Coursera) Coursera
SkillUp EdTech

The DMAIC Framework: Analyze, Improve, and Control Phase (Coursera)

Statistics & Data Analysis Data Science

The course will teach you the competencies and essential skills required to pass the American Society for Quality (ASQ) Certified Six Sigma Green Belt (CSSGB) exam. This course focuses specifically on the last three phases of the define, measure, analyze, improve, and control (DMAIC) framework and will enable you to analyze the root causes of existing issues, implement solutions, and ensure the process's sustainability. You will gain insight into data analysis techniques such as hypothesis testing and regression analysis and be able to develop control plans for process improvement.

Jun 22nd 2026
5-12 Weeks
Exploratory Data Analysis Regression Analysis DMAIC
Details
Delivery Problem (Coursera) Coursera
University of California, San Diego,Higher School of Economics - HSE University

Delivery Problem (Coursera)

CS: Software Engineering CS: Theory

We’ll implement (in Python) together efficient programs for a problem needed by delivery companies all over the world millions times per day — the travelling salesman problem. The goal in this problem is to visit all the given places as quickly as possible. How to find an optimal solution to this problem quickly? We still don’t have provably efficient algorithms for this difficult computational problem and this is the essence of the P versus NP problem, the most important open question in Computer Science.

Jun 22nd 2026
3 Weeks
Probability Combinatorics Algorithms
Details
Aléatoire : une introduction aux probabilités - Partie 1 (Coursera) Coursera
École Polytechnique

Aléatoire : une introduction aux probabilités - Partie 1 (Coursera)

Sci: Mathematics

Ce cours d'introduction aux probabilités a la même contenu que le cours de tronc commun de première année de l'École polytechnique donné par Sylvie Méléard. Le cours introduit graduellement la notion de variable aléatoire et culmine avec la loi des grands nombres et le théorème de la limite centrale. Les notions mathématiques nécessaires sont introduites au fil du cours et de nombreux exercices corrigés sont proposés.

Jun 15th 2026
5-12 Weeks
Math Probability Mathematics
Details
Introduction to Probability and Data with R (Coursera) Coursera
Duke University

Introduction to Probability and Data with R (Coursera)

Statistics & Data Analysis Data Science

This course introduces you to sampling and exploring data, as well as basic probability theory and Bayes' rule. You will examine various types of sampling methods, and discuss how such methods can impact the scope of inference. A variety of exploratory data analysis techniques will be covered, including numeric summary statistics and basic data visualization.

Jun 22nd 2026
5-12 Weeks
Statistics Probability Inference
Details
Taking Safety and Quality Improvement Work to the Next Level (Patient Safety VII) (Coursera) Coursera
Johns Hopkins University

Taking Safety and Quality Improvement Work to the Next Level (Patient Safety VII) (Coursera)

Health & Society

In this culminating course in the Patient Safety and Quality Improvement Specialization, you will apply the skills you have acquired across the previous six courses to address a realistic patient safety issue confronting Mercy Grace, a 500-bed urban hospital that is part of a larger hospital system. Based on the scenario provided, you will assess the situation and work through the problem using a variety of tools and strategies.

Jun 22nd 2026
5-12 Weeks
Health Healthcare Patient Safety
Details
A Crash Course in Causality: Inferring Causal Effects from Observational Data (Coursera) Coursera
University of Pennsylvania

A Crash Course in Causality: Inferring Causal Effects from Observational Data (Coursera)

Statistics & Data Analysis Data Science

We have all heard the phrase “correlation does not equal causation.” What, then, does equal causation? This course aims to answer that question and more! Over a period of 5 weeks, you will learn how causal effects are defined, what assumptions about your data and models are necessary, and how to implement and interpret some popular statistical methods. Learners will have the opportunity to apply these methods to example data in R (free statistical software environment).

Jun 22nd 2026
5-12 Weeks
Statistics Probability Statistical Methods
Details
Statistical Inference (Coursera) Coursera
Johns Hopkins University

Statistical Inference (Coursera)

Statistics & Data Analysis Data Science

Statistical inference is the process of drawing conclusions about populations or scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strategies and explicit use of designs and randomization in analyses. Furthermore, there are broad theories (frequentists, Bayesian, likelihood, design based, …) and numerous complexities (missing data, observed and unobserved confounding, biases) for performing inference.

Jun 22nd 2026
4 Weeks
Statistics Probability Data Analysis
Details