Leading with Designed Experiments: ANOVA and Taguchi Methods from Udemy

As the complexity of your manufacturing processes increase with the addition of input variables like speeds, feeds, temperature, and machine types, you ability to "trial and error" your way into an optimal process decreases. So often, manufacturing professionals fail to recognize that well-designed process experiments can lead to fewer defects, higher production rates, and improved mechanical properties.

In this course, "Leading with Designed Experiments: ANOVA and Taguchi Methods", you will learn how to design, conduct, and analyze the results of process experiments in a manner that leads to those optimal results you desire.

More specifically, you will learn:

A detailed overview of Design of Experiments (DOE) in the manufacturing context
How DOE can improve your decision making skills
What is Design of Experiments? Are where are they commonly used?
Critical terms and definitions
The Two Types of Experimental Error
What is ANOVA? How is it used in decision making?
Real-life examples of 1-way and 2-way ANOVA in Microsoft Excel
An Overview of Full Factorial Experiments
Fractional Factorial Experiments and the Taguchi Methods
Examples of Taguchi Methods used to solve complex manufacturing problems
And MUCH MORE.

In addition to the 3+ hours of instructional video, when you sign up for this course, you also get:

3 tests (with Answer Keys) to verify your learning progress
A Microsoft Excel workbook containing the 5 worksheets with 1-way and 2-way ANOVA's used in the course
17 Real-life case studies used in the course instruction
ALL SLIDES FOR THE CLASS in a pdf format
A Certificate of Completion showing your name, the course title, and length of course
Lifetime access to all course materials ... the videos, exams, slides and Excel worksheets.
Q&A access to the instructors via Udemy

With the case study approach used in this class, you will now only learn the key concepts, terminology, and methods used in

So if you are a quality, industrial, or manufacturing engineer or manager, and want to advance your analytical problem solving skills, then this is the class for you. SIGN UP TODAY.

What's inside

Learning objectives

A detailed overview of design of experiments (doe) in the manufacturing context
How doe can improve your decision making skills
What is design of experiments? are where are they commonly used?
Critical terms and definitions
The two types of experimental error
What is anova? how is it used in decision making?
Real-life examples of 1-way and 2-way anova in microsoft excel

An overview of full factorial experiments
Fractional factorial experiments and the taguchi methods
Examples of taguchi methods used to solve complex manufacturing problems
Downloadable excel templates
Show more
Show less

A detailed overview of design of experiments (doe) in the manufacturing context
How doe can improve your decision making skills
What is design of experiments? are where are they commonly used?
Critical terms and definitions
The two types of experimental error
What is anova? how is it used in decision making?
Real-life examples of 1-way and 2-way anova in microsoft excel
An overview of full factorial experiments
Fractional factorial experiments and the taguchi methods
Examples of taguchi methods used to solve complex manufacturing problems
Downloadable excel templates
Show more
Show less

Syllabus

The Two Error Types

Test #1

What's Next?

Introduction

Downloadable resource is a Microsoft Excel workbook containing the five problems Mike described in the video, each in its own worksheet labeled "PROBLEM." A subsequent worksheet to each of these five is labeled "SOLUTION." These contain the answers to the problems.

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Provides a detailed overview of Design of Experiments (DOE) in the manufacturing context, which is highly relevant for professionals in quality, industrial, or manufacturing engineering roles

Uses real-life case studies to illustrate the application of ANOVA and Taguchi methods, which helps learners connect theoretical concepts to practical problem-solving in manufacturing

Includes downloadable Excel templates for 1-way and 2-way ANOVA, which allows learners to immediately apply the methods taught in the course to their own data and processes

Requires learners to use Microsoft Excel, which may require some learners to purchase a license if they do not already have access to the software

Explores Taguchi methods as an alternative to full factorial experiments, which offers learners a practical approach to solving complex manufacturing problems with fewer resources

Reviews summary

Practical doe, anova, and taguchi with excel

According to learners, this course offers a largely positive introduction to Design of Experiments (DOE), ANOVA, and Taguchi methods, particularly valuable for manufacturing professionals. Many students praise the clear explanations and the extensive use of real-life case studies that effectively bridge theory and practical application. The inclusion of downloadable Excel workbooks with solved examples is frequently highlighted as a highly useful resource. While providing a solid foundation, some reviewers suggest that the course could benefit from more in-depth coverage for advanced users or those seeking complex applications. The focus on Microsoft Excel for analysis is seen as practical by many but less ideal for those preferring dedicated statistical software.

Good overview, may be basic for experts.

"Solid overview. Covers the fundamentals needed for a good introduction to DOE, ANOVA, and Taguchi."

"For those looking for deep statistical theory or advanced applications using software like Minitab, this might be too basic."

"The course explains the basics well, but it felt a bit rushed in the later sections on Taguchi. I needed more depth."

"It's a good starting point, but don't expect to become an expert just from this course."

Practical for many users, but not for all.

"The Excel examples were easy to follow and useful for replicating the analysis myself."

"It's great that the Excel files are provided with solutions; it helped reinforce the learning."

"I wish there were more examples using actual statistical software instead of just Excel, but the concepts are explained clearly."

"While Excel is convenient, it feels limiting for larger or more complex DOE applications."

Slides and Excel sheets are valuable.

"The downloadable materials were very helpful, especially the Excel worksheets with the problems and solutions."

"Getting all the slides in PDF format is a huge plus; it made reviewing the material much easier."

"The provided Excel workbook with solved examples was an incredibly useful resource for practicing."

Complex concepts are made easy to grasp.

"Excellent course! The instructor's explanations were very clear and easy to follow, even for complex statistical ideas."

"Good introduction to ANOVA and Taguchi. The instructor is knowledgeable and the concepts are explained clearly."

"The way the instructor broke down ANOVA and Taguchi methods was very helpful. It made the material accessible."

"I found the lectures easy to digest; the professor has a clear way of explaining things."

Connects theory to real-world manufacturing.

"The case studies really helped connect the theory of DOE to real-world manufacturing problems. I could immediately see the application."

"Perfect for my needs as a quality manager. The course cuts straight to the practical application using relevant case studies."

"The focus on practical application and the step-by-step Excel demos make complex topics like 2-way ANOVA understandable."

"Learned how to apply these methods directly to my work environment thanks to the examples."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Leading with Designed Experiments: ANOVA and Taguchi Methods with these activities:

Review Basic Statistics Concepts

Show steps

Reinforce your understanding of fundamental statistical concepts like hypothesis testing and p-values, which are essential for interpreting ANOVA results.

Browse courses on Hypothesis Testing

Show steps

Review definitions of key statistical terms.
Work through practice problems on hypothesis testing.
Watch introductory videos on statistical concepts.

Read 'Taguchi Methods Explained' by Thomas Barker

Show steps

Enhance your knowledge of Taguchi methods by studying this practical guide to interpreting Taguchi experiment results.

View Engineering Quality by Design: Interpreting the... on Amazon

Show steps

Read the chapters on orthogonal arrays and signal-to-noise ratios.
Work through the examples provided in the book.
Compare the book's approach to the course material.

Read 'Statistics for Experimenters' by Box, Hunter, and Hunter

Show steps

Deepen your understanding of the statistical foundations of designed experiments by studying this comprehensive book.

View Statistics for Experimenters on Amazon

Show steps

Read the chapters on ANOVA and regression analysis.
Work through the examples provided in the book.
Compare the book's approach to the course material.

Four other activities

Expand to see all activities and additional details

Show all seven activities

Perform ANOVA calculations using Excel

Show steps

Solidify your understanding of ANOVA by manually performing calculations using the provided Excel templates and comparing your results to the course examples.

Show steps

Download the Excel templates from the course.
Replicate the ANOVA calculations from the case studies.
Modify the data and observe the changes in the ANOVA results.

Create a Presentation on a DOE Case Study

Show steps

Reinforce your understanding by researching and presenting a real-world case study where Design of Experiments (DOE) was successfully applied.

Show steps

Research case studies of DOE applications.
Select a case study that interests you.
Prepare a presentation summarizing the problem, the experimental design, the results, and the conclusions.
Present your findings to colleagues or peers.

Design and Analyze Your Own Experiment

Show steps

Apply your knowledge by designing, conducting, and analyzing a simple experiment related to your field of work or interest using ANOVA or Taguchi methods.

Show steps

Identify a process or system to experiment with.
Define the factors and levels to be tested.
Design the experiment using a full or fractional factorial design.
Collect the data and perform the ANOVA analysis.
Interpret the results and draw conclusions.

Contribute to a DOE Software Package

Show steps

Deepen your understanding by contributing to an open-source software package related to Design of Experiments (DOE).

Show steps

Find an open-source DOE software package on GitHub or similar platform.
Identify a bug or feature to work on.
Contribute code, documentation, or tests to the project.
Submit a pull request and address any feedback.

Career center

Learners who complete Leading with Designed Experiments: ANOVA and Taguchi Methods will develop knowledge and skills that may be useful to these careers:

Quality Engineer

A quality engineer strives to ensure that products and processes meet certain standards. This often involves statistical analysis and experimentation to identify areas for improvement. This course, with its detailed overview of Design of Experiments in a manufacturing context, helps quality engineers improve their decision-making skills. The course also provides real-life examples of analysis of variance in Microsoft Excel, which may be useful to a quality engineer. The course's exploration of Taguchi methods and downloadable Excel templates helps build the foundation needed to excel as a quality engineer.

See salaries and explore the career path for Quality Engineer

Manufacturing Engineer

A manufacturing engineer works to improve efficiency and productivity in manufacturing processes. This role uses data analysis and experimentation to optimize operations. This course helps manufacturing engineers improve their understanding of Design of Experiments, which is critical for optimizing manufacturing processes. The course also demonstrates how designed process experiments can lead to fewer defects and improved mechanical properties, which may be useful to a manufacturing engineer. Case studies, such as Tube Cutting, Fuel Pump, and Injection Molding, help build a foundation in manufacturing engineering.

See salaries and explore the career path for Manufacturing Engineer

Process Engineer

A process engineer focuses on designing, implementing, controlling, and optimizing industrial processes. This role typically requires a strong understanding of statistical analysis and experimental design. This course helps process engineers improve their decision-making skills, offering a detailed overview of Design of Experiments. It also provides examples of analysis of variance in Microsoft Excel, which may be useful to a process engineer. The course's exploration of Taguchi methods is helpful in solving complex manufacturing problems for a process engineer.

See salaries and explore the career path for Process Engineer

Industrial Engineer

An industrial engineer works to improve efficiency and productivity in organizations and systems. The job involves analyzing data and designing experiments to optimize processes. This course provides industrial engineers with insights into Design of Experiments and how it can improve decision-making skills. The real-life examples of analysis of variance in Microsoft Excel may be useful to an industrial engineer. The course's exploration of Taguchi methods also helps industrial engineers solve complex problems.

See salaries and explore the career path for Industrial Engineer

Six Sigma Black Belt

A Six Sigma Black Belt is a professional trained in the Six Sigma methodologies to lead improvement projects. This role requires expertise in statistical analysis and experimental design. This course helps Six Sigma Black Belts enhance their understanding of Design of Experiments. The real-life examples of analysis of variance in Microsoft Excel may be useful to a Six Sigma Black Belt. The course's exploration of Taguchi methods may also be relevant.

See salaries and explore the career path for Six Sigma Black Belt

Continuous Improvement Manager

A continuous improvement manager focuses on identifying and implementing ways to improve processes and systems within an organization. This role often uses statistical analysis and experimental design to drive improvements. This course helps continuous improvement managers by providing insights into Design of Experiments. The course also presents examples of analysis of variance. The course's downloadable Excel templates can be helpful for a continuous improvement manager.

See salaries and explore the career path for Continuous Improvement Manager

Research and Development Scientist

A research and development scientist plans and conducts experiments to create or improve products or processes, requiring a master's degree or doctorate. This role often uses statistical analysis to interpret experimental results. This course helps research and development scientists enhance their understanding of Design of Experiments in a manufacturing environment. The course also provides real-life examples of analysis of variance. The course's exploration of Taguchi methods may be valuable in solving complex research challenges for a research and development scientist.

See salaries and explore the career path for Research and Development Scientist

Product Development Engineer

A product development engineer designs and develops new products or improves existing ones. This role often involves conducting experiments and analyzing data to optimize product designs. This course helps product development engineers improve their understanding of Design of Experiments. The course also provides real-life examples of analysis of variance in Microsoft Excel. The course's exploration of Taguchi methods may be valuable in solving complex design challenges.

See salaries and explore the career path for Product Development Engineer

Reliability Engineer

A reliability engineer focuses on ensuring the reliability and durability of products. This role often uses statistical methods to analyze failure data and design experiments to improve product reliability. This course may be useful to reliability engineers, providing a detailed overview of Design of Experiments. The course also includes real-life case studies which would provide insights to a reliability engineer. The course's exploration of Taguchi methods may be useful in solving complex reliability problems.

See salaries and explore the career path for Reliability Engineer

Test Engineer

A test engineer designs and implements tests to evaluate the functionality and performance of products. This role often uses statistical methods to analyze test data. This course may be useful to test engineers, providing a detailed overview of Design of Experiments. The course also includes real-life case studies, which would provide insights to a test engineer who designs experiments and conducts tests. The course's exploration of Taguchi methods may be valuable in solving complex testing challenges.

See salaries and explore the career path for Test Engineer

Statistician

A statistician collects, analyzes, and interprets data to identify trends and relationships. This role requires a solid understanding of statistical methods and experimental design, often requiring a master's degree or doctorate. This course may be useful to statisticians, offering a detailed overview of Design of Experiments in a manufacturing context. The course also provides real-life examples of analysis of variance, which may be useful to a statistician. The course's exploration of full and fractional factorial methods helps to solve complex problems.

See salaries and explore the career path for Statistician

Data Analyst

A data analyst examines data to identify trends, patterns, and insights that can inform decision-making. This role requires a strong understanding of statistical analysis and experimental design. This course may be useful to data analysts by providing a detailed overview of Design of Experiments. It also demonstrates real-life examples of analysis of variance in Microsoft Excel which may be relevant to a data analyst. The course's coverage of full and fractional factorial methods is also helpful.

See salaries and explore the career path for Data Analyst

Statistical Consultant

A statistical consultant provides expert advice and guidance on statistical methods and data analysis to clients in various industries. The role requires a solid understanding of statistical methods and experimental design. A master's degree or doctorate is often required. This course may be useful to statistical consultants, offering a detailed overview of Design of Experiments in a manufacturing setup. The course also shares real-life examples of analysis of variance. The course's insights into Taguchi methods helps solve complex manufacturing problems.

See salaries and explore the career path for Statistical Consultant

Operations Manager

An operations manager is responsible for overseeing the efficient and effective operation of an organization. This role uses data analysis to improve processes. This course may be useful to operations managers, providing insights into Design of Experiments. The course also demonstrates how designed process experiments can lead to fewer defects and higher production rates, which is useful to an operations manager. Case studies are provided by the course.

See salaries and explore the career path for Operations Manager

Manufacturing Supervisor

A manufacturing supervisor oversees production processes and personnel, ensuring that operations run smoothly. This role often involves making decisions based on data and observations. This course may be useful to manufacturing supervisors, offering insights into Design of Experiments. The course also demonstrates how designed process experiments can lead to improved mechanical properties, which is useful to a manufacturing supervisor. Case studies within the course provide a foundation for improving overall efficiency.

See salaries and explore the career path for Manufacturing Supervisor

Reading list

We've selected two books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Leading with Designed Experiments: ANOVA and Taguchi Methods.

Statistics for Experimenters

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Classic text on experimental design and data analysis. It provides a comprehensive treatment of statistical methods relevant to designed experiments. It is particularly useful for understanding the underlying principles of ANOVA and Taguchi methods. This book is often used as a textbook in graduate-level statistics courses.

Statistics for Experimenters

Paperback

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Statistics for Experimenters

Kindle Edition

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Engineering Quality by Design

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Provides a practical guide to understanding and applying Taguchi methods. It focuses on interpreting the results of Taguchi experiments and making informed decisions. This book is valuable for those who want to use Taguchi methods to solve real-world manufacturing problems. It adds more depth to the course's coverage of Taguchi methods.

Engineering Quality by Design: Interpreting the...

Hardcover

$$$

Leading with Designed Experiments

ANOVA and Taguchi Methods

What's inside

Learning objectives

Syllabus

Traffic lights

Save this course

Reviews summary

Practical doe, anova, and taguchi with excel

Activities

Career center

Reading list

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