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Design of Experiments Study Guide

Posted by Ted Hessing

Design of Experiments is a difficult topic for many Six Sigma certificate applicants to master. Not to worry, we’ve got you covered with a comprehensive study guide.

The objective of Design of Experiments (DOE) is to establish optimal process performance by finding the right settings for key process input variables. The DOE is a way to intelligently form frameworks to decide which course of action you might take. This is helpful when you are trying to sort out what factors impact a process.

Basic Flow for Design of Experiments

The overall process of a DOE is as follows:

• Define objective(s)
• Gather knowledge about the process
• Develop a list and select your variables
• Assign levels to variables
• Conduct experiments
• Data analysis and conclusions

Learning Design of Experiments

Factors in an experiment.

In most experiments, you’ll have several factors to deal with. These are elements that affect the outcomes of your experiment. They fall into a few basic categories:

• Experimental factors are those you can specify and set yourself. For example, the maximum temperature to which a solution is heated.
• Classification factors can’t be specified or set, but they can be recognized, and your samples selected accordingly. For example, a person’s age or gender.
• Treatment factors are those which are of interest to you in your experiment and that you’ll want to manipulate in order to test your hypothesis.
• Nuisance factors aren’t of interest to you for the experiment but might affect your results regardless.

There are two basic types of treatment factors that you’ll use:

• Quantitative factors can be set to any specific level required–for example, pH levels.
• Qualitative factors contain several categories–for example, different plant species or a person’s gender.

A popular example in explaining factors is the simple-sounding task of baking cookies. Most people would simply follow a recipe–or, let’s face it, buy the cookie dough pre-made and bake whatever we don’t eat raw. But how did the recipe come to be in the first place? Someone had to experiment with ingredients and a baking method to get just the right combination.

• Flour: The ratios of flour to liquid and flour to fat are crucial to the texture of a cookie. Too much flour and you end up with a dry, crumbly cookie. Too little, and you end up with an overly flat, crispy cookie.
• Sugar: The type of sugar used can change the way a cookie reacts to the baking process. Using granulated (white) sugar usually creates a crisper, flatter cookie. Using brown sugar creates a moister, chewier cookie.
• Fat: Rubbing the fat into the flour creates a softer cookie. Using butter creates a flatter cookie than using margarine.
• Eggs: Eggs create a less crumbly, chewier cookie.
• Baking powder: Using baking powder causes a cookie to rise or spread, creating a ‘cakey’ texture or a more crisp cookie.
• Temperature: Low-temperature baking gives a cookie more time to spread out while cooking, meaning it’s more likely to be flatter and crisper.

Think of each ingredient and the baking temperature as factors in an experiment. You can’t test each factor independently–you must have all ingredients to produce the cookies. But you can modify the amount, type of ingredient, and temperature at which they’re baked, to find the combination that yields your perfect cookie.

Other Terminology

Review the common terminology used in Design of Experiments Factorial Design.

Planning and Organizing Designed Experiments

Determine the factors.

Next, we must understand the factors that can affect an outcome to create the appropriate design to determine how to structure our experiment.

It’s also helpful to see an example of the kinds of Factors that are in an Experiment.  These are our variables for the possible and desired outcomes.

Determine the Appropriate Design for the Experiment (Full Factorial or Partial)

Here we want to define the interactions that will be in the experiment and understand how to analyze those interactions.

One Factor at a Time (OFAT)

The (OFAT) approach is to doggedly explore every single factor independently. This is a brute force technique that you can get by with when you have just a few variables or interactions. The downside is that with large variable (factor) sets, you will spend a lot of resources doing this. You may miss the complicated interactions other more sophisticated designed experiments will give you.

One Factor at a Time vs. Full Factorial.

Factorial Design

I’ve included a quick overview of different types of factorial design. For a full description, see this overview of Full Factorial Design and see an overview of Partial or Fractional Factorial Design here.

Full Factorial Design

Full Factorial Design is a thorough and exhaustive way of determining how each factor or combination of factors affects the outcome of an experiment—at least one trial for all possible combinations of factors and levels.

2^5-2 means that there are 5 factors at 2 levels and 2 generators. The generator determines what effects are confounded or combined with one another.

We would call this example a 1/L^g fractional factorial.

Thus, 2^5-2 is a 2 level, 5-factor, 1/4th fractional design.

Partial Factorial Design

Finally, there is a Partial (or Fractional) Factorial Design . Often doing a full factorial design analysis is impossible or impractical. Here’s how you can optimize your resources and still achieve a rigorously-supported decision.

Other Designed Experiment Types

You can find notes on other Design of Experiment types here.

Items to Avoid When Conducting a Designed Experiment:

• Unwarranted assumptions of the process.
• Undesirable combinations of the factors.
• Violation of known laws of physics.
• Too large or small design sizes.
• Inappropriate confounding .
• Imprecise measurement.
• Unacceptable prediction error (Type 1 & type 2 errors).
• Undesirable run order.

Introduction to Design of Experiments Video

Full Lecture here:

Six sigma black belt certification design of experiments questions:.

Question: In comparison to a full-factorial design of experiment (DOE) , a traditional, one-at-a-time approach will: (Taken from ASQ sample Black Belt exam .)

(A) Miss interactions (B) Gain efficiencies (C) Save time (D) Cost less

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Comments (6)

I don’t see DOE in IASSC BOK, Am i required as IASSC GB aspirant to study DOE ?

That’s a good question for the IASSC. I would assume not, but organizations have been known to put items not necessarily on their BOK on their exams.

10.15.2020 Very true… in my Green Belt IASSC exam, there were questions I know now were from the Black Belt level.

The wording of the test questions also is a bit challenging… or unclear …or confusing. Isn’t the test difficult enough already?

Dude… I’m hooked now that you applied Design of Experiment to creating a cookie recipe. As a cookie connoisseur myself, I can’t wait to apply DoE in the kitchen!

But really! DoE has been a hard topic for me to swallow until now.

I’m glad!

I do end up using many cooking analogies in my descriptions (Hypothesis Testing overview being another prime example).

While we’re all in different industries and will see different applications of the material, we all have to eat and many of us cook. It’s a helpful shared experience to draw from.

I want to know how much the design of experiments course and study guide cost? Are there any CE’s available and if so how many

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Keyboard Shortcuts

Lesson 1: introduction to design of experiments, overview section  .

In this course we will pretty much cover the textbook - all of the concepts and designs included. I think we will have plenty of examples to look at and experience to draw from.

Please note: the main topics listed in the syllabus follow the chapters in the book.

A word of advice regarding the analyses. The prerequisite for this course is STAT 501 - Regression Methods and STAT 502 - Analysis of Variance . However, the focus of the course is on the design and not on the analysis. Thus, one can successfully complete this course without these prerequisites, with just STAT 500 - Applied Statistics for instance, but it will require much more work, and for the analysis less appreciation of the subtleties involved. You might say it is more conceptual than it is math oriented.

  Text Reference: Montgomery, D. C. (2019). Design and Analysis of Experiments , 10th Edition, John Wiley & Sons. ISBN 978-1-119-59340-9

What is the Scientific Method? Section  

Do you remember learning about this back in high school or junior high even? What were those steps again?

Decide what phenomenon you wish to investigate. Specify how you can manipulate the factor and hold all other conditions fixed, to insure that these extraneous conditions aren't influencing the response you plan to measure.

Then measure your chosen response variable at several (at least two) settings of the factor under study. If changing the factor causes the phenomenon to change, then you conclude that there is indeed a cause-and-effect relationship at work.

How many factors are involved when you do an experiment? Some say two - perhaps this is a comparative experiment? Perhaps there is a treatment group and a control group? If you have a treatment group and a control group then, in this case, you probably only have one factor with two levels.

How many of you have baked a cake? What are the factors involved to ensure a successful cake? Factors might include preheating the oven, baking time, ingredients, amount of moisture, baking temperature, etc.-- what else? You probably follow a recipe so there are many additional factors that control the ingredients - i.e., a mixture. In other words, someone did the experiment in advance! What parts of the recipe did they vary to make the recipe a success? Probably many factors, temperature and moisture, various ratios of ingredients, and presence or absence of many additives.  Now, should one keep all the factors involved in the experiment at a constant level and just vary one to see what would happen?  This is a strategy that works but is not very efficient.  This is one of the concepts that we will address in this course.

• understand the issues and principles of Design of Experiments (DOE),
• understand experimentation is a process,
• list the guidelines for designing experiments, and
• recognize the key historical figures in DOE.

Design of Experiments | DOE

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Rating 4.65 out of 5 (132 ratings in Udemy)

• Fundamentals for Design of Experiments
• Full and Fractional Factorial Designs
• Data Analysis by Mean Comparison, Charts and ANOVA
• Tips and Best Practices on DOE

Design of Experiments ( DOE ) is a statistical tool which helps you to design any experiment properly toward right conclusions. In this beginner online course, you learn by examples and you will know first what is design of experiment and the aim behind it, …

Design of Experiments ( DOE ) is a statistical tool which helps you to design any experiment properly toward right conclusions. In this beginner online course, you learn by examples and you will know first what is design of experiment and the aim behind it, then you will go deeper thus learning how to plan, execute and analyze any experiment properly using this powerful tool. You will also encounter both types of factorial designs here ( Full Factorial Design and Fractional Factorial Design). This tutorial will allow any newbie fully learn how to plan, execute and analyze any experiment properly, thus making the right conclusions out it.

This tool is obligatory for any scientist or engineer, especially those working within the research and development sector and is also essential for those who practice and work with six sigma.

The course starts preparing you in PARTI, thus making you familiar with the DOE definition, noise factors, relations and levels .

Part II then teaches you how to set all the relevant parameters so that you can then plan your experiment using the full or fractional factorial designs.

In Part III, you will learn how to execute your experiment afterwards in a way that you ensure highest accuracy in the results.

In part IV, you will then learn how to analyze the acquired results in your experiments using two different ways, either by charts, or by analysis of variance (Two-way ANOVA).

Excedify - Engineering Online Courses

List of top 7 design of experiments courses, here are the seven top design of experiments courses in 2023:.

Design of Experiments (DoE) course" by Excedify : Learn design of experiments DOE from the basics to advanced concepts and how to apply it to improve any process, product or machine. Gain skills including how to plan and execute experiments, analyze the data using software tools like Minitab. https://www.excedify.com/courses/design-of-experiments-doe  

"Design of Experiments (DOE) Fundamentals" by Quality American on edX: This course covers the fundamentals of DOE, including planning an experiment, selecting the appropriate design, and analyzing the results. It is suitable for beginners and those with some experience in DOE. https://www.edx.org/course/design-of-experiments-doe-fundamentals

"Design of Experiments (DOE) with MINITAB" by Minitab Inc. on Coursera: This course covers the principles of DOE and provides hands-on practice with the MINITAB statistical software. It is suitable for those with some experience in statistical analysis. https://www.coursera.org/learn/minitab-design-of-experiments

"Design of Experiments (DOE) Using JMP" by SAS on Coursera: This course covers the principles of DOE and provides hands-on practice with the JMP statistical software. It is suitable for those with some experience in statistical analysis. https://www.coursera.org/learn/jmp-design-of-experiments

"Design of Experiments (DOE) with R" by DataCamp: This course covers the principles of DOE and provides hands-on practice with the R programming language. It is suitable for those with some experience in statistical analysis and programming. https://www.datacamp.com/courses/design-of-experiments-with-r

"DOE Fundamentals" by The Quality Group on Udemy: This course covers the fundamentals of DOE, including planning an experiment, selecting the appropriate design, and analyzing the results. It is suitable for beginners and those with some experience in DOE. https://www.udemy.com/course/doe-fundamentals/

"Design of Experiments (DOE) for Continuous Improvement" by Lean Six Sigma Academy on Udemy: This course covers the principles of DOE and how they can be applied to continuous improvement projects. It is suitable for those with some experience in statistical analysis and continuous improvement. https://www.udemy.com/course/design-of-experiments-doe-for-continuous-improvement/

Top 7 design of experiments courses

Geometric Dimensioning and Tolerancing GD&T Fundamentals Online Training Course

Design of Experiments (DoE)

Learn design of experiments DOE from the basics to advanced concepts and how to apply it to improve any process, product or machine. Gain skills including how to plan and execute experiments, analyze the data using software tools like Minitab.

Statistical Thinking for Industrial Problem Solving

A free online statistics course.

Back to Course Overview

Design of Experiments

Design of experiments (DOE) is a rigorous methodology that enables scientists and engineers to study the relationship between multiple input variables, or factors , on key output variables, or responses .

In this module, you will learn why designed experiments are better than trial and error and one-factor-at-a-time approaches to gain an understanding of cause and effect relationships and interactions between factors. You will be introduced to several types of designs such as factorial, response surface and custom designs. Finally, you will learn some DOE guidelines and best practices which will help you succeed with experimentation.

Estimated time to complete this module: 3 to 4 hours

Design of Experiments Overview (1:01)

Specific topics covered in this module include:

Introduction to doe.

• What is DOE?
• Conducting Ad Hoc and One-Factor-at-a-Time (OFAT) Experiments
• Why Use DOE?
• Terminology of DOE
• Types of Experimental Designs

Factorial Experiments

• Designing Factorial Experiments
• Analyzing a Replicated Full Factorial
• Analyzing an Unreplicated Full Factorial

Screening Experiments

• Screening for Important Effects
• A Look at Fractional Factorial Designs
• Custom Screening Designs

Response Surface Experiments

• Introduction to Response Surface Designs
• Analyzing Response Surface Experiments
• Creating Custom Response Surface Designs
• Sequential Experimentation

DOE Guidelines

• Introduction to DOE Guidelines
• Defining the Problem and the Objectives
• Identifying the Responses
• Identifying the Factors and Factor Levels
• Identifying Restrictions and Constraints
• Preparing to Conduct the Experiment

Serving technical professionals globally for over 75 years. Learn more about us.

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Design and Analysis of Experiments

Download the Course Schedule

Explore innovative strategies for constructing and executing experiments—including factorial and fractional factorial designs—that can be applied across the physical, chemical, biological, medical, social, psychological, economic, engineering, and industrial sciences. Over the course of five days, you’ll enhance your ability to conduct cost-effective, efficient experiments, and analyze the data that they yield in order to derive maximal value for your organization.

Course Overview

THIS COURSE MAY BE TAKEN INDIVIDUALLY OR As part of THE  PROFESSIONAL CERTIFICATE PROGRAM IN BIOTECHNOLOGY & LIFE SCIENCES .

This program is planned for those interested in the design, conduct, and analysis of experiments in the physical, chemical, biological, medical, social, psychological, economic, engineering, or industrial sciences. The course will examine how to design experiments, carry them out, and analyze the data they yield. Various designs are discussed and their respective differences, advantages, and disadvantages are noted. In particular, factorial and fractional factorial designs are discussed in greater detail. These are designs in which two or more factors are varied simultaneously; the experimenter wishes to study not only the effect of each factor, but also how the effect of one factor changes as the levels of other factors change. The latter is generally referred to as an interaction effect among factors.

The fractional factorial design has been chosen for extra-detailed study in view of its considerable record of success over the last 30 years. It has been found to allow cost reduction, increase efficiency of experimentation, and often reveal the essential nature of a process. In addition, it is readily understood by those who are conducting the experiments, as well as those to whom the results are reported.

The program will be elementary in terms of mathematics. The course includes a review of the modest probability and statistics background necessary for conducting and analyzing scientific experimentation. With this background, we first discuss the logic of hypothesis testing and, in particular, the statistical techniques generally referred to as Analysis of Variance. A variety of software packages are illustrated, including Excel, SPSS, JMP, and other more specialized packages.

Throughout the program we emphasize applications, using real examples from the areas mentioned above, including such relatively new areas as experimentation in the social and economic sciences.

We discuss Taguchi methods and compare and contrast them with more traditional techniques. These methods, originating in Japan, have engendered significant interest in the United States.

All participants receive a copy of the text, Experimental Design: with applications in management, engineering and the sciences , Duxbury Press, 2002, co-authored by Paul D. Berger and Robert E. Maurer, in addition to extensive PowerPoint notes.

Participant Takeaways

• Describe how to design experiments, carry them out, and analyze the data they yield.
• Understand the process of designing an experiment including factorial and fractional factorial designs.
• Examine how a factorial design allows cost reduction, increases efficiency of experimentation, and reveals the essential nature of a process; and discuss its advantages to those who conduct the experiments as well as those to whom the results are reported.
• Investigate the logic of hypothesis testing, including analysis of variance and the detailed analysis of experimental data.
• Formulate understanding of the subject using real examples, including experimentation in the social and economic sciences.
• Introduce Taguchi methods, and compare and contrast them with more traditional techniques.
• Learn the technique of regression analysis, and how it compares and contrasts with other techniques studied in the course.
• Understand the role of response surface methodology and its basic underpinnings.
• Gain an understanding of how the analysis of experimental design data is carried out using the most common software packages.
• Be able to apply what you have learned immediately upon return to your company.

Who Should Attend

This course is appropriate for anyone interested in designing, conducting, and analyzing experiments in the biological, chemical, economic, engineering, industrial, medical, physical, psychological, or social sciences. Applicants need only have interest in experimentation. No previous training in probability and statistics is required, but any experience in these areas will be useful.

Program Outline

Class runs 9:00 am - 5:00 pm every day.

• Introduction to Experimental Design
• Hypothesis Testing
• ANOVA I, Assumptions, Software
• Multiple Comparison Testing
• ANOVA II, Interaction Effects
• Latin Squares and Graeco-Latin Squares
• 2K Designs (continued)
• Confounding/Blocking Designs
• Confounding/Blocking Designs (continued)
• 2k-p Fractional-Factorial Designs
• 2k-p Fractional-Factorial Designs (continued)
• Taguchi Designs
• Taguchi Designs (continued)
• Orthogonality and Orthogonal contrasts
• 3K Factorial Designs
• Regression Analysis I
• Regression Analysis II
• Regression Analysis III & Introduction to Response Surface Modeling
• Response Surface Modeling (continued), Literature Review, Course Summary

AMONG THE SUBJECTS TO BE DISCUSSED ARE:

• The logic of complete two-level factorial designs
• Detailed discussion of interaction among studied factors
• Large versus small experiments
• Simultaneous study of several factors versus study of one factor at a time
• Fractional experimental designs; construction and examples
• The application of hypothesis testing to analyzing experiments
• The important role of orthogonality in modern experimental design
• Single degree-of-freedom analysis; pinpointing sources of variability
• The trade-off between interaction and replication
• Response surface experimentation
• Yates' forward algorithm
• The reliability of estimates in factorial designs
• The usage of software in design and analysis of experiments
• Latin and Graeco-Latin squares as fractional designs; examples
• Designs with all studied factors at three levels
• The role of fractional designs in response surface experimentation
• Taguchi designs
• Incomplete study of many factors versus intensive study of a few factors
• Multivariate linear regression models
• The book and journal literature on experimental design

Testimonials

The type of content you will learn in this course, whether it's a foundational understanding of the subject, the hottest trends and developments in the field, or suggested practical applications for industry.

How the course is taught, from traditional classroom lectures and riveting discussions to group projects to engaging and interactive simulations and exercises with your peers.

What level of expertise and familiarity the material in this course assumes you have. The greater the amount of introductory material taught in the course, the less you will need to be familiar with when you attend.

Understanding Design of Experiments (DoE) in the Pharmaceutical Industry

12/13 October 2021, Heidelberg, Germany

Course No. 18390

Dr. Raphael Bar

BR Consulting

This course will explain the basics of DoE with practicing with factorial and fractional DoE as well as DoE by RSM If you have no or little previous knowledge with DoE, you will learn how to set up an experimental design and how to explore the effect of factors that influence either a development/production process or an analytical procedure  while taking into account interactions between the factors.   To better understand and assimilate the DoE  principles, you will learn first to calculate the main effects and factors  interactions by simple manual calculations (with Excel). Then, you will learn how to use Minitab  software program to create a variety of DoE designs, analyze and interpret them. Multiple exercises and examples from pharmaceutical development and laboratory analysis such as robustness studies will be solved by the participants. The participants will learn how to interpret the output of a DoE programme.

With FDA´s Process Validation Guidance for Industry from 2011 and the Annex 15 Revision 2015 process validation has changed to a life cycle. And the life cycle starts with the development which delivers process knowledge and the critical process parameters. To get there the FDA mentions „Design of Experiments“ (DoE). Therefore, DoE is a tool for implementing the process validation life cycle.   Also, ICH Guidelines Q8 (Pharmaceutical Development) and ICH Q9 (Quality Risk Management) speak about DoE as a tool, also in relation to Quality by Design (QbD= approaches).   Meanwhile, DoE is also common practice in other pharmaceutical areas, i.e. in the analytical development or as a CAPA measure for process optimisation.

Target Group

The addressees of the event are employees from the development, quality control lab and  quality assurance departments who are using DoE or wanted to use DoE in the future. We address also GMP auditors and inspectors and validation personnel also involved in DoE.

Each participant should bring a laptop with Excel and a previously downloaded 30 day free-trial Minitab 19 program from http://www.minitab.com . This program should be downloaded on a laptop a few days before the beginning date of the course and verified that it works on the laptop.

Seminar Programme as PDF

• DoE and Quality by Design
• Regulations (EU and FDA)
• A factorial experiment
• DoE vs one-at-a-time experiment
• Where is DoE applied in development and validation of analytical methods
• Where is DoE applied in manufacturing process
• development and validation
• Factorial experiments (categorical and numeric factors)
• Two and three factorial designs
• Manual calculation of main effects
• Manual calculation of interactions
• What is an orthogonal DoE
• Exercises with Excel
• Basic structure of Minitab software
• Input of data
• Running a DoE
• Plotting output results
• Practicing with Minitab
• Diagnostics for goodness of fit to model
• Deviations from normality plot
• Making replicate experiments
• Adding experiments at centre points
• Using known variability
• Two factor full DoE experiments
• Interactions between two factors
• Plotting Main effects and Interactions
• Interpretation of DoE Minitab output
• Does the linear fit the model?
• Significance with p values
• General full factorial DoE
• Exercises in interpretation of Minitab outputs
• Two and three factor experiments with Minitab
• Aliasing in DoE experiments
• Resolution of  DoE experiments
• 4-7 fractional factorial DoE
• Blackett-Burmann designs
• Definitive screening design
• Robustness of HPLC method with fractional DoE
• Optimisation of a process with fractional DoE
• 22 factorial experiments with RSM
• Contour plot
• Surface plot
• Concept of Design Space
• Exercises: optimization of drug solubility with RSM design
• Effect of process parameters on dissolution assay and variability
• Why we use DoE in the pharmaceutical development?
• Example: DoE for formulation selection / optimization
• Example: DoE for manufacturing process optimization
• DoE vs “traditional” approach – when to use which?
• Screening experiments
• Fractional experiments
• Full factorial experiments
• Optimisation experiments: Surface Response Methodology
• Design Space versus Proven Operating Range (PAR)
• Normal Operating Range (NOR)
• Robustness of experiments of a process/method

This course is part of the GMP Certification Programme "ECA Certified Validation Manager" Learn more

This training/webinar cannot be booked. Send us your inquiry by using the following contact form.

To find alternative dates for this training/webinar or similar events please see the complete list of all events .

For many training courses and webinars, there are also recordings you can order and watch any time. Just take a look at the complete list of all recordings .

Please contact us: Tel.: +49 6221 8444-0 E-Mail: [email protected]

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“Fantastic course – I really enjoyed the interactive structure & greatly appreciate social activity.”

Anthony Cummins, Sebela Pharmaceuticals, Ireland GMP Auditor Practice, September 2023

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Design of Experiments | DOE

via Skillshare Help

This course may be unavailable.

Design of Experiments ( DOE ) is a statistical tool which helps you to design any experiment properly toward right conclusions. In this beginner online course, you learn by examples and you will know first what is design of experiment and the aim behind it, then you will go deeper thus learning how to plan, execute and analyze any experiment properly using this powerful tool. You will also encounter both types of factorial designs here ( Full Factorial Design and Fractional Factorial Design). This tutorial will allow any newbie fully learn how to plan, execute and analyze any experiment properly, thus  making the right conclusions out it.

This tool is obligatory for any scientist or engineer, especially those working within the research and development sector and is also essential for those who practice and work with six sigma.

The course starts preparing you in PART I, thus making you familiar with the DOE definition, noise factors, relations and levels .

Part II then teaches you how to set all the relevant parameters so that you can then plan your experiment using the full or fractional factorial designs.

In Part III, you will learn how to execute your experiment afterwards in a way that you ensure highest accuracy in the results.

In part IV, you will then learn how to analyze the acquired results in your experiments using two different ways, either by charts, or by analysis of variance (Two-way ANOVA).

• Sec01 Lec00 Introduction
• Sec01 Lec01 Definition
• Sec01 Lec02 Relations
• Sec01 Lec03 Error
• Sec01 Lec04 Levels
• Sec02 Lec01 Set all Parameters
• Sec02 Lec02 Full Factorial Design
• Sec02 Lec03 Fractional Factorial Design
• Sec03 Lec01 Execute the Experiment
• Sec04 Lec01 Analyze by Charts
• Sec04 Lec02 Analyze by ANOVA

Ali Suleiman

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