design of experiments workshop

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Design and Analysis of Experiments

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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.

Design of Experiments Workshop

The program is offered in both Classroom and Self-paced Digital Learning formats.

For details of Self-paced Digital Learning, click here .

Workshop Overview

Structured Experimentation or Design of Experiments (DOE) helps Product Engineers to develop and refine designs. It is simply not possible to develop optimal designs that deliver right product performance without understanding the relationship between dependent and independent factors (& within independent factors). Unstructured experimentation consumes indefinite resources and time that no organizations have.

This is true for both Engineering Product Managers and Service Product Managers.

Similarly, both Engineering and Service Process Managers cannot optimize process parameters without conducting structured experiments.

Quality Managers have to constantly improve the quality of product, its reliability and that cannot happen without improvising design in a structured manner.

Learning Objectives

This Design of Experiments (DOE) workshop will help Product and Process Engineering Managers from both Service & Engineering Sectors to learn DOE from scratch and apply structured experimentation methods.

Learning Outcomes:

At the end of the workshop, participants will be able to:

• Develop an understanding about the concepts of structured experimentation 
• Learn how to conduct a 2K design of experiments from scratch

Target Audience:

Mid to Senior Management Professionals who are required to develop and optimize new products and processes:

• Product Managers
• Process Managers
• Quality Managers
• Six Sigma Black Belts

Broad Curriculum:

Broad scope is include here:

• One Factor At a Time (OFAT) Experiments – Pros and Cons
• Solving problems in experiments – different types & purpose
• Multi-vary charts
• Construction of ANOVA Table in Excel & Minitab
• DOE 2K Factorial Design – Creating Orthogonal Arrays in Excel and Minitab
• Terms – Factors, Levels, Runs, Trials, Fixed Factor, Random Factor, Block, Replication, Repeats, Center
• Main Effects and Interaction Effects
• Effect, Estimate, and Sum of Square Calculation for 2K
• Fractional Factorial Experiments – Resolution and Confounding factors
• Taguchii DOE – Why, When, How – S/N Ratio, Inner/Outer Arrays
• RSM – Response Surface Methodology
• Addressing practical experiment nuances
• Performing Design of Experiments Using MS Excel & Minitab
• DOE Simulation game

All the methods to be covered in the training will be Application, Analysis or Synthesis as per Bloom’s taxonomy.

In this workshop, learning will be through interactive group activities.

To conduct Design of Experiments (DOE) Workshop training in Chennai, Bangalore, Mumbai, Delhi and across India and also to know more about the curriculum, structure & customization to your need, contact us .

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On-Demand Webinars

On-Demand Workshop

Getting started with design of experiments (doe).

  This series of three one-hour workshops provides inspirational examples of how to use DOE to bring products to the market, including product design, discovery, and development. It will also address process development, scale-up, transfer, and analytical method development.

In this fun, engaging online training program, you will learn how to use experimentation more efficiently so that you can achieve better outcomes faster.

By allowing you to plan your experimental work, DOE helps you:

• Build a deeper understanding of how your product, system, or process performs and the ways in which various inputs affect performance.
• Define better, less expensive solutions. By exploiting the deeper knowledge gained, you can determine how to consistently achieve top performance at a lower cost.
• Reduce your total experimental effort per project by an average of 50%. With DOE, you can determine the best and smallest set of experimental combinations needed to address your questions upfront, thus avoiding budget overruns and the need to ask for more time and experimental resources to find a solution.

Despite the wins offered by DOE, many people working in commercial research, development, and manufacturing have little experience with it. Whether it is due to a lack of awareness or a lack of know-how, the best way to gain an appreciation for what DOE can offer is to experience it firsthand.

This series of three one-hour workshops arms you with the knowledge and the resources you need to get started with DOE. 

Key learning points:

• Why DOE offers a faster, more predictable, and better-informed way of learning than other experimentation approaches.
• Common terminology and types of DOE.
• How to design, analyse, and present the findings of your first DOEs.
• Hands-on exercises, expert advice, and feedback to deepen your understanding of DOE.

This three-part workshop has been designed for scientists and engineers who want to make their experimentation more efficient and effective. Follow along in your own time with  JMP Statistical Discovery software  and give your learning a valuable boost.

Presented by the Royal Society of Chemistry

Register now for this free webinar

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Design of Experiments

Measurement System Analysis

Take the class. Learn the tools. Become a more effective problem solver. Get lifetime support for free.

Objective Experiments teaches engineers, chemists, and other process and product professionals how to use Design of Experiments (DOE or DoX) and evaluate measurement systems. You'll learn practical tools to reduce the time it takes to get a product from the lab to production. Plus, the time involved in getting answers decreases—and quality increases.

Certification is available.

Our workshops are designed to help you keep your edge. You'll learn how to:

• Turn theory into practice.
• Make something work—or work better.
• Run fewer experiments—collect less data—and get better results.
• Make the manufacturing process more efficient.
• Remedy a failing part.
• Stop the costly trial and error experiments and move on to solutions.

All of our workshops come with free support—for life!

• Call your instructor to ask questions about what you've learned.
• Email your designed experiments for review.
• Have your experimental results reviewed.
• Brainstorm your plans before running your experiment.

We also offer consulting services in these areas.

Theories have limitations. Data can be trusted.

Our workshops offer practical, real‐world‐ready tools, taught by knowledgeable, friendly experts, who speak in plain English—not jargon.

Objective Experiments (DOE, DoX)

Which is right for me? Objective Experiments Strategies Objective Experiments Strategies for Biotech Objective Experiment Strategies for Chemistry Customized Design of Experiments Workshop

Which is right for me? Practical Measurement System Analysis—EMP (EMP) Practical Measurement System Analysis—GR&R (Gage R&R)

Background Fundamentals

Advanced Problem Solving

Not sure which course is right for you? Contact us at (888) 764‐3958 for more information and to schedule your workshop.

JMP: Introduction to Design of Experiments

Full course description.

Design of experiments (DOE) is the design of a process aimed to describe and explain sources of variability to improve product and process development. In simpler terms, experimentation is used to determine causation. This workshop begins with an overview of basic statistical concepts, the scientific method, and the statistical contributions to the scientific method. A simple example is used to walk through the scientific method while demonstrating statistical thinking, methodology and analysis. Basic principles of DOE are then covered leading into specific types of designs. Completely Randomized Designs (CRD) and Randomized Complete Block Designs (RCBD) are introduced. The workshop ends with the development of these designs built in JMP software. This workshop takes approximately 1 hour and 45 minutes to complete.

• Time to complete: 1 hour 45 minutes
• Audience: UT Community
• Credit: none
• Offered by: Office of Innovative Technologies , UTK

Design of Experiments Specialization

Program fee.

Well-designed experiments are a powerful tool for developing and validating cause and effect relationships between factors when evaluating and improving product and process performance. Deliberately changing the input variables to a system allows for observation and identification of the reasons for the change that may be observed in the output responses. Design of Experiments can identify important interactions that are usually overlooked when experimenters vary only one factor at a time (OFAT experimentation). Unfortunately, OFATS are still widely used in many experimental settings.

Design of Experiments can be used in a variety of experimental situations. This program is suitable for participants from a broad range of industries, including electronics and semiconductor, automotive, aerospace, chemical and process, pharmaceutical, medical device, and biotechnology. There are also many business and commercial applications of designed experiments, including marketing, market research, and e-commerce. Program participants will learn how to run effective and strong experiments using modern statistical software. 

Program Topics

We are proud to offer the Design of Experiments Specialization through the Coursera platform. The course is instructed by Dr. Doug Montgomery, a Regents Professor of industrial engineering and statistics in the Ira A. Fulton Schools of Engineering at ASU, and an expert in experimental design. Dr. Montgomery has taught academic courses on experimental design for over 40 years, and his Design of Experiments textbook, in its 10th edition and utilized in the specialization, is the most widely used textbook on the subject in the world. He has also led numerous engagements with Design of Experiments, teaching the course and consulting for more than 250 companies, including Motorola, Intel, Boeing and IBM. Drawing from these commercial experiences, Montgomery provides participants with an accurate understanding of modern approaches to using Design of Experiments.

The specialization is offered in a four-course format, with each course comprising three-to-four units and, in most courses, an applied project to demonstrate the tools and concepts learned. Accessible entirely online, the courses can be attempted at your own pace. We recommend completing one unit per week.

Live Fireside Chats

Unique to this specialization, Dr. Montgomery hosts monthly fireside chats using Zoom where he discusses different topics in the areas and application of Design of Experiments concepts. Drawing from his expertise and vast network, Dr. Montgomery is frequently joined by a special guest and expert in the topic area being discussed. Planned for the second Wednesday of every month, these chats are open to the public for viewing. During this time, viewers can ask questions to Dr. Montgomery and his guest related to Design of Experiments’ concepts, application, and situational experiences. The previous fireside chat recordings can be found here .

If you would like information on how to join the monthly live fireside chats, please contact us at [email protected] . 

Specialization Courses

Experimental Design Basics

Unit 1: Getting Started and Introduction to Design and Analysis of Experiments

Unit 2: Simple Comparative Experiments

Unit 3: Experiments with a Single Factor - The Analysis of Variance

Unit 4: Randomized Blocks, Latin Squares, and Related Designs

Factorial and Fractional Factorial Designs

Unit 1: Introduction to Factorial Design

Unit 2: The 2^k Factorial Design

Unit 3: Blocking and Confounding in the 2^k Factorial Design

Unit 4: Two-Level Fractional Factorial Designs

Response Surfaces, Mixtures, and Model Building

Unit 1: Additional Design and Analysis Topics for Factorial and Fractional Factorial  Designs

Unit 2: Regression Models

Unit 3: Response Surface Methods and Designs

Unit 4: Robust Parameter Design and Process Robustness Studies

Random Models, Nested and Split-Plot Designs

Unit 1: Experiments with Random Factors

Unit 2: Nested and Split-Plot Designs

Unit 3: Other Design and Analysis Topics

If you would like to take all four courses, we recommend taking them in the above order. Each subsequent course will build on materials from the previous.

Learning Outcomes

Learning outcomes are organized by course. By completing all four courses, participants will:

• Organize a step-by-step process for designing, conducting and analyzing that experiments will lead to successful results
• Collect, analyze, and interpret data to provide the knowledge required for project success
• Demonstrate effective use of a wide range of modern experimental tools that enable practitioners to customize their experiment to meet practice resource constraints
• Use the analysis of variance, ANOVA, to analyze data from single-factor experiments with several factor levels

Earning a Certificate

The Design of Experiments Specialization is offered 100% online and through the Coursera platform. Participants can complete any of the four courses to receive a certificate of completion, and can complete all four to receive the specialization, thus mastering experimental design.

Who Should Enroll

These courses are open to any that are interested in learning about experimental design tools. Any person working in modern industry can apply the tools acquired in these courses to their current and future positions.

Pre-requisites

We recommend working knowledge of a basic statistics course. The basic fundamentals will be covered in the Experimental Design Basics course.

Textbook and Software

The textbook used throughout the specialization is  Design and Analysis of Experiments, 10th Edition  by Dr. Douglas C. Montgomery. Students are recommended to purchase or rent the textbook, but are not required. The courses within the specialization also utilize JMP statistical software. Participants have access to a free trial in the courses.

Contact Information

For more information on professional programs or certifications contact:

Professional & Executive Education [email protected] (480) 727-4534

or fill out our "Request for Information" form at the bottom of the page. 

Request Information

Basic Statistics and Design of Experiments (DOE)

At-a-glance

Format	On-campus or online (instructor-led)
Cost	$895
Duration	3 days (on-campus), 2 weeks (online)
Availability	Summer, on-demand
Prerequisites	None
CEU's or PDH's	2.0 CEU's

How can you determine if a new process performs better than the original? How can you be confident that a new drug or vaccine will be safe and effective?  How can you best determine which market(s) to target a new product?  Reliable answers to questions like these are found by collecting data from properly designed tests, trials, or experiments.  Too often organizations use informal methods (eg. opinions) or poorly designed experiments to make important decisions.  Experimental design is a way to carefully plan experiments in advance so that your results are both objective and valid. Effective experimental design and analysis are critical to improving products and processes, reducing waste, lowering costs, and improving productivity.

This how-to workshop focuses on understanding the fundamental elements of experimental design and how to apply experimental design to solve real problems.  A statistical software package, Minitab®, is used to help create designs, analyze data, and interpret results more efficiently and effectively. We will examine how to use proven statistical methods to properly design, collect, and analyze experimental data.

1) On-campus option

The on-campus workshop is offered over a 3-day period.  Each topic is reinforced through discussion, case studies, exercises, and simulations.  Exercises and simulations are conducted in our computer lab using Minitab statistical software.  Outside of class, participants are welcome to join online discussions with the instructor and other students. 

2) “Blended online” option

The online instructor-led workshop covers the same material as the 3-day on-campus program but is available over a 2-week period through our online Learning Management System, providing flexibility to your schedule.  Learning materials include pre-recorded lectures, case studies, exercises, and simulations.  We keep you actively engaged through online discussions with the instructor and other students. (If you don’t have access to Minitab software, go to www.Minitab.com to download a free trial copy).

• One-factor-at-a-time experiments - what your competitors want you to use
• The importance of a strategy of experimentation
• Summarizing results in experiments - main effects and interactions
• Two-level designs for 3-5 factors - the 2k designs
• Variation when conditions are held constant -  a review
• The importance of randomizing and blocking in experiments
• Handling variation naturally in two-level designs
• The value of non-replicated experiments
• How to select the correct number of runs for an experiment
• The role of center points and how to use them correctly
• Fractional factorial (2k–p) designs - studying 5-15 factors, or more efficiently
• The use of fold-over designs to gain more information
• Case studies, simulations, and exercises
• Using Minitab® to, help create designs and interpret results

Who Should Attend

Professionals in engineering, R&D, manufacturing, quality, and marketing who plan, conduct, analyze, and interpret tests to evaluate the impact of key parameters on the performance of products and processes.  Learn how to select the most effective design to answer important questions that maximize results while minimizing time and effort.

Certification

A “certificate of completion” from RIT’s Center for Quality and Applied Statistics is issued upon completing the program including all assigned exercises. 

Contact us at [email protected] or 585-475-6990 for future dates.

Pricing and Registration

Individual Registration: $895

Discounts (only one discount per person)

• Members of MedTech or GRQC: $806 (10%)
• RIT Alumni, Employees, and Immediate Family Members: $716 (20%)
• Full-Time College Students (all institutions): $716 (20% discount).  Contact [email protected] or 585-475-6990 for a coupon code.

Group Registration (no other discounts apply) *

• 2 people: $806 per person (10%).  RIT Alumni and Employees - please register as 2 individuals
• 3+ people: $716 per person (20%)

Please register by clicking on the appropriate program below. You may also register by contacting 585-475-6990 or [email protected] .

Accommodations: Rooms may be reserved at the Radisson Hotel adjacent to the RIT campus. Information on how to make a reservation will be emailed to you.

Cancellation Policy

Refunds will be issued for cancellations received and confirmed at least 10 business days prior to the program date. No refunds will be issued for cancellations received after that date. Please email [email protected] with any questions.

Frequently Asked Questions

Are there any prerequisites for this workshop?

No.  If Minitab is new to you or you have not used it in a while, we offer a short “Introduction to Minitab” workshop, but this is not required. 

What if I don’t have access to Minitab software?

On-campus participants will utilize Minitab in our computer lab.  Online students may go to www.minitab.com and download a free trial version which will last the duration of the workshop.

If I register for the on-campus workshop, can I switch to the online version or complete the remaining part of the workshop online?

Yes. If you are unable to come to campus on any of the workshop days, you may instead access the materials online and complete the workshop.  Online discussions are available to all participants whether they are on-campus or online.

What does the term “instructor-led” mean?

Our online courses and workshops are virtually identical in content to our “live” on-campus courses, but they are conducted online and are flexible to your personal schedule.  Unlike self-paced online training, we keep you actively engaged with our instructors and other program participants.  Instead of in-class discussions, you will participate in online discussions.

I prefer “live” over online classes but I would like access to online materials. Will I have access to these materials even if I register for the live class?

Yes.  Our online course environment is available to our on-campus students, who are also welcome to participate in online discussions for the duration of the online workshop.  If you aren’t comfortable with online learning, register for the on-campus version but feel free to check-out the online materials.  All registrants are provide pdf’s of course materials which remain available after the workshop ends.

What if I don’t have time to submit the required exercise before the workshop ends?

If you do not complete the required material and exercises, you will not receive a “certificate of completion” from RIT.  The workshop includes about 24 hours or class material along with exercises using Minitab, and online participants are given two weeks to meet all requirements, so be sure to budget your time accordingly.

Can I get a refund if I am unable to meet the requirements in the allotted time?

No.  Participants are offered the flexibility of completing the workshop on-campus or online.

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Workshop: Your guide to solving complex problems by mastering Design of Experiments

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Mastery of the fundamentals of statistical design and analysis of experiments enables you to ensure effectiveness and efficiency in your empirical learning across a range of science and engineering situations – but you may soon have questions about how to solve more complex problems using Design of Experiments (DOE).

In this workshop, you will learn about designs that go beyond classical factorial and fractional factorial designs and explore how optimal designs can be tailored to the practical constraints of your process or system.

You will have the opportunity to discover unique methods for maximising your insight when time is a variable or when you have curve responses. You will also learn more about sequential approaches to experimentation, including newer approaches like Bayesian optimisation.

This three-part series is intended to expand existing knowledge of DOE concepts. Participants are encouraged to first learn the basics of DOE by registering for the on-demand workshop ‘Getting started with Design of Experiments’ .

Register to view the three-part series

Watch the recordings

Programme benefits

This three-part workshop – has been designed for scientists and engineers who want to make their experimentation more efficient and effective. Follow along in your own time with  JMP’s Statistical Discovery Software  and give your learning a valuable boost.

Support when and where you need it

JMP experts know DOE and have years of experience putting these techniques into practice. During the workshop, the JMP team provide real-time assistance to attendees, giving answers to the most popular questions. You’ll see the theory in the workshop – then it’s up to you to test it out.

Register now

Part 1: optimal/custom designs.

17 October 2023, 10.00 BST

You don’t need to force your problem into a standard design; optimal DOE gives you experiments that are bespoke to your requirements.

This session will include:

• Recap of basic DOE: factorial, fractional factorial, RSM and DSD designs
• A case study example of custom design
• Introduction to optimality criteria (D, I, A, Alias)
• Where to find additional resources

If you want to learn more:

• Download the free JMP trial in preparation for the workshop
• Homework task for day 1
• JMP data file for day 1 homework

Part 2: Curve or Functional Responses

18 October 2023, 10.00 BST

You can model curve or ‘functional’ responses including spectra and chromatograms.

• Examples where time is a variable
• Additional examples of curve responses, including spectra and chromatograms
• Functional and nonlinear modelling solutions for curve responses
• Homework task for day 2

Part 3: Sequential Design of Experiments

19 October 2023, 10.00 BST

Sequential experimentation ensures efficiency.

• The efficiency of sequential experimentation
• Scoping, screening, optimisation, robustness
• Bayesian optimisation

Download your free trial software

This programme includes hands-on exercises, which you can complete on your own computer using JMP’s Statistical Discovery Software. You will need to download and activate a free trial of this software to follow along. Once activated this trial will last for 30 days. You can get your free trial direct from JMP’s website .

Meet your JMP experts

Marco salfi.

Marco Salfi joined JMP as a Senior Systems Engineer after spending many years in the oil and gas industry. He has held various roles with ExxonMobil in Italy, Belgium, and the United States, with a primary focus on manufacturing and supply chain. Throughout his career, Marco has always prioritised making data-driven decisions. He holds a master’s degree in energy engineering from Politecnico di Milano, Italy.

Owen Jonathan

Owen Jonathan is an Associate Systems Engineer at JMP. His role involves identifying critical business issues of UK customers and guiding them in adopting data-driven solutions within their organisations. Prior to joining JMP, Owen obtained a master’s degree in systems and synthetic biology from Imperial College London. He first joined JMP as an intern, where he identified DOE applications in the field of biotechnology and delivered DOE workshops with relevant case studies to synthetic biologists.

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Targeted topics provide faster insights, leading to immediate results! Our interactive learning experience promotes real understanding of DOE methods and tools.

Stat-Ease 360 Software

SE360 augments Design-Expert software with integration with Python scripting for data analysis, new Custom Graphs, and Gaussian process models for zero-error and noisy responses.

Hosted Network Licensing Available

Our Hosted Network License allows you to run the software on multiple devices without requiring an on-premises license server. Ask us for a quote!

Design-Expert Software

Best in class design of experiments software makes R&D easy with an intuitive interface and amazing graphics. Whether you are new to DOE or a seasoned pro, you'll make breakthrough improvements with Design‑Expert software.

New-User Intro to Stat-Ease® 360 Software

A complete introduction to Stat-Ease DOE software, this free webinar provides new users with the tools needed to start planning your DOE.

A better product. An improved process. Stat-Ease, armed with an expertise in design of experiments, has been helping people make breakthroughs since 1985. Find out how our industry-leading software and multi-modal training can help you improve your bottom line.

Make the most from every experiment sm

Stat-Ease Software

Powerful tools for experimenters.

Make breakthrough improvements with Stat-Ease 360 or Design-Expert® software. Our software makes it incredibly simple to apply powerful multifactor testing tools.

Whether you are new to design of experiments or a pro, our straightforward, easy-to-use interface sits on top of a powerful statistical engine. You will not only screen for vital factors, but also locate ideal process settings for top performance & discover optimal product formulations.

The latest releases provide new Custom Graphs, a multi-response Analysis Summary view, and the ability to Import/Export data to Excel. View the software brochure here .

For information on the latest version, view our change log .

Hands-on DOE Training

Learning at your convenience.

Learn design of experiments (DOE) your way! Stat-Ease offers premium training options for Design-Expert and Stat-Ease 360 alongside general DOE education.

Take advantage of our online eLearning modules designed for self-directed DOE learning, then work through our step-by-step software tutorials . Learn more by registering for a free one-hour webinar .

For comprehensive DOE education, enroll in our highly interactive instructor-led workshops for either process DOE or mixture formulations. Experienced experts lead our hands-on, case-study based courses. Whatever your educational style, we offer training that meets your needs.

Version 23.1.6 Now Available

All subscribers of Stat-Ease software or users with an active Annual Support & Maintenance plan are eligible to install and run the latest versions of Stat-Ease 360 or Design-Expert as they are released. Check our changelog for the details on what's new, and download the latest version from the Licenses tab in your account .

Full 2024 Workshop Schedule Available

Our public workshop schedule for the rest of 2024 is live! Register now for either our Mixture Design for Optimal Formulations or our Modern DOE for Process Optimization course. If you can't make the scheduled time, or if you have more than 5 people to train, contact us for options . We look forward to helping you save time and money with design of experiments!

Tips and tools for modeling counts most precisely

Back in 2021, we added Poisson regression to Stat-Ease 360 and Design-Expert software. Mark's latest blog delves into how this alternative modelling method is useful for experiments handling counts. Read it today to learn how!

Biostatistics and Design of experiments

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Note: This exam date is subject to change based on seat availability. You can check final exam date on your hall ticket.

Page Visits

Course layout, instructor bio.

Prof. Mukesh Doble

Course certificate.

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SWAYAM SUPPORT

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Course Status :	Completed
Course Type :	Elective
Duration :	8 weeks
Category :
Credit Points :	2
Undergraduate/Postgraduate
Start Date :	22 Jan 2024
End Date :	15 Mar 2024
Enrollment Ends :	05 Feb 2024
Exam Registration Ends :	16 Feb 2024
Exam Date :	24 Mar 2024 IST