Fundamentals of DOE with a Full Factorial Simulation
Design of Experiments (DOE) is one of the most powerful tools in the continuous improvement toolkit and one of the most misunderstood. It’s not as intimidating as it sounds. You can learn it. And once you do, it starts to feel surprisingly logical.
In this 3-part Master Series, participants will go beyond learning DOE terminology and into putting DOE to practice by running a full factorial design using an interactive trebuchet simulation. You won’t just see the math. You’ll make decisions, collect data, analyze results, and learn how to turn DOE output into practical improvement actions.
This series is designed for professionals who want to go from “I've heard of DOE” to “I’m starting to get dangerous designing and interpreting experiments in my own work.” If you are ready to design, execute, and interpret a designed experiment from end to end – don't wait! Enroll today.
Early Bird Pricing: Register Now and Receive $100 Off
Use Coupon Code DOEWINTER26 to receive $100 of when you register for this 3-week master class.
*Early Bird pricing discount available with coupon code only. Available now until 1/07/2026. Not available with any other promotion, discount, or coupon. Call for group pricing.
Fundamentals of Design of Experiments Training Sessions
January 15, 2025
Session 1: Foundations of Design of Experiments
- Review common approaches to experimentation and their limitations
- Explore where Design of Experiments (DOE) fits and the problems it uniquely solves
- Learn key concepts including factors, levels, and responses
- Run a hands-on simulation and experience experimentation firsthand
January 22, 2025
Session 2: Designing a Full Factorial Experiment
- Learn how to identify factors and levels
- Define practical ranges and constraints
- Discuss key DOE fundamentals like randomization, replication, and blocking
- Translate a process problem into an experimental plan using EngineRoom's 'DOE Planning Worksheet'
- Build a full factorial design
January 29, 2025
Session 3: Running a DOE and Analyzing Results
- Run a full factorial DOE using the trebuchet simulation
- Build and interpret a DOE model by estimating main effects and interaction effects
- Evaluate model quality using summary statistics
- Optimize within practical process constraints or to hit a defined performance target
Maximize Your Data Analysis Skills with EngineRoom®
Included in your training, you'll receive full access to EngineRoom, our comprehensive data analysis and statistical software designed specifically for quality and process improvement professionals. With 60 days of unlimited access, you can dive deep into EngineRoom's powerful features, exploring tools and techniques that enhance your analytical skills.
- Comprehensive Data Analysis: Utilize an extensive suite of tools, including statistics, diagramming, documentation and process modeling.
- Process Playground: Experiment and simulate scenarios to see the impact of different process improvement techniques with EngineRoom's process modeling software.
- Smart Assistant: Hexie, our AI chatbot, can answer questions, help explain statistics and direct you to process improvement tools.
- 60-Day Access: Continue to explore and apply your learning with EngineRoom even after the course ends.
Learn from Industry Leaders
Our instructors are seasoned professionals with extensive backgrounds in their fields, combining academic expertise with real-world experience. Expect interactive sessions, hands-on application, and expert guidance tailored to your learning journey.
Principal Statistician • MoreSteam
Thomas DeMarco is a Principal Statistician at MoreSteam, where he helps develop and teach practical, real-world statistical methods for continuous improvement professionals. Before joining MoreSteam in Spring 2025, he was an Industrial Statistician at Eastman Chemical, partnering with engineers and chemists across manufacturing and R&D to design and analyze over 100 formal experiments. Thomas has spent years teaching practitioners how to apply Design of Experiments (DOE) and other statistical tools to make better, data-driven decisions in complex processes. He holds both a B.S. and M.S. in Mathematical Sciences with a concentration in Statistics from Clemson University and is known for making advanced statistical concepts approachable, relevant, and actionable.
