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Copy of Design of Experiments 101 – Methods and Analysis
LEARNING OBJECTIVES:
By the end of this seminar, participants will be equipped with the skills and requisite knowledge to effectively apply Design of Experiments in their work, leading to improved decision-making, efficiency, and innovation.
Foundational Knowledge: Gain an understanding of the core principles and concepts of DoE.
Experiment Planning: Develop the ability to effectively plan and design experiments, selecting appropriate variables and design types.
Data Analysis Skills: Learn to analyze experimental data using statistical software and interpret results accurately.
Practical Application: Apply DoE techniques to real-world problems, enhancing problem-solving skills and practical knowledge.
Optimization: Understand how to use DoE for process optimization, improving efficiency, and achieving better outcomes.
Best Practices: Learn best practices and strategies for implementing DoE in various professional fields.
Advanced Techniques: Explore some of the advanced DoE techniques and their applications in complex scenarios
AGENDA
DAY 1 (10 AM to 4 PM)
Session 1 - Introduction to Design of Experiments
Importance and applications of DoE
Basic principles and terminology
Session 2 - Simple Comparative Experiments
Simple comparative experiments
Sample size determination and power
Coffee Break
Session 3 - Experiments with a Single Factor (One-way ANOVA)
One-factor experiments with multiple levels
Multiple comparisons and random effects models
Lunch Break
Session 4 - Blocking Designs
Randomized complete block designs (RCBD)
Latin square designs and their extensions
Q&A Session
DAY 2 (10 AM to 4 PM)
Session 5 - Factorial Designs
Factorial designs with two treatment factors
Main effects and interactions
Session 6 - Factorial Designs
Simplest case and estimated effects
2k factorial designs
Coffee Break
Session 7 - Advanced Experimental Designs
Fractional factorial designs
Response surface methodology (RSM)
Lunch Break
Session 8 – Regression Analysis in DoE
Regression analysis
Q&A and Closing Remarks
ELAINE EISENBEISZ
Elaine Eisenbeisz is a private practice statistician and owner of Omega Statistics, a statistical consulting firm based in Southern California. Elaine has over 30 years of experience in creating data and information solutions for industries ranging from governmental agencies and corporations, to start-up companies and individual researchers.
Elaine’s love of numbers began in elementary school where she placed in regional and statewide mathematics competitions. She attended University of California, Riverside, as a National Science Foundation scholar, where she earned a B.S. in Statistics with a minor in Quantitative Management, Accounting. Elaine received her Master’s Certification in Applied Statistcs from Texas A&M, and is currently finishing her graduate studies at Rochester Institute of Technology. Elaine is a member in good standing with the American Statistical Association as well as many other professional organizations. She is also a member of the Mensa High IQ Society. Omega Statistics holds an A+ rating with the Better Business Bureau.
Elaine has designed the methodology for numerous studies in the clinical, biotech, and health care fields. She currently is an investigator on approximately 10 proton therapy clinical trials for Proton Collaborative Group, based in Illinois. She also designs and analyzes studies as a contract statistician for nutriceutical and fitness studies with QPS, a CRO based in Delaware. Elaine has also worked as a contract statistician with numerous private researchers and biotech start-ups as well as with larger companies such as Allergan and Rio Tinto Minerals. Not only is Elaine well versed in statistical methodology and analysis, she works well with project teams. Throughout her tenure as a private practice statistician, she has published work with researchers and colleagues in peer-reviewed journals. Please visit the Omega Statistics website at www.OmegaStatistics.com to learn more about Elaine and Omega Statistics.
