Design of Experiments 101 - Methods and Analysis

SEMINAR DESCRIPTION		WHO SHOULD ATTEND
In today's data-driven world, making informed decisions is crucial for success. Design of Experiments (DoE) provides a structured approach to investigate relationships between variables, eliminating guesswork of what is important, leading to reliable outcomes. DoE enhances efficiency by allowing simultaneous examination of multiple factors, saving time and providing a comprehensive understanding of systems. Professionals across various fields greatly benefit from using DoE. Scientists and researchers can conduct rigorous experiments, engineers can optimize manufacturing processes, pharmaceutical professionals can develop new treatments, and marketers can understand consumer behavior better. Mastering DoE is essential for effective decision-making, efficiency, and innovation.		Research Scientists Laboratory Technicians Manufacturing Engineers Quality Control Specialists Data Analysts and Statisticians Product Development Managers Clinical Trial Coordinators Biostatisticians

To harness the full potential of DoE, consider attending our 2-Day Virtual Seminar on Design of Experiments. Elaine Eisenbeisz, Owner of Omega Statistics, will provide practical insights and demonstration of DOE models. Minitab statistical software will be used in the training. Whether you’re a scientist, engineer, marketer, or industry professional, this seminar will equip you with the skills to implement DoE effectively, driving success in your field.

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 (11 AM to 5 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 (11 AM to 5 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

Design of Experiments DoE