Design and Analysis of Experiments (DoE)

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04 Jun

Design and Analysis of Experiments (DoE)

Design and Analysis of Experiments (DoE)

Tuesday, June 4, 2024 (12:00 AM) to Sunday, May 31, 2026 (11:59 PM)
5 PDCs
Provider: Quality Gurus Inc
Course Name: Design and Analysis of Experiments (DoE)

Speaker: Sandeep Kumar
Program Type: Videoconferences, webcasts, audiocasts, podcasts, eBooks, self-directed E-Learning
Registration URL: https://www.qualitygurus.com/link/doe/

Email Details

By the end of this course, participants will be able to: Understand Fundamental Concepts: Grasp the basic principles and terminology used in Design of Experiments (DoE). Differentiate between factorial and partial factorial designs. Apply Simple DoE Techniques: Perform manual calculations for simpler processes to build a foundational understanding. Utilize case studies like coffee tasting and catapult experiments to illustrate DoE concepts. Implement Blocking and Replication: Understand and apply concepts such as Blocking, Analysis of Covariance, and Replication in experimental design. Recognize the importance of these techniques in controlling variability and enhancing the reliability of experimental results. Understand and Apply Statistical Methods: Review and apply Analysis of Variance (ANOVA) and Regression Analysis to interpret experimental data. Gain the necessary statistical knowledge to understand and analyze DoE results effectively. Conduct Screening Experiments: Use Plackett-Burman Design to identify and reduce the number of critical factors in an experiment. Understand the purpose and methodology of screening designs in simplifying complex processes. Develop and Analyze Experimental Models: Apply full factorial, fractional factorial, and Split Plot Designs to model and analyze experimental data. Recognize the advantages and limitations of each design type for different experimental scenarios. Optimize Processes Using DoE: Use Central Composite Design (CCD) to optimize processes and achieve desired outcomes. Implement techniques to refine and improve experimental processes based on DoE findings. Interpret and Communicate Experimental Results: Analyze and interpret the results of designed experiments to make data-driven decisions. Communicate findings and insights effectively to stakeholders. Apply DoE in Real-World Scenarios: Translate DoE principles to various industry sectors and practical applications. Develop the skills to design, conduct, and analyze experiments that improve quality and performance in diverse settings. Participants will leave the course with a comprehensive understanding of the Design of Experiments, equipped with practical skills to apply DoE techniques in their professional roles, enhance process improvements, and drive data-based decision-making.

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Welcome to "Design and Analysis of Experiments (DoE)," a comprehensive course designed to take you from the basics to the intermediate level of understanding and applying DoE principles. This course is crafted for learners from various sectors, using simple case studies such as coffee tasting and catapult experiments to ensure a clear focus on core concepts. Why Choose This Course? Systematic Approach: Learn the systematic approach to studying the relationship between various inputs (factors) and key outputs (responses). Hands-On Learning: Engage with manual calculations on simpler processes to build a solid foundation in DoE. Sector-Neutral Case Studies: Focus on fundamental concepts through easy-to-understand examples. Statistical Knowledge: A quick refresher on ANOVA and Regression Analysis ensures you have the necessary background to interpret DoE results. Course Structure: This course consists of video lectures, readings, and quizzes that build upon each other to ensure you gain a firm grasp of the topics covered. Topics Covered: Section 1: Basics of Design of Experiments Understand common terms used in DoE. Learn about factorial and partial factorial designs through the coffee tasting example. Explore the catapult experiment to understand process variation. Other key concepts include Blocking, Analysis of Covariance, Replication, Confounding, and Design Resolutions. Section 2: ANOVA and Regression Review these critical statistical principles to provide sufficient knowledge for interpreting experimental results. Section 3: Screening, Modelling, and Optimizing Screening: Use Plackett-Burman Design to reduce the number of factors to be studied. Modelling: Apply full factorial, fractional factorial, and Split Plot Designs (for Hard to Change Factors). Optimizing: Optimize processes using Central Composite Design (CCD). Who This Course Is For: Quality Engineers Quality Managers All Engineers Performance Improvement Professionals Anyone who wants to understand the behavior of complex processes to achieve desired outcomes What You'll Learn: Fundamentals of the Design of Experiments (DoE) using simple, understandable examples. Techniques for Screening (Plackett-Burman), Modelling (Full, Fractional Factorial, and Split Plot Designs), and Optimizing (Central Composite Design) Designs. A quick refresher on ANOVA and Regression Analysis to help you clearly understand analysis results. A clear understanding of Blocking, Analysis of Covariance, Replication, Confounding, and Design Resolutions. Enroll Now: Join us in this comprehensive course to master the principles and applications of the Design of Experiments, and take your understanding of quality and process improvement to the next level. Enroll now and start your journey to becoming proficient in DoE!