Overview

Welcome to the Tabtrainer® Masterclass Series - the professional learning standard for advanced statistical training in quality and process engineering.This expert-level course on Full Factorial Design of Experiments (DOE) in Minitab empowers engineers, researchers, and Six Sigma professionals to confidently plan, execute, and interpret 2-level factorial experiments in real industrial settings. The training is based on proven case studies from the Smartboard Company and combines scientific rigor with practical application.Developed and taught by Prof. Dr. Murat Mola, TÜV-certified instructor and founder of Tabtrainer®, this course bridges the gap between statistical theory and actionable results. As Germany's "Professor of the Year 2023", Prof. Mola ensures clarity, precision, and maximum relevance for industrial practice.Course DescriptionThis course offers an in-depth, scientifically grounded introduction to the Full Factorial Design of Experiments (DOE), one of the most powerful tools in applied statistics for identifying and quantifying cause-and-effect relationships between factors and response variables. The course places strong emphasis on the use of Minitab as a professional platform for designing, executing, and analyzing full factorial experiments in both research and industrial settings.Participants are guided through the entire DOE lifecycle, from planning and data collection to statistical modeling and interpretation of interaction effects. The course follows the PDCA cycle and complies with industrial quality standards (e.g., AIAG, VDA Volume 5, and ISO 13053).The key focus lies in the construction and evaluation of 2-level full factorial experiments, including the use of replicates, center points, and coded vs. uncoded designs, and the rigorous interpretation of main effects and multi-level interactions using Minitab's built-in analytical and graphical tools.Key Learning ObjectivesBy the end of the course, participants will be able to:Understand the theoretical foundation and statistical assumptions of full factorial DOEDesign orthogonal and balanced full factorial experiments using MinitabDefine experimental boundaries, blocking, and replicate structureIdentify statistically significant main effects, two-way, and three-way interactionsInterpret alias structures, confounding effects, and design resolution levelsUse coded coefficients and factorial plots to understand effect magnitudes and directionsPerform normality testing, residual diagnostics, and check model validity via R² metricsVisualize experimental space with cube plots, main effect plots, and interaction plotsConduct response optimization including desirability functions, confidence intervals, and prediction intervalsCommunicate results to technical and non-technical stakeholders with statistical confidence

Skills

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