Udemy – Full Factorial Doe In Minitab – Tabtrainer® Masterclass

What you'll learn
Create full and fractional factorial designs in Minitab to investigate linear cause-and-effect relationships efficiently.
Understand center points to detect non-linear effects and assess whether factorial design is suitable for your system.
Evaluate model significance via main effect plots, interaction plots, and cube plots based on experimental results.
Interpret coded coefficients, t-values, and p-values to identify statistically significant factors and interactions.
Use VIF to check for multicollinearity and ensure model accuracy by excluding correlated predictors.
Calculate and interpret confidence and prediction intervals for both population means and future observations.
Apply power and sample size analysis to define the required number of replicates for robust D.O.E. results.
Perform response optimization in Minitab to minimize, maximize, or target response variables based on model output.
Use interactive optimization plots to test parameter changes and visualize effects on the response variable.
Translate statistical D.O.E. results into practical process settings for technical and economic optimization.
Requirements
No Specific Prior Knowledge Needed: all topics are explained in a practical step-by-step manner.
Description
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
Overview
Section 1: DOE - Full factorial experimental design - Part 1
Lecture 1 Explore the Curriculum
Lecture 2 Business Case and Process Understanding
Lecture 3 Understanding of Full Factorial Experimental Design (DOE)
Lecture 4 From Statistical Modeling to Production Implementation
Lecture 5 From Fractional Factorials to Mixture and Taguchi Designs
Lecture 6 Setting Up and Interpreting a Full Factorial DOE in Minitab
Lecture 7 The Right Resulution: Full vs. Fractional Factorial Designs and Confounding Risk
Lecture 8 Ensuring Model Validity: The Decision for Full Factorial Designs
Lecture 9 Enhancing DOE Validity Through Statistical Power
Lecture 10 Optimizing Replicates and Blocking - Stable Experimental Conditions
Lecture 11 Factor Setup, Randomization, and Design Display Options
Lecture 12 Coded Design, Orthogonality, and Transition to DOE Data Analysis
Section 2: DOE - Full factorial experimental design - Part 2
Lecture 13 Main Effects, Interactions, and Statistical Significance
Lecture 14 Interpreting Statistical Significance, Multicollinearity, and Effect Strength
Lecture 15 From Factorial Plots to Cube Plot Interpretation
Lecture 16 Analyzing Main and Interaction Effects
Lecture 17 Why Interaction Plots Are Essential in Factorial DOE Analysis
Lecture 18 Two-Way and Three-Way Interactions
Lecture 19 Preparing Data for Three-Way Interaction Plots
Lecture 20 Three-Way Interaction Plots: Using Unstacked Data
Lecture 21 Uncovering Hidden Dependencies in Factorial DOE
Section 3: DOE - Full factorial experimental design - Part 3
Lecture 22 Statistical Interpretation of R², R²(adj), and Residuals in Minitab
Lecture 23 ANOVA, and Predictive Reliability in Full Factorial Designs
Lecture 24 Interaction Effects and Regression Equation Diagnostics
Lecture 25 Alias Structures and Confounding
Lecture 26 The Pareto Chart in Full Factorial DOE
Lecture 27 Model Integrity and Statistical Validity
Section 4: DOE - Full factorial experimental design - Part 4
Lecture 28 Optimal Factor Settings under Technical and Economic Constraints
Lecture 29 Regression Models for Targeted Process Control
Lecture 30 Optimal Parameter Settings in Minitab Using Response Optimization
Lecture 31 Summary of the Most Important Findings
Data Analysts, Six Sigma Belts, Minitab Process Optimizers, Minitab Users,Quality Assurance Professionals: Those responsible for monitoring production processes and ensuring product quality will gain practical tools for defect analysis.,Production Managers: Managers overseeing manufacturing operations will benefit from learning how to identify and address quality issues effectively.,Six Sigma Practitioners: Professionals looking to enhance their expertise in statistical tools for process optimization and decision-making.,Engineers and Analysts: Individuals in manufacturing or technical roles seeking to apply statistical methods to real-world challenges in production.,Business Decision-Makers: Executives and leaders aiming to balance quality, cost, and efficiency in production through data-driven insights and strategies.

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