Minitab® Tabtrainer Series: Full Factorial D.O.E. Mastery

Design of Experiments (DOE) with Center Points, Blocking, and Optimization in Minitab®

What you'll learn

Use Minitab to build full factorial designs, set center points, and define blocks for testing multiple factors like roughness, seam width, and thickness.

Perform power analysis in Minitab to calculate the number of replicates needed to detect a specific mean shift in the response variable with 80% certainty.

Insert center points in Minitab to detect nonlinear behavior between factors and validate the linear model assumptions using p-values from t-tests.

Add blocks in Minitab to separate variance caused by external factors like different test pilots, ensuring clean analysis of primary factors.

Run D.O.E. analysis via “Analyze Factorial Design” to check which factors significantly impact the response using p-values and coded coefficients.

Visualize effects with Minitab's factorial plots and interval plots to detect main effects and interactions influencing the cd value.

Improve model quality by removing non-significant terms using manual or automated backward elimination while preserving model hierarchy.

Evaluate model quality using R-squared, adjusted R-squared, and predicted R-squared in Minitab to assess precision and prediction power.

Check the model residuals with the 4-in-1 residual plot to ensure normality, homoscedasticity, and absence of time-based trends.

Use Minitab's response optimizer to dynamically adjust factor settings and visually identify optimal parameter combinations for cd reduction.

Use contour plots to visualize factor interactions, define robust process windows, and make reliable decisions even under process variation

Analyze cube plots to compare mean responses at all design points, identify optimal settings, and recognize non-linear effects across the factor space.

Rotate and edit surface plots to explore predicted responses in 3D space, validate model accuracy, and present results clearly in technical discussions.

Requirements

No Specific Prior Knowledge Needed: all topics are explained in a practical step-by-step manner.

Description

Welcome to the Tabtrainer® Certified Series – your expert platform for advanced statistical quality improvement using Minitab®.In this course, you'll master full factorial Design of Experiments (DOE) with a special focus on integrating center points and blocking – powerful techniques that bring real-world robustness to your experimental designs.You’ll learn how to structure experiments in Minitab®, detect curvature with center points, control for external noise using blocking variables, and use power analysis to ensure your design’s sensitivity. Through real industrial cases, you’ll perform ANOVA, regression, and optimization using Minitab’s Response Optimizer to derive statistically sound, actionable recommendations.Taught by Prof. Dr. Murat Mola, TÜV-certified Six Sigma expert and Professor of the Year 2023 in Germany, this course equips engineers, technicians, and Six Sigma professionals with the advanced skills needed to make data-driven decisions and optimize complex manufacturing processes.Course Description:This comprehensive training course is your step-by-step guide to mastering full factorial Design of Experiments (D.O.E.) using Minitab®, the industry-leading software for statistical analysis and quality improvement. Designed for engineers, quality managers, Six Sigma practitioners, and analysts, this course combines robust statistical theory with practical application inside Minitab’s user-friendly interface.You will begin by learning how to create full factorial experimental designs in Minitab, including the integration of center points and blocking structures to account for real-world factors like operator variability or process instability. With Minitab’s intuitive dialogs, you’ll learn to calculate statistical power, determine the required number of replicates, and ensure that your design can detect meaningful effects with high sensitivity.As the course progresses, you will perform detailed ANOVA and regression analysis using Minitab’s analysis tools. You’ll interpret coded coefficients, p-values, F-values, and t-statistics, and generate factorial plots and Pareto charts to visualize the significance of main effects and interactions. You’ll also use stepwise regression and hierarchical backward elimination to optimize your model without violating the model hierarchy.Minitab’s Four-in-One residual plots, normality tests, and variance inflation factors (VIF) will help you validate your model and confirm assumptions like normality and homoscedasticity.In the final section, you’ll use the Minitab Response Optimizer with dynamic sliders to explore the optimal settings for your process parameters. You will learn how to visualize individual and composite desirability, interpret confidence and prediction intervals, and make data-driven recommendations with up to 95% statistical certainty.To support clear communication and effective stakeholder alignment, you’ll also master three powerful visualization tools: Contour plots help you define robust process windows at a glance, identifying ideal factor combinations even under variation. Cube plots allow you to detect nonlinear effects and compare average responses across all design points in your experimental space. Finally, surface plots enable you to explore predictive model behavior in a dynamic 3D view—ideal for technical discussions and presentations where clarity and precision matter.Whether you are preparing for a Six Sigma project or aiming to bring your process optimization to a new level, this course gives you practical Minitab® skills and a deep understanding of factorial design principles—applied to a real-world engineering case.Keywords (SEO): Minitab DOE course, full factorial design with Minitab, response optimization, Minitab training, Six Sigma statistics, design of experiments tutorial, ANOVA in Minitab, regression analysis, Minitab Response Optimizer, process optimization tools.

Overview

Section 1: Tabtrainer Series: Full Factorial D.O.E. with Centerpoints and Blocks - Part 1

Lecture 1 Explore the curriculum

Lecture 2 Business Case and Process Understanding

Lecture 3 Understanding Type I & II Errors in Full Factorial D.O.E.

Lecture 4 Power Analysis in Full Factorial Design Using Minitab

Lecture 5 Optimizing Experimental Sensitivity in Full Factorial Design with Minitab

Lecture 6 Enhancing Experimental Rigor in Full Factorial Designs

Section 2: Tabtrainer Series: Full Factorial D.O.E. with Centerpoints and Blocks - Part 2

Lecture 7 Implementing a Full Factorial Design with Center Points and Blocks

Lecture 8 Finalizing Full Factorial Design

Lecture 9 Visualizing Factorial Results

Section 3: Tabtrainer Series: Full Factorial D.O.E. with Centerpoints and Blocks - Part 3

Lecture 10 Analyzing Factorial Effects

Lecture 11 Visualizing Effects & Interactions

Lecture 12 R² Metrics and Variance Analysis in Minitab®

Lecture 13 Interpreting ANOVA and Regression Equations in Full Factorial D.O.E.

Lecture 14 Hierarchical Model Refinement in Full Factorial D.O.E.

Lecture 15 Automated Model Optimization & Residual Validation

Section 4: Tabtrainer Series: Full Factorial D.O.E. with Centerpoints and Blocks - Part 4

Lecture 16 Response Optimization in Minitab®

Lecture 17 Interactive Optimization in Minitab®: Fine-Tuning Parameters

Lecture 18 Contour, Cube & Surface Plots for DOE Optimization

Lecture 19 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.