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

Minitab® Tabtrainer Series: Full Factorial D.O.E. Mastery
Published 5/2025
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz

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.

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

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.

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

Overview

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

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

Lecture 8 Finalizing Full Factorial Design

Lecture 9 Visualizing Factorial Results

Lecture 10 Analyzing Factorial Effects

Lecture 11 Visualizing Effects & Interactions
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

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.