Full Factorial Doe In Minitab – Tabtrainer® Masterclass

Design, analyze, and optimize full factorial experiments in Minitab with real-world case studies and best practices.

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.