Full Factorial Doe In Minitab – Tabtrainer® Masterclass

Full Factorial Doe In Minitab – Tabtrainer® Masterclass
Published 5/2025
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz

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.

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

Overview

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

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

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 27 Model Integrity and Statistical Validity

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.