FA-0536Data & Analytics

Minitab Advanced Analytics

Optimization, Diagnostics, and Applied Project

Introduction

Why this course

This advanced one-day Minitab workshop takes participants well beyond the basics into the kinds of analytical techniques used by experienced engineers, quality analysts, and data scientists. You’ll deepen your understanding of design of experiments with response surface methods, critically evaluate regression and ANOVA models, and integrate results into decision-making frameworks.

The course culminates in a capstone project where participants apply everything learned to design, analyze, and optimize a real problem using Minitab, reinforcing not just tool usage but interpretation and communication of results — skills essential in industry.

Instructor Experience: The instructor brings over 30 years of industry experience and focuses on practical, real-world applications rather than purely academic theory.

Learning outcomes

By the end of this course, participants will be able to:

Design and analyze Response Surface and other advanced DOE designs to model curvature and find optimal settings.
Evaluate and diagnose model fits — including regression diagnostics and checks for ANOVA assumptions.
Apply General Linear Models with interactions, covariates, and factor coding to complex data.
Conduct model validation and refinement, including residual analysis and transformation strategies.
Integrate analysis steps into a capstone project workflow that goes from problem definition to results interpretation and recommendations.

Prerequisites

Participants should have completed an introductory Minitab course or have equivalent experience, including:

Comfortable creating DOE (full and fractional factorial) and interpreting GLM outputs.
Familiarity with basic regression and ANOVA analysis.
Basic data management skills in Minitab.

Training outline

8 modules

011. Review & Setup3 topics

Course goals and agenda
Advanced project brief and data sets
Quick review of Minitab interface and output interpretation

022. Advanced Experimental Design2 topics

Response Surface Methodology (RSM)
- What RSM is and why it’s useful for optimization (curvature modeling).
- Response Surface designs: Central Composite, Box-Behnken
- Creating and executing RSM designs in Minitab
Interpreting RSM output, contour and surface plots

033. Model Diagnostics & Validation2 topics

Regression diagnostics
- Residual plots
- Normality, homoscedasticity, leverage and influence
ANOVA assumption checks and transformations

044. General Linear Model (GLM) Deep Dive3 topics

Including interaction terms and covariates
Model comparison and selection
Using GLM for complex experimental or observational data

055. Optimization & Interpretation3 topics

Response Optimizer and numerical search for “best” settings
Overlaid contour plots for multiple responses
Practical interpretation and communication of optimized solutions

066. Project Work — Capstone6 topics

Project introduction: participants choose or are assigned a dataset/problem
Phase 1: Define goals, select factors and responses
Phase 2: Design experiment or analysis strategy
Phase 3: Conduct analysis (DOE + GLM + diagnostics)
Phase 4: Optimize and interpret results
Phase 5: Prepare brief presentation of findings

077. Presentations & Feedback3 topics

Participants present capstone results
Instructor critiques based on industrial standards
Group discussion of insights and alternative approaches

088. Wrap-Up3 topics

Review key takeaways
Best practices for advanced Minitab workflows
Resources for further learning (e.g., response surface design resources and statistical modeling references)

A programme built around your team.

Share your training goals and requirements.