Analyze pressure & roughness effects in Minitab – apply Two-Way ANOVA with interaction and Tukey test to optimize.

What you'll learn
- Introduction to Two-Way ANOVA: Analyze how two categorical factors (laminating pressure and surface roughness) affect a continuous response variable.
- Data Preparation for ANOVA: Import, clean, and structure experimental data correctly, assigning clear factor names and response variables.
- Visual Exploration of dаta: Use boxplots and main effect plots to visually identify preliminary trends, interactions, and possible effects.
- GLM Setup: Build a Two-Way ANOVA using Minitab's GLM function, clearly defining predictors, response variables, and interaction terms.
- Interpreting: Interpret p-values (< 0.05) to identify meaningful main and interaction effects between laminating pressure and surface roughness.
- Main Effects vs. Interaction Effects: Differentiate between independent main effects and interaction effects.
- Model Quality and Residual Analysis: Evaluate GLM quality using R-squared and residual diagnostics (normality, variance consistency).
- Factorial Interaction Plots: Create and interpret factorial plots to clearly visualize significant interactions between categorical factors.
- Tukey Post-hoc Analysis: Apply the Tukey test to determine exactly which factor combinations significantly differ from each other.
- Evidence-based Production Recommendations: Translate statistical insights into practical decisions, recommending optimal factor settings.

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

Description
Welcome to this expert-level training from theTabtrainer® Certified Series– your trusted platform for applied statistical modeling in manufacturing.

In this course, you will master theTwo-Way ANOVA with interactionusingMinitab®, based on real production data from theSmartboard Company. You'll analyze howlaminating pressureandsurface roughnessinfluence the tensile shear strength in skateboard deck production – and learn how to validate statistical results with post-hoc tests likeTukey.

From raw data preparation and model diagnostics to graphical interpretation and business optimization, this training builds your skills to evaluate complex processes with confidence.

Led byProf. Dr. Murat Mola, TÜV-certified Six Sigma trainer andProfessor of the Year 2023 in Germany, this course empowers you to turn statistics into actionable production strategies:

In this training unit,students learn how to apply theTwo-Way Analysis of Variance (ANOVA)to evaluate the influence of two categorical factors on a continuous response variable, using a realistic industrial quality control scenario.

The case study is taken from thelamination process in skateboard deck productionat Smartboard Company, where the tensile shear strength of glued maple layers is the central performance indicator. The two experimental factors –laminating pressureandsurface roughness– are each examined at three levels, leading to 9 parameter combinations and 270 measured values.

Students follow a structured learning process:

Data Preparation: Importing raw test data, stacking column blocks, assigning factor names, and formatting the response variable.

Visual Analysis: Usingboxplotsandmain effects plotsto explore initial trends and identify possible interaction effects between predictors.

Statistical Modeling: Performing a two-way ANOVA with interaction terms using the General Linear Model (GLM), interpretingp-values, and understanding the logic behindmain effectsandinteraction effects.

Model Diagnostics: Evaluating model quality with R-squared and residual analysis; confirming the normal distribution of residuals with a 4-in-1 plot and the Anderson-Darling test.

Post-hoc Testing: Applying theTukey significance testto identify which specific combinations of pressure and roughness lead to significantly different strength outcomes.

Business Interpretation: Drawing conclusions about which parameter settings deliver the most stable and high tensile shear strength, and recommending optimized production configurations based on statistical evidence.

By the end of the training, students are able to:

Build and interpret a Two-Way ANOVA model.

Understand and distinguish betweenmain effectsandinteraction effects.

Apply residual diagnostics to evaluate model fit.

UseTukey grouping lettersand confidence intervals to validate significant factor combinations.

Translate statistical results intoconcrete optimization strategiesin an industrial environment.

This training connects applied statistics with real-world manufacturing and enables participants to make better, evidence-based decisions in complex production processes.

Who this course is for:
- 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.
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