Master Six Sigma Statistics with Minitab – Taught by Prof. Dr. Murat Mola, Germany’s Professor of the Year!
This training unit provides a comprehensive exploration of Box Plot Analysis to identify trends and differences in categorical data groups. Designed for professionals in quality management, this course uses real-world scenarios from the Smartboard Company to demonstrate the analysis of production scrap rates across weekdays.
What you’ll learn
- Fundamentals of Box Plot Analysis: How to create and interpret box plots to identify differences and trends in categorical data groups..
- Data Preparation and Cleansing: Working with real-world datasets, including data cleaning, extraction, and subset formation..
- Analysis of Production Metrics: Applying box plot analysis to production-related metrics to detect specific trends and anomalies..
- Identifying Optimization Potential: Analyzing daily differences in scrap rates to uncover opportunities for process improvement..
- Practical Evaluation: Utilizing the analysis results to support decisions and actions within quality management and optimization projects..
Course Content
- Boxplot Analysis with Minitab –> 7 lectures • 41min.
Requirements
This training unit provides a comprehensive exploration of Box Plot Analysis to identify trends and differences in categorical data groups. Designed for professionals in quality management, this course uses real-world scenarios from the Smartboard Company to demonstrate the analysis of production scrap rates across weekdays.
Participants will work with a pre-processed dataset containing 750 values representing daily scrap rates. Through hands-on exercises, participants will learn to construct, interpret, and customize box plots to uncover valuable insights for process optimization.
Key Learning Objectives:
- Understand the fundamentals of Box Plot Analysis, including the calculation of quartiles, median, and interquartile range (IQR).
- Explore the four types of box plots and their application based on categorical and response variables.
- Identify production trends and variability, such as higher scrap rates on specific weekdays, using statistical tools.
- Conduct an Outlier Analysis with Grubbs’ Test to detect anomalies and validate their causes.
- Create and use Individual Value Plots for detailed data visualization.
Advanced Skills Development:
- Automate repetitive analyses (e.g., box plots, outlier tests) by creating macros in Exec format using the Command Line History.
- Customize a new Daily Quality Analysis menu for one-click execution of recurring tasks, enhancing efficiency in routine operations.
- Incorporate additional statistical elements, such as arithmetic mean and trend lines, into visualizations.
Practical Application: Participants will analyze weekday-specific box plots to identify and address production inconsistencies. The course also emphasizes:
- The importance of stable production processes across the week.
- Techniques to streamline workflows with automated Minitab macros.
- Saving and exporting project results for consistent reporting.
This hands-on course equips participants with essential tools and techniques to optimize quality processes using Minitab, ensuring robust, repeatable statistical analyses in dynamic business environments.