Tabtrainer Minitab: Spc Charts For Attribute Quality Data
Published 5/2025
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 1h 6m | Size: 520 MB
Mastercourse SPC - Control Charts in Minitab - with Prof. Dr. Murat Mola, Germany's Professor of the Year 2023
What you'll learn
Identify nominally scaled attribute data and apply the principles of binomial distribution to evaluate production quality using real data.
Differentiate between relative defect rates in P Charts and absolute defect counts in NP Charts, understanding when to use each method.
Analyze control chart signals and trace root causes of process instabilities, using real examples like increased defects during holidays.
Interpret probability plots from the P Chart Diagnostic to validate if process data meet binomial assumptions and decide on the correct control chart.
Perform a full P Chart Diagnostic to determine if the data's dispersion matches the expected random behavior of a binomial distribution.
Apply AIAG guidelines to smooth control limits by assessing whether subgroup size variations meet the standard's 75% tolerance condition.
Calculate daily defect rates by relating the number of bad skateboards to the total produced, adapting to fluctuating daily subgroup sizes.
Save the entire defect rate analysis project, ensuring structured documentation and easy reference for future process improvements.
Learn how to use U charts to analyze relative defect rates based on Poisson-distributed data with variable subgroup sizes in final assembly processes.
Understand how to perform U chart diagnostics to verify whether real-world defect data follows the statistical Poisson distribution model.
Apply C charts to monitor absolute defect counts when subgroup sizes are constant, and interpret fixed control limits accurately.
Learn how to manually calculate upper and lower control limits for U and C charts according to AIAG guidelines using U-bar and C-bar values.
Identify process instabilities using built-in control tests and visualize shifts or outliers directly in Minitab's control chart output.
Use the "Stages" function to split the process into sub-processes and apply correct control limits before and after process improvements.
Compare p, np, U, and C charts to understand the difference between binomial and Poisson-distributed attribute quality data.
Interpret the agreement rate from Poisson probability plots and decide whether a Laney U′ chart is needed to correct for overdispersion.
Analyze real-world production data from Smartboard Company and calculate defect rates with accurate statistical and visual control tools.
Save your analysis as a Minitab project file, ensuring all charts, diagnostics, and calculations are preserved for further quality reviews.
Requirements
No Specific Prior Knowledge Needed: all topics are explained in a practical step-by-step manner.
Description
This comprehensive training course provides a deep, practice-driven introduction to Statistical Process Control (SPC) using attribute control charts in Minitab. It is based on two detailed real-world scenarios from the final assembly process of skateboards at Smartboard Company. The training focuses on understanding, selecting, applying, interpreting, and differentiating the most relevant SPC tools for attribute data: P charts, NP charts, Laney P′ charts, U charts, C charts, and Laney U′ charts.Participants learn not only the technical application of each control chart but also the underlying statistical distributions (binomial and Poisson), diagnostics, interpretation of process instabilities, and the impact of subgroup structure and process changes on control chart accuracy.What This Course CoversModule 1: Monitoring Defective Units (Binomial Distribution)In the first part of the course, you will work with a dataset that reflects the number of defective skateboards identified during final surface inspection. The analysis focuses on
Chart - to monitor the proportion of defective products across subgroups of varying size.NP Chart - to evaluate the number of defectives in subgroups of constant size.Laney P′ Chart - a modified version of the P chart that adjusts for overdispersion or underdispersion, providing more reliable control limits.Key learning points include:How to diagnose binomial suitability using probability plots.When to apply the Laney P′ chart to avoid false alarms or missed process shifts.How to detect special cause variation using built-in Western Electric control tests.How to interpret control chart results in the context of real production shifts and inspection quality.Module 2: Monitoring Defect Counts (Poisson Distribution)In the second part, you transition from the classification of defective units to analyzing the number of defects per product-such as surface scratches detected per skateboard. This requires a different statistical approach based on Poisson distribution and the use of:U Chart - for tracking the defects per unit, especially when subgroup sizes vary.C Chart - for analyzing total defect counts in subgroups of constant size.Laney U′ Chart - a dispersion-adjusted U chart used when Poisson assumptions are not fully met.This module also introduces advanced techniques such as:Running a U chart diagnostic to check Poisson distributional fit.Manual calculation of control limits based on AIAG formulas.Understanding and applying the "Stages" function in Minitab to split charts before and after process improvements.Visual comparison of U chart vs. C chart when working with the same data under different conditions.Learners explore how mixing data from two different process phases in a single chart leads to distorted control limits, and how correct segmentation enables meaningful interpretation and true process insight.By the End of the Course, You Will Be Able To:Select the appropriate attribute control chart based on defect type, data structure, and distribution.Understand the difference between binomially and Poisson-distributed quality data.Perform diagnostics and validate data suitability for P, NP, U, or C charts.Interpret agreement rates and confidence limits in probability plots.Use Laney charts to correct overdispersed or underdispersed data and avoid misinterpretation.Apply control tests to detect assignable causes and process instability.Split your analysis into pre- and post-improvement process phases using stage control.Manually calculate control limits to validate software-generated results.Present and document your findings in a structured Minitab project for quality reporting.This course combines theory, diagnostics, and applied analytics into a complete learning journey for mastering attribute SPC methods in Minitab-ideal for both industrial practice and academic advancement.Who this course is for
Production and operations managers who need to interpret control charts for decision-making, resource planning, and continuous improvement.
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.
Quality engineers and quality assurance specialists who monitor and improve production processes and need reliable tools for evaluating defect data.
Data analysts and statisticians working in manufacturing or service industries, who want to deepen their applied knowledge of SPC for count and classification data.
Technical trainers and university instructors looking for real-world examples to teach attribute-based quality control using Minitab.
Students in engineering, quality management, operations research, or applied statistics programs who want to bridge theory and practice in preparation for their careers.
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