Tabtrainer Minitab: Attribute Agreement Analysis - Good/Bad
Published 5/2025
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 1h 10m | Size: 440 MB
Achieve top-level expertise in Minitab with Prof. Dr. Murat Mola, recognized as Germany's Professor of the Year 2023.
What you'll learn
Understand the difference between nominal, ordinal, and cardinal scale levels and their impact on measurement system analysis
Set up a complete attribute agreement study based on real-world manufacturing examples
Create structured measurement protocols for "GOOD/BAD" evaluations in production environments
Perform attribute agreement analysis and assess appraiser consistency (repeatability and reproducibility)
Calculate and interpret Cohen's anIdentify decision biases among appraisers and their effects on product qud Fleiss's kappa statistics for appraiser evaluations
Analyze agreement rates between appraisers and compare them against customer standards
Visualize appraiser performance using agreement rate charts and confidence intervals
Derive practical action recommendations to improve appraiser training and measurement system capability
Achieve AIAG-compliant kappa values and build robust, reliable attribute measurement systems
Requirements
No Specific Prior Knowledge Needed: all topics are explained in a practical step-by-step manner.
Description
Course DescriptionIn this course, participants learn how to analyze attribute-based measurement systems, evaluate appraiser performance, and ensure that decisions in a manufacturing environment are reliable and meet customer expectations.Using a real-world business case from Smartboard Company, the training covers every step from basic concepts to full practical execution, including graphical evaluations, statistical assessment using kappa statistics, and deriving concrete action recommendations.Course StructureThe course begins with an introduction to scale levels, explaining nominal, ordinal, and cardinal data through practical manufacturing examples. Participants learn how the type of data determines which statistical analyses are appropriate.The course then moves into the practical setup of an attribute agreement analysis. Participants design a measurement protocol, define appraisers, select representative samples, and structure the testing procedure for "GOOD/BAD" evaluations of skateboards.Building on this foundation, participants conduct a full attribute measurement system analysis. They explore the transition from continuous data assessments to attribute data assessments, supported by a review of scale levels and their implications.A detailed explanation of Cohen's and Fleiss's kappa statistics follows. Participants manually calculate kappa values for simple examples and then apply them to complex real-world appraiser evaluations.Participants evaluate appraiser repeatability by checking consistency within each appraiser. They then assess reproducibility by comparing the agreement of different appraisers with one another.Through detailed comparisons against customer standards, participants identify biases such as the tendency to over-reject or over-accept parts. They learn how to detect whether appraisers apply stricter or more lenient quality criteria than the customer requires.Next, the course evaluates the overall agreement between all appraisers, revealing how well the team functions as a whole and highlighting where inconsistencies exist.Participants visualize results using graphical summaries that illustrate appraiser performance in terms of repeatability and agreement with customer standards. These graphics are used to facilitate effective discussions in appraiser training sessions.The course concludes with a full summary of key findings. Participants derive concrete action recommendations, such as conducting targeted appraiser retraining, aiming to achieve a kappa value of 0.75 or higher, thus meeting AIAG standards for measurement system capability.Key OutcomesParticipants will be able to classify data correctly as nominal, ordinal, or cardinal.They will be able to independently design and execute an attribute agreement analysis.They will understand how to calculate and interpret kappa statistics for both individual and team appraiser evaluations.They will be able to assess and visualize measurement system capability and recommend improvement actions based on statistical evidence.They will understand how to align appraiser decision behavior with customer quality requirements to minimize scrap costs and customer complaints.Target AudienceThis course is designed for quality engineers, quality managers, Six Sigma Belts (Green, Black, Master Black), production managers, and professionals involved in quality assurance and measurement system evaluations in industrial environments.Course ObjectiveBy the end of the course, participants will be fully equipped to perform professional attribute agreement analyses, identify weak points in appraiser behavior, and implement improvements that ensure reliable, customer-focused quality decisions in manufacturing.
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.
Homepage:
https://www.udemy.com/course/tabtrainer-minitab-attribute-agreement-analysis-goodbad/Screenshots
Download linkrapidgator.net:
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nitroflare.com:
https://nitroflare.com/view/38602E9ECB9CFA1/qudli.Tabtrainer.Minitab.Attribute.Agreement.Analysis..GoodBad.rar
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