Tabtrainer Minitab: Capability Analysis - Non-Normal Data
Published 5/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 452.78 MB | Duration: 1h 4m
Achieve top-level expertise in Minitab with Prof. Dr. Murat Mola, recognized as Germany's Professor of the Year 2023.
What you'll learn
Learn how to analyze real industrial process data using Minitab, focusing on capability analysis with non-normal, binomial, and Poisson distributions.
Use Minitab to transform non-normal data into a normal shape with the Johnson method and apply classical capability metrics with full confidence.
Evaluate process stability and capability for heat-treated components with Minitab, even when the measurement data does not follow a normal curve.
Identify the best-fitting statistical distribution for your process data using Minitab's Individual Distribution Identification function.
Understand how to conduct binomial capability analysis in Minitab for good/bad data, such as final inspection results in manufacturing lines.
Verify process stability using Minitab's p-chart and identify random versus systematic variation in defect rates across subgroups.
Learn how to assess whether your process follows a binomial distribution using graphical tools like histograms and rate-of-defectives plots.
Use Minitab's capability analysis tools for binomial data to calculate process Z-values and determine compliance with Six Sigma targets.
Perform Poisson capability analysis with Minitab to monitor and evaluate discrete defects, such as scratch counts during final assembly.
Apply the U-chart in Minitab to track and control the number of defects per unit and confirm statistical control over your process.
Validate whether your scratch data follows a Poisson distribution using Minitab's graphical Poisson plot and summary statistics.
Combine stability and capability assessments in one step using Minitab's capability tools for binomial and Poisson data types.
Generate complete Sixpack reports in Minitab to simultaneously assess normality, control limits, and capability metrics for real process data.
Use DPU, PPM, and Z-value statistics in Minitab to translate operational defect rates into meaningful quality and performance indicators.
Master capability analysis across all data types in Minitab and drive quality improvement with statistical insights from real production cases.
Requirements
No Specific Prior Knowledge Needed: all topics are explained in a practical step-by-step manner.
Description
Advanced Process Capability Analysis Using Minitab: From Non-Normal Data to Attribute Metrics (Binomial & Poisson) Part 1 - Part 3
art 1 - Process Capability for Continuous Non-Normal DataPart 2 - Capability Analysis for Binomially Distributed Data (Good/Bad Classification)Part 3 - Capability Analysis for Poisson-Distributed Defect CountsCourse Description:This comprehensive three-part training course equips participants with the essential knowledge and applied skills to perform process capability analysis across a broad spectrum of real-world manufacturing scenarios. Using Minitab, one of the most powerful tools for statistical quality control, participants will learn how to evaluate process performance for non-normally distributed continuous data, binomial (attribute-based) data, and Poisson-distributed defect data.Through three hands-on modules based on the Smartboard Company case studies, the course not only covers the statistical theory behind each analysis type but also focuses on practical Minitab applications that ensure reliable and actionable insights in quality engineering.Course Structure and Learning Outcomesart 1 - Process Capability for Continuous Non-Normal DataScenario: Dimensional changes during heat treatment of skateboard axlesKey Learnings:Understand the limitations of classical process capability metrics (Cp, Cpk, Pp, Ppk) when data are non-normally distributed.Use Minitab's Descriptive Statistics, Boxplots, and the Anderson-Darling test to evaluate distribution assumptions.Apply Minitab's Individual Distribution Identification to determine the best-fitting transformation model.Perform Johnson Transformation and validate it via p-values and probability plots.Utilize the Capability Sixpack (Normal) in Minitab to analyze process stability and capability after transformation.Interpret Pp and Ppk values to determine conformance to customer specification limits.Conclude on process centering potential and sigma level adequacy (Six Sigma benchmark).Heavy emphasis on real-world data preprocessing and transformation in Minitab before performing capability analysis.Part 2 - Capability Analysis for Binomially Distributed Data (Good/Bad Classification)Scenario: Surface inspection of final assembled skateboardsKey Learningsifferentiate nominal scale data from metric data and understand its implications on statistical modeling.Use Minitab's p-charts to evaluate process stability with respect to fluctuating subgroup sizes.Understand how to verify binomial distribution assumptions using rate of defectives plots, histograms, and cumulative defect curves.Conduct Capability Analysis for Binomial Data in Minitab using:"Statistics > Quality Tools > Capability Analysis > Binomial"Specification of varying or constant subgroup sizesInterpret key indicators such as defect rate, PPM, and Z benchmark.Evaluate whether the current process meets the Six Sigma threshold (Z ≥ 2.0).Participants gain hands-on skills to analyze binary attribute data (pass/fail, good/bad) using Minitab's specialized capability tools.Part 3 - Capability Analysis for Poisson-Distributed Defect CountsScenario: Number of surface scratches per subgroup in final assemblyKey Learnings:Understand when and why to apply Poisson distribution for defect count data.Learn to model defects per unit (DPU) using U-charts in Minitab.Use Minitab's "Capability Analysis > Poisson" function to evaluate process performance for count data.Analyze process stability using U-charts and cumulative DPU plots.Verify Poisson distribution assumptions using Poisson plots.Interpret summary statistics such as:Mean DPUExpected vs. observed defect levelsConfidence intervalsDerive improvement actions when current processes are not capable (e.g., scratch rate > 0%).This module focuses on translating real-time count data into statistically valid process insights using Poisson capability models in Minitab.Software Focus:Throughout all three modules, Minitab is the central tool. Participants will become proficient inata importing and structuringDistribution identification and transformationSelection and interpretation of control chartsCapability analysis tools (Sixpack, Binomial, Poisson)Understanding reports and graphical outputs for executive decision-making Overview
Section 1: Part 1 - Process Capability for Continuous Non-Normal Data
Lecture 1 Explore the curriculum: Process Capability for Continuous Non-Normal Data
Lecture 2 Business Case and Process Understanding
Lecture 3 Foundations for Capability Analysis with Non-Normal Data
Lecture 4 Multiple Distributions and Applying the Johnson Transformation
Lecture 5 Johnson Transformation and Capability Metrics
Lecture 6 Summary of the Most Important Findings
Section 2: Part 2 - Capability Analysis for Binomially Distributed Data (Good/Bad)
Lecture 7 Explore the curriculum: Capability Analysis for Binomially Distributed Data
Lecture 8 Business Case and Process Understanding
Lecture 9 Binomial Capability Analysis: Set up
Lecture 10 Validating Process Stability for Binomial Data Using Minitab
Lecture 11 Verifying Binomial Distribution Fit Using Minitab Diagnostic Plots
Lecture 12 Interpreting Statistics and Capability Indicators
Lecture 13 Graphical Derivation and Interpretation of the Z-Value Using Minitab
Lecture 14 Summary of the Most Important Findings
Section 3: Part 3 - Capability Analysis for Poisson-Distributed Data
Lecture 15 Explore the curriculum: Capability Analysis for Poisson-Distributed Data
Lecture 16 Business Case and Process Understanding
Lecture 17 Process Stability and Distribution Fit
Lecture 18 Poisson Capability Results and Process DPU
Lecture 19 Summary of the Most Important Findings
Data Analysts, Six Sigma Belts, Minitab Process Optimizers, Minitab Users,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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