Tabtrainer Minitab: Capability Analysis – NonNormal Data

Tabtrainer Minitab: Capability Analysis – Non-Normal Data
Published 5/2025
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz

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.

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.

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

Overview

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

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

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.