Overview

Welcome to the Tabtrainer® Certified Series - your go-to platform for statistically sound decision-making in real production environments.In this training unit, you'll learn to apply the One Sample Poisson Rate Test using Minitab®, based on real quality data from shock pad production at Smartboard Company. The goal: determine whether the observed defect rate exceeds the customer's strict threshold of 25 defects per batch of 500 units.You'll analyze 50 sampled batches, visualize Poisson distributions, formulate hypotheses correctly, and interpret p-values, Poisson means, and decision boundaries. You'll also understand when to use the exact Poisson method instead of approximations - a key point in low-count quality environments.Taught by Prof. Dr. Murat Mola, TÜV-certified expert and Professor of the Year 2023 in Germany, this course shows how to translate statistical theory into business-critical recommendations - using Six Sigma thinking, real Minitab tools, and practical casework from the factory floor.Learning DescriptionIn this training unit, students are introduced to a realistic quality control scenario in the shock pad production at Smartboard Company. The goal is to assess whether the production process meets the customer's strict requirement: a maximum of 25 surface defects per batch of 500 shock pads, equivalent to a 5% defect rate.Since inspecting every shock pad would be economically unfeasible, students work with sample data consisting of 50 randomly selected batches. Each batch contains 500 parts, and the number of defects per batch was measured using an automatic surface inspection system. Based on this data, the students perform a statistical analysis using the one-sample Poisson hypothesis test.By completing this unit, students will learn to:Understand the background and relevance of quality control in a production environment with tight customer specifications.Work with real sample data and interpret its structure, including batch numbers, sample sizes, and detected defects.Learn key statistical terms:Total Occurrences - the total number of defects in all samples combinedSample Rate - the average number of defects per single partSample Mean - the average number of defects per batchUnderstand why the Poisson distribution is the appropriate choice for modeling such defect data, and how it compares to the Binomial, Normal, and Chi-square distributions.Visualize and interpret the Poisson probability distribution and understand its parameterization based on the mean (λ or μ).Perform a hypothesis test to estimate the population defect rate and assess whether the process is still in control.Learn the correct formulation of:Null Hypothesis (H₀): The average number of defects per batch is ≤ 25.Alternative Hypothesis (H₁): The average number of defects per batch is > 25.Select the exact Poisson method over the normal approximation due to its higher accuracy and better selectivity in low-count situations.Interpret the results of the hypothesis test, including:The calculated Poisson rate (λ) and mean (μ)The p-value and its implications for decision-makingRecognize the difference between sample-based values and population-based estimations, and how a hypothesis test can bridge this gap.Make data-driven quality management decisions:Even if the sample appears just above the customer threshold, the test might show the process is statistically still in control-with 95% confidence.In conclusion, students will be able to make informed, statistically sound recommendations to management-deciding whether immediate process improvements are necessary or if the current process performance is acceptable despite being close to the critical limit.This unit demonstrates how Six Sigma tools like the Poisson distribution and hypothesis testing are applied in real-world production environments, combining statistical theory with business impact.

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