Six Sigma Statistics with Minitab: One Sample t-Test

Master Six Sigma Statistics with Minitab – Taught by Prof. Dr. Murat Mola, Germany's Professor of the Year!


What you'll learn
Understand the Importance of Sample Size: Learn how to determine the appropriate sample size to ensure sufficient hypothesis power in statistical tests.
Perform Hypothesis Power Analysis: Analyze the discriminatory power of a hypothesis test and its relationship to sample size and target differences.
Identify and Mitigate Errors: Gain insights into type 1 (producer risk) and type 2 (customer risk) errors and their implications for decision-making.
Interpret t-test, power curves, and confidence intervals to derive meaningful conclusions about process performance and c

Requirements
No Specific Prior Knowledge Needed: The course is suitable for beginners, as all topics are explained in a practical and step-by-step manner.

Description
In this training unit, participants will master the application of a one-sample t-test with a primary focus on understanding hypothesis test power and determining optimal sample size. Through a real-world example drawn from a skateboard manufacturing process, this course emphasizes the critical role of statistical tools in assessing process stability and ensuring compliance with stringent customer specifications. Participants will learn how to interpret statistical results to make informed, data-driven decisions that can enhance quality control and minimize risk.Key Learnings:Statistical Foundations:Comprehend the principles of hypothesis testing to validate process performance.Differentiate between Type 1 errors (false alarms) and Type 2 errors (missed alarms), and understand their practical implications.Apply confidence intervals and p-values to assess the likelihood of process deviations and guide actionable decisions.Power Analysis:Measure the reliability of hypothesis tests through power analysis, ensuring that sample sizes are sufficient for robust conclusions.Learn how inadequate sample sizes can lead to incorrect recommendations, even with correctly performed tests.Calculate appropriate sample sizes to reduce errors and increase the reliability of statistical conclusions.Practical Application:Analyze compressive strength data of skateboard axles to evaluate process quality.Use statistical tools like normality tests and probability plots to detect and interpret process deviations.Develop actionable insights to decide whether interventions in production processes are necessary.Six Sigma Integration:Integrate hypothesis testing within Six Sigma methodologies to improve process performance.Leverage statistical insights to align production processes with customer requirements, minimizing defects and scrap rates.Participants will engage with real data sets and learn to use software tools like Minitab to conduct analyses, ensuring they leave with practical skills for immediate implementation in their quality management processes.

Professionals in Quality Management: Ideal for individuals working in process improvement, quality assurance, or Six Sigma initiatives.,Students and Beginners: Suitable for students or beginners interested in gaining practical skills in hypothesis testing and statistical analysis.,Managers and Decision-Makers: Beneficial for managers looking to make data-driven decisions and improve operational processes.,Data Enthusiasts: A great fit for anyone with an interest in statistical analysis and its practical applications in real-world scenarios.