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所在平台: Udemy |
课程主页: https://www.udemy.com/course/six-sigma-statistics-with-minitab-one-sample-t-test/
课程评论：没有评论
课程名称：Minitab中的单样本t检验与功效分析 - Tabtrainer 课程概述：欢迎加入Tabtrainer®认证系列，这是您在制造业中实现统计精度和流程卓越的可信平台。在这门课程中，您将掌握使用Minitab®进行的单样本t检验和功效分析，课程以滑板轴制造的真实生产案例为框架。您将学习评估过程的稳定性、计算可靠结论所需的样本大小，并降低不必要的错误决策带来的高昂成本。此外，您将了解置信区间、p值及第一类和第二类错误如何影响质量决策，如何使用正态性测试和概率图来验证您的假设。 本课程由德国2023年度教授获奖者、TÜV认证的六西格玛专家Murat Mola教授授课，适合希望将统计准确性与实际生产洞察结合的工程师、质量专业人士和六西格玛从业者。在此次培训中，参与者将深入掌握单样本t检验的应用，重点理解假设检验的功效及确定最佳样本大小。通过滑板制造过程中的真实案例，本课程强调了统计工具在评估过程稳定性和确保符合严格客户规范方面的重要作用。参与者将学习如何解读统计结果，从而做出数据驱动的明智决策，以提升质量控制并降低风险。 主要学习内容： - 统计基础：理解假设检验的原理，从而验证过程性能。区分第一类错误（假警报）与第二类错误（漏警报），理解其实际影响。应用置信区间和p值评估过程偏差的可能性并指导可行决策。 - 功效分析：通过功效分析来衡量假设检验的可靠性，确保样本大小足够，以得出可靠结论。了解不充分的样本规模可能导致的错误推荐，即使测试执行正确。计算适当的样本大小以减少错误并提高统计结论的可靠性。 - 实际应用：分析滑板轴的抗压强度数据以评估过程质量。使用正态性测试和概率图等统计工具检测和解读过程偏差。开发可行性见解，以决定是否需要对生产过程进行干预。 - 六西格玛整合：将假设检验融入六西格玛方法论中，以提高过程性能。利用统计洞察对齐生产过程与客户需求，尽量降低缺陷和废料率。 参与者将与真实数据集互动，并学习如何利用Minitab等软件工具进行分析，确保他们能够掌握实际技能，并能立即在质量管理过程中应用。
Welcome to the Tabtrainer® Certified Series - your trusted platform for statistical precision and process excellence in manufacturing.In this course, you'll master the One-Sample T-Test and Power Analysis using Minitab®, framed by a real production case from skateboard axle manufacturing. You'll learn to evaluate process stability, calculate the required sample size for reliable conclusions, and reduce the risk of costly false decisions.Understand how confidence intervals, p-values, and Type I/II errors impact quality decisions, and how to use normality tests and probability plots to validate your assumptions.Taught by Prof. Dr. Murat Mola, TÜV-certified Six Sigma expert and Professor of the Year 2023 in Germany, this course is ideal for engineers, quality professionals, and Six Sigma practitioners who want to combine statistical accuracy with practical production insight.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.
