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所在平台: Udemy |
课程主页: https://www.udemy.com/course/six-sigma-statistics-with-minitab-box-plot-analysis/
课程评论：没有评论
课程名称：Minitab中的箱形图分析 - 质量团队的Tabtrainer 课程概述：欢迎参加Tabtrainer®认证系列课程，这是您获取实用数据可视化和质量过程洞察的可靠来源。在本培训单元中，您将掌握使用Minitab®进行的箱形图分析，重点关注来自Smartboard公司的真实生产废料数据。您将学习如何在工作日之间可视化趋势，通过Grubbs' Test检测异常值，以及通过宏和命令行脚本自动化分析。通过750个真实数据点，您将获得构建和解释箱形图的实际经验，创建单独的值图，并通过自定义的每日质量菜单简化重复分析任务。 本课程由获得TÜV认证的六西格玛培训师、2023年德国年度教授Murat Mola教授授课，旨在帮助工程师、分析师和质量团队快速、一致地做出数据驱动的决策。该培训单元全面探讨了箱形图分析，以识别分类数据组的趋势和差异。课程使用Smartboard公司的真实案例，演示了如何分析工作日的生产废料率。 参与者将使用包含750个值的预处理数据集，代表每日废料率。通过实践练习，参与者将学习如何构建、解释和自定义箱形图，以揭示优化过程的宝贵见解。 学习目标包括： - 理解箱形图分析的基本概念，包括四分位数、中位数和四分位差(IQR)的计算。 - 探索四种类型的箱形图及其在分类变量和响应变量中的应用。 - 使用统计工具识别生产趋势和变异，如特定工作日的废料率较高。 - 通过Grubbs' Test进行异常值分析，以检测异常并验证其原因。 - 创建和使用单独值图进行详细数据可视化。 高级技能发展： - 通过在Exec格式中创建宏，自动化重复分析（如箱形图、异常值测试）。 - 自定义新的每日质量分析菜单，以一键执行重复任务，提高常规操作的效率。 - 将其他统计元素（如算术平均值和趋势线）纳入可视化中。 实际应用：参与者将分析特定工作日的箱形图，以识别和解决生产不一致问题。课程强调： - 全周生产过程稳定性的重要性。 - 使用自动化Minitab宏简化工作流程的技术。 - 保存和导出项目结果，以确保一致的报告。 本实用课程为参与者提供了使用Minitab优化质量过程的必要工具和技术，确保在动态商业环境中进行稳健、可重复的统计分析。
Welcome to the Tabtrainer® Certified Series - your trusted source for practical data visualization and quality process insights.In this training unit, you'll master Box Plot Analysis using Minitab®, focusing on real production scrap data from the Smartboard Company. You'll learn how to visualize trends across weekdays, detect outliers using Grubbs' Test, and automate your analysis through macros and command line scripting.With 750 real data points, you'll gain hands-on experience constructing and interpreting box plots, creating individual value plots, and streamlining recurring analysis tasks with a custom Daily Quality Menu.Taught by Prof. Dr. Murat Mola, TÜV-certified Six Sigma trainer and Professor of the Year 2023 in Germany, this course empowers engineers, analysts, and quality teams to make data-driven decisions quickly and consistently.This training unit provides a comprehensive exploration of Box Plot Analysis to identify trends and differences in categorical data groups. Designed for professionals in quality management, this course uses real-world scenarios from the Smartboard Company to demonstrate the analysis of production scrap rates across weekdays.Participants will work with a pre-processed dataset containing 750 values representing daily scrap rates. Through hands-on exercises, participants will learn to construct, interpret, and customize box plots to uncover valuable insights for process optimization.Key Learning Objectives:Understand the fundamentals of Box Plot Analysis, including the calculation of quartiles, median, and interquartile range (IQR).Explore the four types of box plots and their application based on categorical and response variables.Identify production trends and variability, such as higher scrap rates on specific weekdays, using statistical tools.Conduct an Outlier Analysis with Grubbs' Test to detect anomalies and validate their causes.Create and use Individual Value Plots for detailed data visualization.Advanced Skills Development:Automate repetitive analyses (e.g., box plots, outlier tests) by creating macros in Exec format using the Command Line History.Customize a new Daily Quality Analysis menu for one-click execution of recurring tasks, enhancing efficiency in routine operations.Incorporate additional statistical elements, such as arithmetic mean and trend lines, into visualizations.Practical Application: Participants will analyze weekday-specific box plots to identify and address production inconsistencies. The course also emphasizes:The importance of stable production processes across the week.Techniques to streamline workflows with automated Minitab macros.Saving and exporting project results for consistent reporting.This hands-on course equips participants with essential tools and techniques to optimize quality processes using Minitab, ensuring robust, repeatable statistical analyses in dynamic business environments.
