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所在平台: Udemy |
课程主页: https://www.udemy.com/course/six-sigma-statistics-with-minitab-two-sample-t-test/
课程评论：没有评论
**课程名称：** Minitab 中的双样本 t 检验 - Tabtrainer 质量基准 **课程概述：** 本课程是 Tabtrainer® 认证系列的一部分，专注于在质量和制造领域应用实践统计分析。课程以奥运滑板组件测试的真实案例为基础，教授学员使用 Minitab® 进行双样本 t 检验。学员将比较来自两个独立供应商的螺钉的屈服强度，并判断性能差异是否具有统计学意义。 通过分步练习、可视化分析和软件驱动的测试，学员将增强对 p 值、置信区间以及从中得出可操作质量见解的理解能力。课程由 TÜV 认证的六西格玛培训师、德国年度教授（2023 年）Murat Mola 博士讲授，旨在帮助工程师、质量专业人士和生产专家掌握评估供应商绩效和做出增强工艺能力及产品一致性决策的技能。 **课程重点：** 本“双样本 t 检验”培训课程侧重于假设检验的应用，以评估两个独立数据组的总体均值是否存在显著差异。学员将通过涉及奥运滑板队使用的**高强度螺钉屈服强度评估**的真实场景进行学习。 通过互动练习和软件演示，学习者将熟练掌握统计检验、图形数据分析以及可操作结果的解读，从而在质量控制和供应商比较方面做出基于证据的决策。 **学习成果：** 完成本培训后，学员将能够： * **理解双样本 t 检验的概念和目的：** 解释零假设和备择假设，定义显著性水平并解读 p 值。 * **确定合适的样本量：** 进行功效分析，以确定针对特定置信水平和功效所需的样本量。 * **执行数据清洗和准备：** 识别和处理数据集中的异常值和缺失值。 * **有效可视化数据：** 使用点图和箱形图来分析和比较数据分布。理解图形摘要并解读置信区间. * **进行假设检验：** 执行双样本 t 检验和方差检验。解读检验结果，包括统计显著性和置信区间。 * **得出可操作的结论：** 根据均值和方差评估供应商绩效。为提高过程稳定性和产品质量提出建议。 * **专业地展示研究成果：** 使用图形工具和布局功能总结结果。利用软件集成创建面向利益相关者的专业报告。 * **将所学知识应用于实际场景：** 将统计见解转化为改进过程和确保质量的实际建议。 本次实践培训将提高学员将统计分析纳入决策过程的能力，从而在竞争环境中确保产品一致性和供应商可靠性。
Welcome to the Tabtrainer® Certified Series - your go-to learning platform for practical statistical analysis in quality and manufacturing.In this course, you'll master the 2-sample t-test using Minitab®, based on a real case from Olympic skateboard component testing. You'll compare yield strengths of screws from two independent suppliers and determine if the performance difference is statistically significant.With step-by-step exercises, visual analytics, and software-driven testing, you'll build confidence in interpreting p-values, confidence intervals, and drawing actionable quality insights.Taught by Prof. Dr. Murat Mola, TÜV-certified Six Sigma trainer and Professor of the Year 2023 in Germany, this course gives engineers, quality professionals, and production experts the skills to benchmark supplier performance and make decisions that strengthen process capability and product consistency.Focus:This training session, "2-Sample T-Test" focuses on the application of hypothesis testing to assess whether the population means of two independent data groups differ significantly. Participants will work through a real-world scenario involving yield strength evaluation of high-strength screws used in skateboards for the Olympic skateboard team.Through interactive exercises and guided software demonstrations, learners will develop expertise in statistical testing, graphical data analysis, and actionable result interpretation, empowering them to make evidence-based decisions in quality control and supplier benchmarking.Learning Outcomes:By the end of this training session, participants will be able to:Understand the Concept and Purpose of a Two-Sample T-Test:Explain the null and alternative hypotheses.Define the significance level and interpret p-values.Determine Appropriate Sample Sizes:Conduct power analyses to determine the necessary sample size for specified confidence levels and power.Perform Data Cleaning and Preparation:Identify and manage outliers and missing values in data sets.Visualize Data Effectively:Use dot plots and box plots to analyze and compare distributions.Understand graphical summaries and interpret confidence intervals.Conduct Hypothesis Testing:Execute a two-sample t-test and variance test.Interpret test results, including statistical significance and confidence intervals.Draw Actionable Conclusions:Evaluate supplier performance based on mean values and variance.Develop recommendations to improve process stability and product quality.Present Findings Professionally:Summarize results using graphical tools and layout features.Create professional reports for stakeholders using software integrations.Apply Learnings to Practical Scenarios:Transfer statistical insights into practical recommendations for process improvement and quality assurance.This hands-on training will enhance participants' capability to integrate statistical analysis into decision-making processes, ensuring product consistency and supplier reliability in competitive environments.
