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所在平台: Udemy |
课程主页: https://www.udemy.com/course/full-factorial-doe-in-minitab-tabtrainer-masterclass/
课程评论：没有评论
**课程名称：** Minitab 中的全因子实验设计 (Full Factorial DOE) - Tabtrainer 大师班 **课程概述：** 本课程是 Tabtrainer® 大师班系列的一部分，专为质量和流程工程领域的专业人士设计，旨在提供高级统计培训。本专家级课程专注于 Minitab 软件中的全因子实验设计 (DOE)，旨在帮助工程师、研究人员和六西格玛专业人士自信地规划、执行和解读工业环境中的二维因子实验。课程基于 Smartboard 公司的实际案例研究，将科学严谨性与实践应用相结合。 课程由 TÜV 认证讲师、Tabtrainer® 创始人 Prof. Dr. Murat Mola 教授开发和讲授，他曾荣获“2023 年度德国教授”称号。Mola 教授以其清晰、精准和注重工业实践的教学风格，有效弥合了统计理论与实际应用之间的差距。 **课程内容：** 本课程深入系统地介绍了全因子实验设计 (DOE)，这是应用统计学中识别和量化因子与响应变量之间因果关系的最强大工具之一。课程重点强调在 Minitab 平台中进行全因子实验的设计、执行和分析，适用于研究和工业环境。 课程将引导学员完成 DOE 的整个生命周期，从规划、数据收集到统计建模和交互效应的解读。整个过程遵循 PDCA 循环，并符合工业质量标准（例如 AIAG、VDA Volume 5 和 ISO 13053）。 课程的核心内容是构建和评估二维全因子实验，包括如何使用重复试验、中心点、编码与非编码设计，以及利用 Minitab 内置的分析和图形工具，严谨地解读主效应和多水平交互作用。 **关键学习目标：** 完成本课程后，学员将能够： * 理解全因子 DOE 的理论基础和统计假设。 * 使用 Minitab 设计正交和平衡的全因子实验。 * 定义实验边界、分块和重复结构。 * 识别统计上显著的主效应、二阶和三阶交互作用。 * 解读别名结构、混淆效应和设计分辨率级别。 * 利用编码系数和因子图理解效应的大小和方向。 * 进行正态性检验、残差诊断，并检查 R² 指标的模型有效性。 * 使用立方体图、主效应图和交互作用图可视化实验空间。 * 执行响应优化，包括期望函数、置信区间和预测区间。 * 以统计上的信心向技术和非技术相关人员沟通结果。
Welcome to the Tabtrainer® Masterclass Series - the professional learning standard for advanced statistical training in quality and process engineering.This expert-level course on Full Factorial Design of Experiments (DOE) in Minitab empowers engineers, researchers, and Six Sigma professionals to confidently plan, execute, and interpret 2-level factorial experiments in real industrial settings. The training is based on proven case studies from the Smartboard Company and combines scientific rigor with practical application.Developed and taught by Prof. Dr. Murat Mola, TÜV-certified instructor and founder of Tabtrainer®, this course bridges the gap between statistical theory and actionable results. As Germany's "Professor of the Year 2023", Prof. Mola ensures clarity, precision, and maximum relevance for industrial practice.Course DescriptionThis course offers an in-depth, scientifically grounded introduction to the Full Factorial Design of Experiments (DOE), one of the most powerful tools in applied statistics for identifying and quantifying cause-and-effect relationships between factors and response variables. The course places strong emphasis on the use of Minitab as a professional platform for designing, executing, and analyzing full factorial experiments in both research and industrial settings.Participants are guided through the entire DOE lifecycle, from planning and data collection to statistical modeling and interpretation of interaction effects. The course follows the PDCA cycle and complies with industrial quality standards (e.g., AIAG, VDA Volume 5, and ISO 13053).The key focus lies in the construction and evaluation of 2-level full factorial experiments, including the use of replicates, center points, and coded vs. uncoded designs, and the rigorous interpretation of main effects and multi-level interactions using Minitab's built-in analytical and graphical tools.Key Learning ObjectivesBy the end of the course, participants will be able to:Understand the theoretical foundation and statistical assumptions of full factorial DOEDesign orthogonal and balanced full factorial experiments using MinitabDefine experimental boundaries, blocking, and replicate structureIdentify statistically significant main effects, two-way, and three-way interactionsInterpret alias structures, confounding effects, and design resolution levelsUse coded coefficients and factorial plots to understand effect magnitudes and directionsPerform normality testing, residual diagnostics, and check model validity via R² metricsVisualize experimental space with cube plots, main effect plots, and interaction plotsConduct response optimization including desirability functions, confidence intervals, and prediction intervalsCommunicate results to technical and non-technical stakeholders with statistical confidence
