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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/six-sigma-define-measure-advanced
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课程名称：六西格玛高级定义与测量阶段 概述：本课程适合希望进一步深入六西格玛，并加强和扩展六西格玛及精益基本组成部分知识的学员。六西格玛技能在国内外的雇主中广受欢迎，已被证明能够改善业务流程和绩效。本课程将深入探讨DMAIC结构中的“定义”和“测量”阶段相关的原则和工具。 课程建议在学习之前完成“黄带专业化”及“六西格玛与组织（高级）”课程。 在课程中，讲师将介绍并应用一些六西格玛的关键工具和指标，提供团队动态和绩效、流程分析、概率、统计、统计分布、数据收集与总结、测量系统分析、流程与绩效能力以及与六西格玛和精益相关的探索性数据分析的高级知识。 每个模块包括阅读材料、视频和测验，以确保您理解所学习的材料和概念。 注册课程后可以在线访问课程内容、项目和资源，但不包括《认证六西格玛绿带手册（第二版）》一书。该书并非完成作业的必备材料，但在该领域的专业人士中被广泛认可，同时对于希望深入六西格玛并最终获得美国质量协会（ASQ）等专业机构认证的学员极为推荐。 课程大纲简要如下： 1. **黄带复习 - 团队基础**：学习团队发展、不同类型的团队、决策工具和沟通工具。 2. **流程分析与文档化**：学习流程图和泳道图的应用，书面程序和工作指示的应用等。 3. **概率与统计 - 第一部分**：学习互斥事件和独立事件与概率的关系，解决基本概率问题的技巧。 4. **概率与统计 - 第二部分**：解决与组合和排列相关的概率问题，学习中心极限定理及其在置信区间中的应用。 5. **统计分布**：了解概率分布的基本特征，包括二项分布、正态分布、泊松分布、t分布、卡方分布和F分布。 6. **数据收集与总结及测量系统分析**：学习数据类型与来源、描述性统计分析、数据可视化方法及测量系统分析的重要性。 7. **流程与绩效能力及探索性数据分析**：使用统计工具分析数据，学习自然过程限制与规格限制的区分，计算Cp和Cpk值，以及处理过程能力研究。 通过这些模块的学习，您将掌握六西格玛的高级技术和应用，提升您的专业能力。
Name:Yellow Belt Refresher - Team Basics
Description:This module is a Yellow Belt Refresher Area, since some learners may have entered this course without having taken the previous yellow belt material. This refresher area does not serve as a "replacement" for taking the yellow belt specialization. It is only a "patch" for those who already have some knowledge of six sigma, but find the yellow belt specialization to be too introductory for their current situation. If you find yourself struggling with the green belt material in this course, it is highly recommended that you travel through the yellow belt specialization before attempting the green belt content. In this module you will learn about team development, different types of teams, tools for decision making, and tools for communication.
Name:Process Analysis and Documentation
Description:In this module you will learn the application of process maps and flow charts swim lane. You will also about the application of: written procedures and works instructions, CEDAC, the Pareto diagram, and the relationship matrix.
Name:Probability and Statistics - pt1
Description:In this module, you will learn how mutually-exclusive events and independent events relate to probability. You will also learn how to solve basic probability problems, including those that require the addition, multiplicative, and conditional rules of probability.
Name:Probability and Statistics - pt2
Description:In this module, you will learn how to solve probability problems related to combinations and permutations. You will also learn about central limit theorem and how to apply it to the concept of confidence intervals. This module also touches on descriptive and inferential statistics.
Name:Statistical Distribution
Description:In this module, you will learn about the basic characteristics of probability distributions. You will also learn about binomial, normal, Poisson, t-, chi-square, and F-distributions.
Name:COLLECTING AND SUMMARIZING DATA & MEASURING SYSTEM ANALYSIS
Description:In this module, you will learn the different types and sources of data. You will also learn: how to classify groups of data for descriptive statistics analysis, different ways to graphically display data, how to express the importance of measurement systems analysis, and the difference between precision and accuracy. This module also begins with a Yellow Belt Refresher Area, since some learners may have entered this course without having taken the previous yellow belt material. This refresher area does not serve as a "replacement" for taking the yellow belt material. It is only a "patch" for those who already have some knowledge of six sigma, but find the yellow belt specialization to be too introductory for their current situation. If you find yourself struggling with the green belt material in this course, it is highly recommended that you travel through the yellow belt specialization before attempting the green belt content.
Name:PROCESS AND PERFORMANCE CAPABILITY & EXPLORATORY DATA ANALYSIS
Description:In this module, you will learn how to use statistical tools and analyze data, including skills like: how to distinguish between natural process limits and specification limits, how to calculate the value of Cp and Ck, how to deal with process capability studies. This module also addresses multi-vari studies, the the correlation coefficient, and linear regression.
This course is for you if you are looking to dive deeper into Six Sigma or strengthen and expand your knowledge of the basic components of green belt level of Six Sigma and Lean. Six Sigma skills are widely sought by employers both nationally and internationally. These skills have been proven to help improve business processes and performance. This course will take you deeper into the principles and tools associated with the "Define" and "Measure" phases of the DMAIC structure of Six Sigma. It is highly recommended that you complete the "Yellow Belt Specialization" and the course "Six Sigma and the Organization (Advanced)" before beginning this course. In this course, your instructors will introduce you to, and have you apply, some of the tools and metrics that are critical components of Six Sigma. This course will provide you with the advanced knowledge of team dynamics and performance, process analysis, probability, statistics, statistical distributions, collecting and summarizing data, measurement systems analysis, process and performance capability, and exploratory data analysis associated with Six Sigma and Lean. Every module will include readings, videos, and quizzes to help make sure you understand the material and concepts that are studied. Registration includes online access to course content, projects, and resources but does not include the companion text The Certified Six Sigma Green Belt Handbook (2nd edition). The companion text is NOT required to complete the assignments. However, the text is a recognized handbook used by professionals in the field. Also, it is a highly-recommended text for those wishing to move forward in Six Sigma and eventually gain certification from professional agencies such as American Society for Quality (ASQ).
