|
所在平台: Coursera |
课程主页: https://www.coursera.org/learn/stability-and-capability-in-quality-improvement
课程评论：没有评论
课程名称：质量改进中的稳定性和能力 课程概述：本课程将指导您分析过程的稳定性和统计控制中的数据，强调在进行统计假设检验之前，确保过程稳定的重要性。您将使用R软件创建连续和离散数据的统计过程控制图，并运用基于概率的控制规则分析数据集的统计控制。此外，您还将学习如何评估一个过程的能力，以满足内部或外部的规范，并对过程改进做出决策。 本课程可作为库拉多大学数据科学硕士（MS-DS）学位的一部分，通过Coursera平台获得学分。MS-DS项目是一门跨学科课程，由库拉多大学的应用数学、计算机科学、信息科学等系的教师共同授课。该项目采用基于绩效的招生方式，无需申请流程，适合拥有计算机科学、信息科学、数学和统计学等广泛本科教育和/或专业经验的个人。 课程大纲： 1. 模块名称：理解过程变异、过程控制和控制图 描述：本模块将教您如何定义一个过程并将其拆分为组成部分，以识别潜在的变异来源。学习如何使用控制图将变异分类为共同原因和特殊原因，了解田口损失函数及其与质量哲学的关联。 2. 模块名称：Xbar和R / Xbar和S图 / X和偏差移动图 描述：本模块讲解如何根据样本大小和数据类型选择合适的图表，以及如何在R中创建和解释变量数据的控制图。 3. 模块名称：针对非正态分布数据的X和偏差移动图 描述：学习如何在基础分布非正态的情况下创建X和偏差移动图，计算控制限并解释控制图以检查统计控制和稳定性。 4. 模块名称：过程能力 描述：比较过程变异与客户规范，并学习与能力衡量相关的三种指标和性能衡量的三种指标，特别是在数据非正态的情况下。 5. 模块名称：离散数据的控制图 描述：学习如何为离散数据创建和分析控制图，区分二项数据与泊松分布数据，以选择合适的控制图。 通过完成本课程，您将掌握质量改进中的统计过程控制和能力评估的核心知识，为进一步的过程改进打下坚实的基础。
Name: Understanding Process Variation, Process Control and Control Charts
Description:In this module, you will learn how to define a process and break it down into components for the purpose of identifying potential sources of variation. You will learn how to classify variation into common and special causes through the use of a control chart. You’ll discover the Taguchi Loss function, and how it relates to the philosophy of quality, and its association to the product control and process control cycles. You will learn the basic anatomy of a control chart as well as the process used to create a control chart, and common errors encountered when using a control chart in practice. You will be able to calculate an appropriate sample size, as well as determine when a process is in control or out of control based on statistical rules.
Name:Xbar and R / Xbar and S Charts / X and MR Charts
Description:In this module, you will learn how to select the appropriate chart given information on sample size and data type. You’ll learn how to create and interpret control charts with subgroups for variables data, as well as how to create them in R. You will also create and interpret control charts with a sample size of one data that is normally distributed. You'll learn how to monitor other statistics using the Individuals and Moving Range Chart. Finally, you will interpret the control charts for statistical control / stability.
Name:X and Moving Range Charts for Non-Normally Distributed Data
Description:In this module, you will learn how to create an X and Moving Range Chart when the underlying distribution is not normally distributed. You’ll learn how to calculate control limits for the X and MR Charts with LogNormal transformed distribution and exponential distribution. Additionally, you will learn how to fit a distribution to the data and calculate control limits associated with the selected distribution. Finally, you will interpret the control charts for statistical control / stability.
Name:Process Capability
Description:In this module, you will learn how to compare process variation to customer specifications. You’ll learn the three indices associated with capability measures and the three indices associated with performance measures. Additionally, you will learn to assess capability and performance when the data are not normally distributed.
Name:Control Charts for Discrete Data
Description:In this module, you will learn how to create and analyze control charts for discrete data. You will learn how to differentiate between data that are Binomial and data that are Poisson distributed in order to select the appropriate control chart. Additionally, you will learn to assess capability using an appropriate discrete probability model.
In this course, you will learn to analyze data in terms of process stability and statistical control and why having a stable process is imperative prior to perform statistical hypothesis testing. You will create statistical process control charts for both continuous and discrete data using R software. You will analyze data sets for statistical control using control rules based on probability. Additionally, you will learn how to assess a process with respect to how capable it is of meeting specifications, either internal or external, and make decisions about process improvement. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
