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.
Este curso forma parte de Programa especializado: Data Science Methods for Quality Improvement
ofrecido por
Programa especializado: Data Science Methods for Quality ImprovementUniversidad de Colorado en BoulderAcerca de este Curso
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Fechas lÃmite flexibles
Restablece las fechas lÃmite en función de tus horarios.
Certificado para compartir
Obtén un certificado al finalizar
100Â % en lÃnea
Comienza de inmediato y aprende a tu propio ritmo.
Nivel intermedio
Background knowledge in statistics is helpful; visit Managing, Describing, and Analyzing Data as a prerequisite to this course. R / RStudio required.
Aprox. 9 horas para completar
Inglés (English)
SubtÃtulos: Inglés (English)
¿PodrÃa tu empresa beneficiarse de la capacitación de los empleados en las habilidades más demandadas?
Prueba Coursera para negociosQué aprenderás
Understand how to use, select, and interpret process control charts to identify special causes of variation
Create and interpret control charts for normal and non-normal distributions
Create and interpret control charts for discrete data
Analyze the capability of a process to meet customer specifications
Habilidades que obtendrás
- Analyzing a process for capability
- Analyzing a process for stability
- Make decisions about process improvement
- R Programming
- Rstudio
Fechas lÃmite flexibles
Restablece las fechas lÃmite en función de tus horarios.
Certificado para compartir
Obtén un certificado al finalizar
100Â % en lÃnea
Comienza de inmediato y aprende a tu propio ritmo.
Nivel intermedio
Background knowledge in statistics is helpful; visit Managing, Describing, and Analyzing Data as a prerequisite to this course. R / RStudio required.
Aprox. 9 horas para completar
Inglés (English)
SubtÃtulos: Inglés (English)
¿PodrÃa tu empresa beneficiarse de la capacitación de los empleados en las habilidades más demandadas?
Prueba Coursera para negocios
ofrecido por
Universidad de Colorado en Boulder
CU-Boulder is a dynamic community of scholars and learners on one of the most spectacular college campuses in the country. As one of 34 U.S. public institutions in the prestigious Association of American Universities (AAU), we have a proud tradition of academic excellence, with five Nobel laureates and more than 50 members of prestigious academic academies.
Comienza a trabajar para obtener tu maestrÃa
Este curso es parte del Master of Science in Data Science completamente en lÃnea de Universidad de Colorado en Boulder.
Si te aceptan en el programa completo, tus cursos cuentan para tu tÃtulo de grado.
Programa - Qué aprenderás en este curso
2 horas para completar
Understanding Process Variation, Process Control and Control Charts
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.
2 horas para completar
13 videos (Total 80 minutos), 1 lectura, 1 cuestionario
2 horas para completar
Xbar and R / Xbar and S Charts / X and MR Charts
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.
2 horas para completar
11 videos (Total 66 minutos)
2 horas para completar
X and Moving Range Charts for Non-Normally Distributed Data
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.
2 horas para completar
12 videos (Total 58 minutos)
2 horas para completar
Process Capability
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.
2 horas para completar
16 videos (Total 73 minutos)
Acerca de Programa especializado: Data Science Methods for Quality Improvement
Data analysis skills are widely sought by employers, both nationally and internationally. This specialization is ideal for anyone interested in data analysis for improving quality and processes in business and industry. The skills taught in this specialization have been used extensively to improve business performance, quality, and reliability.
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