Six Sigma projects typically involve a significant amount of statistical analysis. Black Belts commonly use Minitab or JMP to perform these analyses. These products are fairly limited in capability and quite expensive. This document provides an overview of using the very capable open source application R to perform those analyses common in Six Sigma projects.
Type of Material:
Tutorial
Recommended Uses:
In-class syndicate/team-based tutorial activity.
Technical Requirements:
Firefox ver 85.0.1
Adobe reader ver 21.001.20135
Microsoft 365 ver 16.0.13530.20440
Identify Major Learning Goals:
Users will understand how to use an open-source application (R) to run statistical analyses.
Users will have learn that R provides the free and user-friendly platform needed.
Target Student Population:
This is recommended for use in professional situations by Six Sigma experts which are typically found in organizations.
However, this may be of interest in upper-level undergraduate open-source software discussions and demonstrations, or as a way to demonstrate statistical analysis in upper-level undergraduate and potentially in graduate levels.
Prerequisite Knowledge or Skills:
This is a sophisticated, complex tutorial intended for those who can run R open-source applications in Red Hat /Linux. Knowledge of open source software, knowledge of statistical software, knowledge of statistics, and knowledge of Six Sigma, preferably at the expert level, are strongly recommended. Typically, certified Black Belts in the Six Sigma process run statistical analyses.
Content Quality
Rating:
Strengths:
Six Sigma projects typically involve a significant amount of statistical analysis. Black Belts commonly use Minitab or JMP to perform these analyses. These products are fairly limited in capability and quite expensive.
This document provides an overview of using the very capable open-source application R to perform those analyses common in Six Sigma projects. R is a free and open-source software tool.
Often, individuals wishing to perform the types of analysis employed in Six Sigma are frustrated by the limitations of the tools at their disposal, and the cost of more capable tools. R avoids those frustrations.
R is an extremely versatile and powerful statistical tool. However, unlike the commercial desktop tools, R is a command-line application. The command line makes it easier to replicate analyses, make subtle changes to analyses in a controlled fashion, and use revision control tools to manage those analyses.
This is very specific; instructions are clear about how this can be used and in what IT environment.
Notes, warnings important items are highlighted and provided throughout the document relative to the topics.
The document requests feedback from anyone who uses the information.
Concerns:
This paper does not attempt to teach how to be an effective Six Sigma Black Belt because it requires far more than a little statistics, and could only be achieved under the mentorship of an experienced Master Black Belt.
Rather, this article discusses how common analyses used in Six Sigma may be performed using R.
This is not a typical concern, but this is a point about how much experience is necessary to use these instructions, and that users must be able to work in open-access environments using Red Hat/Linux and should have a significant amount of experience in Six Sigma as represented by the Black Belt level.
This is clearly noted as a DRAFT document. Again, this is not necessarily a concern but users need to be aware
Potential Effectiveness as a Teaching Tool
Rating:
Strengths:
The instructional document is broken down into multiple sections by beginning to look first at the data and then by customizing, running scripts and functions, looking at control charts, process capability and hypothesis testing (to name a few).
This appears to be a step-by-step approach with explanations provided throughout.
The table of contents makes it easy to locate specific topics.
The effectiveness of this learning material is dependent upon the ability to maximize the usage of R platform as a free tool. However, this tutorial is designed as a manual guide to actual utilization of R in Six Sigma.
Concerns:
Again, while this is not necessarily concerning, the reader/user needs to be aware that this material requires expertise to use and to understand and it is not something that can typically be used in an undergraduate course or by inexperienced professionals.
There are a number of graphical user interfaces available for R, however this paper does not address them.
Ease of Use for Both Students and Faculty
Rating:
Strengths:
Information is provided on how to navigate, how to insert special characters, and explains how the menu names and items are applied.
Spacing is discussed, as are other technical items.
Conventions are demonstrated and examples are provided. Instructions are provided on how to submit reports of software bugs.
Concerns:
The reader/user needs to be aware that this material requires expertise to use and to understand and it is not something that can typically be used in an undergraduate course or by inexperienced professionals.
Expertise is required in both statistical analysis procedures, the Six Sigma process, and in using open-source software.
Creative Commons:
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