Tips and Techniques for Statistics and Quality Improvement

Blog posts and articles about using Minitab software in quality improvement projects, research, and more.

When data are collected in subgroups, it’s easy to understand how the variation can be calculated within each of the subgroups based the subgroup range or the subgroup standard deviation.

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A while back, I offered an overview of process capability analysis that emphasized the importance of matching your analysis to the distribution of your data.

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The Cp and Cpk are well known capability indices commonly used to ensure that a process spread is as small as possible compared to the tolerance interval (Cp), or that it stays well within specifications (Cpk).

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Minitab's capability analysis output gives you estimates of the capability indices Ppk and Cpk, and we receive many questions about the difference between them. Some of my colleagues have taken other approaches to explain the difference between Ppk and Cpk, so I wanted to show you how they differ by detailing precisely how each one is calculated. 

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In technical support, we frequently receive calls from Minitab users who have questions about the differences between Cpk and Ppk. 

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In quality initiatives such as Six Sigma, practitioners often need to assess the capability of a process to make sure it can meet specifications, or to verify that it produces good parts. While many Minitab users are familiar with the capability analysis tools in the Stat menu and in Minitab’s Assistant, the Assistant includes a less-frequently used featurethe Capability Snapshot.

What Is the Capability Snapshot, and...

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Remember "The Little Engine That Could," the children's story about self-confidence in the face of huge challenges? In it, a train engine keeps telling itself "I think I can" while carrying a very heavy load up a big mountain. Next thing you know, the little engine has done it...but until that moment, the outcome was uncertain.

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Ughhh... your process is producing some parts that don't meet your customer's specifications! Fortunately, after a little hard work, you find a way to improve the process.

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Transformations and non-normal distributions are typically the first approaches considered when the when the Normality test fails in a capability analysis. These approaches do not work when there are extreme outliers because they both assume the data come from a single common-cause variation distribution. But because extreme outliers typically represent special-cause variation, transformations and non-normal...

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Capability statistics are wonderful things. These statistics tell you how well your process is meeting the specifications that you have. But there are so many capability statistics that it's worth taking some time to understand how they’re useful together.

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