Tips and Techniques for Statistics and Quality Improvement

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

Meredith Griffith

My name is Meredith Griffith and I am a technical project manager at Minitab. I received a B.S. in Statistics from Virginia Tech and am finishing Penn State's M.S. program in Industrial Engineering with a focus on Quality Engineering. Having spent time in Quality Engineering and Marketing, I'm always looking for different ways to apply my knowledge of statistics and passion for quality improvement!
Meredith Griffith

Before cutting an expensive piece of granite for a countertop, a good carpenter will first confirm he has measured correctly. Acting on faulty measurements could be costly.

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I thought 3 posts would capture all the thoughts I had about B10 Life. That is, until this question appeared on the Minitab LinkedIn group:

In case you missed it, my first post, How to Calculate B10 Life with Statistical Software, explains what B10 life is and how Minitab calculates this value. My second post, How to Calculate BX Life, Part 2, shows how to compute any BX life in Minitab. But before I round out my BX...

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In the first partof this series, we looked at a case study where staff at a hospital used ATP swab tests to test 8 surfaces for bacteria in 10 different hospital rooms across 5 departments. ATP measurements below 400 units pass the swab test, while measurements greater than or equal to 400 units fail the swab test and require further investigation.

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Working with healthcare-related data often feels different than working with manufacturing data. After all, the common thread among healthcare quality improvement professionals is the motivation to preserve and improve the lives of patients. Whether collecting data on the number of patient falls, patient length-of-stay, bed unavailability, wait times, hospital acquired-infections, or readmissions, human lives are...

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When I wrote How to Calculate B10 Life with Statistical Software, I promised a follow-up blog post that would describe how to compute any “BX” lifetime. In this post I’ll follow through on that promise, and in a third blog post in this series, I will explain why BX life is one of the best measures you can use in your reliability analysis.

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I was recently asked a couple of questions about stability studies in Minitab.

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I recently guest lectured for an applied regression analysis course at Penn State. Now, before you begin making certain assumptions—because as any statistician will tell you, assumptions are important in regression—you should know that I have no teaching experience whatsoever, and I’m not much older than the students I addressed.

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Many of the things you need to monitor can be measured in a concrete, objective way, such as an item's weight or length. But, many important characteristics are more subjective, such as the collaborative culture of the workplace, or an individual's political outlook.

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To choose the right statistical analysis, you need to know the distribution of your data. Suppose you want to assess the capability of your process. If you conduct an analysis that assumes the data follow a normal distribution when, in fact, the data are nonnormal, your results will be inaccurate. To avoid this costly error, you must determine the distribution of your data.

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Over the last year or so I’ve heard a lot of people asking, “How can I calculate B10 life in Minitab?” Despite being a statistician and industrial engineer (mind you, one who has never been in the field like the customers asking this question) and having taken a reliability engineering course, I’d never heard of B10 life. So I did some research.

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