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Reliability Analysis

Blog posts and articles about reliability analysis techniques applied to Lean and Six Sigma quality improvement projects.

Reliability analysis is the perfect tool for calculating the proportion of items that you can expect to survive for a specified period of time under identical operating conditions. Light bulbs—or lamps—are a classic example. Want to calculate the number of light bulbs expected to fail within 1000 hours? Reliability analysis can help you answer this type of question. But to conduct the analysis... Continue Reading
Pareto charts are a special type of bar chart you can use to prioritize almost anything. This makes them very useful in making sound decisions. For example, if you have several possible quality improvement projects, but not enough time or people to do them all now, you can use a Pareto chart to identify which projects have the most potential for making meaningful improvement. Pareto charts look... Continue Reading

7 Deadly Statistical Sins Even the Experts Make

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Every day, thousands of people withdraw extra cash for daily expenses. Each transaction may be small, but the total amount of cash dispersed over hundreds or thousands of daily transactions can be very high. But every bank branch has a fixed cash flow, which must be set without knowing what each customer will need on a given day. This creates a challenge for financial entities. Customers expect... Continue Reading
Since the release of Minitab Express in 2014, we’ve often received questions in technical support about the differences between Express and Minitab 17.  In this post, I’ll attempt to provide a comparison between these two Minitab products. What Is Minitab 17? Minitab 17 is an all-in-one graphical and statistical analysis package that includes basic analysis tools such as hypothesis testing,... Continue Reading
The ultimate goal of most quality improvement projects is clear: reducing the number of defects, improving a response, or making a change that benefits your customers. We often want to jump right in and start gathering and analyzing data so we can solve the problems. Checking your measurement systems first, with methods like attribute agreement analysis or Gage R&R, may seem like a needless waste... Continue Reading
We hosted our first-ever Minitab Insights conference in September, and if you were among the attendees, you already know the caliber of the speakers and the value of the information they shared. Experts from a wide range of industries offered a lot of great lessons about how they use data analysis to improve business practices and solve a variety of problems. I blogged earlier about five key... Continue Reading
We’ve got a plethora of case studies showing how businesses from different industries solve problems and implement solutions with data analysis. Take a look for ideas about how you can use data analysis to ensure excellence at your business! Boston Scientific, one of the world’s leading developers of medical devices, is just one organization who has shared their story. A team at their Heredia,... Continue Reading
The other day I was talking with a friend about control charts, and I wanted to share an example one of my colleagues wrote on the Minitab Blog.  Looking back through the index for "control charts" reminded me just how much material we've published on this topic. Whether you're just getting started with control charts, or you're an old hand at statistical process control, you'll find some valuable... Continue Reading
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... Continue Reading
When you perform a statistical analysis, you want to make sure you collect enough data that your results are reliable. But you also want to avoid wasting time and money collecting more data than you need. So it's important to find an appropriate middle ground when determining your sample size. Now, technically, the Major League Baseball regular season isn't a statistical analysis. But it does kind... Continue Reading
It's been called a "demographic watershed".  In the next 15 years alone, the worldwide population of individuals aged 65 and older is projected to increase more than 60%, from 617 million to about 1 billion.1 Increasingly, countries are asking themselves: How can we ensure a high quality of care for our growing aging population while keeping our healthcare costs under control? The answer? More... Continue Reading
Earlier this month, PLOS.org published an article titled "Ten Simple Rules for Effective Statistical Practice." The 10 rules are good reading for anyone who draws conclusions and makes decisions based on data, whether you're trying to extend the boundaries of scientific knowledge or make good decisions for your business.  Carnegie Mellon University's Robert E. Kass and several co-authors devised... Continue Reading
You often hear the data being blamed when an analysis is not delivering the answers you wanted or expected. I was recently reminded that the data chosen or collected for a specific analysis is determined by the analyst, so there is no such thing as bad data—only bad analysis.  This made me think about the steps an analyst can take to minimise the risk of producing analysis that fails to answer... Continue Reading
Most of us have heard a backwards way of completing a task, or doing something in the conventionally wrong order, described as “putting the cart before the horse.” That’s because a horse pulling a cart is much more efficient than a horse pushing a cart. This saying may be especially true in the world of statistics. Focusing on a statistical tool or analysis before checking out the condition of your... Continue Reading
If you want to convince someone that at least a basic understanding of statistics is an essential life skill, bring up the case of Lucia de Berk. Hers is a story that's too awful to be true—except that it is completely true. A flawed analysis irrevocably altered de Berk's life and kept her behind bars for five years, and the fact that this analysis targeted and harmed just one person makes it more... Continue Reading
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. As a refresher, B10 life refers to the time at which 10% of... Continue Reading
Reliability and survival analysis is used most frequently in manufacturing. Companies use these methods to estimate the proportion of units that will fail within, or survive beyond, a given period of time. But could these reliability and survival analysis techniques prove useful in a zombie apocalypse, too? Today's blog post explores that chilling scenario.  Think. This is what Zachary is telling... Continue Reading
Since it's the Halloween season, I want to share how a classic horror film helped me get a handle on an extremely useful statistical distribution.  The film is based on John W. Campbell's classic novella "Who Goes There?", but I first became  familiar with it from John Carpenter's 1982 film The Thing.   In the film, researchers in the Antarctic encounter a predatory alien with a truly frightening... Continue Reading
Whatever industry you're in, you're going to need to buy supplies. If you're a printer, you'll need to purchase inks, various types of printing equipment, and paper. If you're in manufacturing, you'll need to obtain parts that you don't make yourself.  But how do you know you're making the right choice when you have multiple suppliers vying to fulfill your orders?  How can you be sure you're... Continue Reading
Imagine a multi-million dollar company that released a product without knowing the probability that it will fail after a certain amount of time. “We offer a 2 year warranty, but we have no idea what percentage of our products fail before 2 years.” Crazy, right? Anybody who wanted to ensure the quality of their product would perform a statistical analysis to look at the reliability and survival of... Continue Reading