Blog posts and articles about the statistical method called DOE (Design of Experiments) in quality improvement.

In my last post, I wrote about making a cluttered data set easier to work with by removing unneeded columns entirely, and by displaying just those columns you want to work with now. But too much unneeded data isn't always the problem. What can you do when someone gives you data that isn't organized the way you need it to be?   That happens for a variety of reasons, but most often it's because the... Continue Reading
In its industry guidance to companies that manufacture drugs and biological products for people and animals, the Food and Drug Administration (FDA) recommends three stages for process validation: Process Design, Process Qualification, and Continued Process Verification. In this post, we we will focus on that third stage. Stage 3: Continued Process Verification Per the FDA guidelines, the goal of... Continue Reading

7 Deadly Statistical Sins Even the Experts Make

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People can make mistakes when they test a hypothesis with statistical analysis. Specifically, they can make either Type I or Type II errors. As you analyze your own data and test hypotheses, understanding the difference between Type I and Type II errors is extremely important, because there's a risk of making each type of error in every analysis, and the amount of risk is in your control.    So if... Continue Reading
A recent discussion on the Minitab Network on LinkedIn pertained to the I-MR chart. In the course of the conversation, a couple of people referred to it as "The Swiss Army Knife of control charts," and that's a pretty great description. You might be able to find more specific tools for specific applications, but in many cases, the I-MR chart gets the job done quite adequately. When you're... Continue Reading
Right now I’m enjoying my daily dose of morning joe. As the steam rises off the cup, the dark rich liquid triggers a powerful enzyme cascade that jump-starts my brain and central nervous system, delivering potent glints of perspicacity into the dark crevices of my still-dormant consciousness. Feels good, yeah! But is it good for me? Let’s see what the studies say… Drinking more than 4 cups of coffee... Continue Reading
Statistics can be challenging, especially if you're not analyzing data and interpreting the results every day. Statistical software makes things easier by handling the arduous mathematical work involved in statistics. But ultimately, we're responsible for correctly interpreting and communicating what the results of our analyses show. The p-value is probably the most frequently cited statistic. We... Continue Reading
As a person who loves baking (and eating) cakes, I find it bothersome to go through all the effort of baking a cake when the end result is too dry for my taste. For that reason, I decided to use a designed experiment in Minitab to help me reduce the moisture loss in baked chocolate cakes, and find the optimal settings of my input factors to produce a moist baked chocolate cake. I’ll share the... Continue Reading
Histograms are one of the most common graphs used to display numeric data. Anyone who takes a statistics course is likely to learn about the histogram, and for good reason: histograms are easy to understand and can instantly tell you a lot about your data. Here are three of the most important things you can learn by looking at a histogram.  Shape—Mirror, Mirror, On the Wall… If the left side of a... Continue Reading
by Matthew Barsalou, guest blogger.  The old saying “if it walks like a duck, quacks like a duck and looks like a duck, then it must be a duck” may be appropriate in bird watching; however, the same idea can’t be applied when observing a statistical distribution. The dedicated ornithologist is often armed with binoculars and a field guide to the local birds and this should be sufficient. A... Continue Reading
Genichi Taguchi is famous for his pioneering methods of robust quality engineering. One of the major contributions that he made to quality improvement methods is Taguchi designs. Designed experiments were first used by agronomists during the last century. This method seemed highly theoretical at first, and was initially restricted to agronomy. Taguchi made the designed experiment approach more... Continue Reading
Have you ever wished your control charts were better?  More effective and user-friendly?  Easier to understand and act on?  In this post, I'll share some simple ways to make SPC monitoring more effective in Minitab. Common Problems with SPC Control Charts I worked for several years in a large manufacturing plant in which control charts played a very important role. Virtually thousands of SPC... Continue Reading
Did you ever wonder why statistical analyses and concepts often have such weird, cryptic names? One conspiracy theory points to the workings of a secret committee called the ICSSNN. The International Committee for Sadistic Statistical Nomenclature and Numerophobia was formed solely to befuddle and subjugate the masses. Its mission: To select the most awkward, obscure, and confusing name possible... Continue Reading
Previously, I discussed how business problems arise when people have conflicting opinions about a subjective factor, such as whether something is the right color or not, or whether a job applicant is qualified for a position. The key to resolving such honest disagreements and handling future decisions more consistently is a statistical tool called attribute agreement analysis. In this post, we'll... Continue Reading
In my last post on DMAIC tools for the Define phase, we reviewed various graphs and stats typically used to define project goals and customer deliverables. Let’s now move along to the tools you can use in Minitab Statistical Software to conduct the Measure phase. Measure Phase Methodology The goal of this phase is to measure the process to determine its current performance and quantify the problem.... Continue Reading
People frequently have different opinions. Usually that's fine—if everybody thought the same way, life would be pretty boring—but many business decisions are based on opinion. And when different people in an organization reach different conclusions about the same business situation, problems follow.  Inconsistency and poor quality result when people being asked to make yes / no, pass / fail, and... Continue Reading
Process validation is vital to the success of companies that manufacture drugs and biological products for people and animals. According to the FDA guidelines published by the U.S. Department of Health and Human Services: “Process validation is defined as the collection and evaluation of data, from the process design state through commercial production, which establishes scientific evidence that a... Continue Reading
Ahoy, matey! Ye’ve come to the right place to learn about Value Stream Maps (VSM).  Just as a treasure map can lead a band o’ pirates to buried treasures, so too can the VSM lead a process improvement bilge rat to the loot buried deep inside a process! Minitab’s Quality Companion has an easy-to-use VSM tool to guide yer way. Use a value stream map to illustrate the flow of materials... Continue Reading
Did you ever get a pair of jeans or a shirt that you liked, but didn't quite fit you perfectly? That happened to me a few months ago. The jeans looked good, and they were very well made, but it took a while before I was comfortable wearing them. I much prefer it when I can get a pair with a perfect fit, that feel like I was born in them, with no period of "adjustment."  So which pair do you think I... Continue Reading
The line plot is an incredibly agile but frequently overlooked tool in the quest to better understand your processes. In any process, whether it's baking a cake or processing loan forms, many factors have the potential to affect the outcome. Changing the source of raw materials could affect the strength of plywood a factory produces. Similarly, one method of gluing this plywood might be better... Continue Reading
In Parts 1 and 2 of this blog series, I wrote about how statistical inference uses data from a sample of individuals to reach conclusions about the whole population. That’s a very powerful tool, but you must check your assumptions when you make statistical inferences. Violating any of these assumptions can result in false positives or false negatives, thus invalidating your results.  The common... Continue Reading