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Design of Experiments

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

In the world of linear models, a hierarchical model contains all lower-order terms that comprise the higher-order terms that also appear in the model. For example, a model that includes the interaction term A*B*C is hierarchical if it includes these terms: A, B, C, A*B, A*C, and B*C. Fitting the correct regression model can be as much of an art as it is a science. Consequently, there's not always a... Continue Reading
How deeply has statistical content from Minitab blog posts (or other sources) seeped into your brain tissue? Rather than submit a biopsy specimen from your temporal lobe for analysis, take this short quiz to find out. Each question may have more than one correct answer. Good luck! Which of the following are famous figure skating pairs, and which are methods for testing whether your data follow a... Continue Reading

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By Matthew Barsalou, guest blogger A problem must be understood before it can be properly addressed. A thorough understanding of the problem is critical when performing a root cause analysis (RCA) and an RCA is necessary if an organization wants to implement corrective actions that truly address the root cause of the problem. An RCA may also be necessary for process improvement projects; it is... Continue Reading
Did you know that November is World Quality Month? The American Society for Quality is once again heading up this year’s festivities. Throughout the month of November, ASQ will be promoting the use of quality tools in businesses, communities, and institutions all over the world. You can check it out at http://asq.org/world-quality-month/. Here at Minitab, we’re also pretty excited about World... Continue Reading
In Part 5 of our series, we began the analysis of the experiment data by reviewing analysis of covariance and blocking variables, two key concepts in the design and interpretation of your results. The 250-yard marker at the Tussey Mountain Driving Range, one of the locations where we conducted our golf experiment. Some of the golfers drove their balls well beyond this 250-yard maker during a few of... Continue Reading
In Part 3 of our series, we decided to test our 4 experimental factors, Club Face Tilt, Ball Characteristics, Club Shaft Flexibility, and Tee Height in a full factorial design because of the many advantages of that data collection plan. In Part 4 we concluded that each golfer should replicate their half fraction of the full factorial 5 times in order to have a high enough power to detect... Continue Reading
Step 3 in our DOE problem solving methodology is to determine how many times to replicate the base experiment plan. The discussion in Part 3 ended with the conclusion that our 4 factors could best be studied using all 16 combinations of the high and low settings for each factor, a full factorial. Each golfer will perform half of the sixteen possible combinations and each golfer’s data could stand as... Continue Reading
I read trade publications that cover everything from banking to biotech, looking for interesting perspectives on data analysis and statistics, especially where it pertains to quality improvement. Recently I read a great blog post from Tony Taylor, an analytical chemist with a background in pharmaceuticals. In it, he discusses the implications of the FDA's updated guidance for industry analytical... Continue Reading
Step 2 in our DOE problem-solving methodology is to design the data collection plan you will use to study the factors in your experiment. Of course, you will have to incorporate blocking and covariates in your experiment design, as well as calculate the number of replications of run conditions needed in order to be confident in your results. We will address these topics in future posts, but for... Continue Reading
Step 1 in our DOE problem-solving methodology is to use process experts, literature, or past experiments to characterize the process and define the problem. Since I had little experience with golf myself, this was an important step for me. This is not an uncommon situation. Experiment designers often find themselves working on processes that they have little or no experience with. For example, a... Continue Reading
As we broke for lunch, two participants in the training class began to discuss, debate, and finally fight over a fundamental task in golf—how to drive the ball the farthest off the tee. Both were avid golfers and had spent a great deal of time and money on professional instruction and equipment, so the argument continued through the lunch hour, with neither arguer stopping to eat. Several other... Continue Reading
Repeated measures designs don’t fit our impression of a typical experiment in several key ways. When we think of an experiment, we often think of a design that has a clear distinction between the treatment and control groups. Each subject is in one, and only one, of these non-overlapping groups. Subjects who are in a treatment group are exposed to only one type of treatment. This is the... Continue Reading
When I started out on the blog, I spent some time showing some data sets that would be easy to illustrate statistical concepts. It’s easier to show someone how something works with something familiar than with something they’ve never thought about before. Need a quick illustration to share with someone about how to summarize a variable in Minitab? See if they have a magazine on their desk, and... Continue Reading
It sometimes may be prohibitively expensive or time-consuming to gather data for all runs for a designed experiment (DOE). For example, a 6 factor, 2-level factorial design can entail 64 experimental runs, which may be too high a number for your particular situation. We have seen how to handle these some of these situations in previous posts, such as  Design of Experiments: "Fractionating" and... Continue Reading
Ever use dental floss to cut soft cheese? Or Alka Seltzer to clean your toilet bowl? You can find a host of nonconventional uses for ordinary objects online. Some are more peculiar than others. Ever use ordinary linear regression to evaluate a response (outcome) variable of counts?  Technically, ordinary linear regression was designed to evaluate a a continuous response variable. A continuous... Continue Reading
When performing a design of experiments (DOE), some factor levels may be very difficult to change—for example, temperature changes for a furnace. Under these circumstances, completely randomizing the order in which tests are run becomes almost impossible.To minimize the number of factor level changes for a Hard-to-Change (HTC) factor, a split-plot design is required. Why Do We Want to Randomize a... Continue Reading
Design of Experiments is an extremely powerful statistical method, we added a DOE tool to the Assistant in Minitab 17  to make it more accessible to more people. Since it's summer here, I'm applying the Assistant's DOE tool to outdoor cooking. Earlier, I showed you how to set up a designed experiment that will let you optimize how you grill steaks.  If you're not already using it and you want to... Continue Reading
Design of Experiments (DOE) has a reputation for difficulty, and to an extent, this statistical method deserves that reputation. While it's easy to grasp the basic idea—acquire the maximum amount of information from the fewest number of experimental runs—practical application of this tool can quickly become very confusing.  Even if you're a long-time user of designed experiments, it's still easy to... Continue Reading
I've never understood the fascination with selfies. Maybe it's because I'm over 50. After surviving the slings and arrows of a half a century on Earth, the minute or two I spend in front of the bathroom mirror each morning is more than enough selfie time for me. Still, when I heard that Microsoft had an online app that estimates the age of any face on a photo, I was intrigued. How would the app... Continue Reading
In my previous post, I wrote about the hypothesis testing ban in the Journal of Basic and Applied Social Psychology. I showed how P values and confidence intervals provide important information that descriptive statistics alone don’t provide. In this post, I'll cover the editors’ concerns about hypothesis testing and how to avoid the problems they describe. The editors describe hypothesis testing... Continue Reading