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Design of Experiments

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

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

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by Laerte de Araujo Lima, guest blogger The NBA's 2015-16 season will be one for the history books. Not only was it the last season of Kobe Bryan, who scored 60 points in his final game, but the Golden State Warriors set a new wins record, beating the previous record set by 1995-96 Chicago Bulls. The Warriors seem likely to take this season's NBA title, in large part thanks to the performance of... Continue Reading
About a year ago, a reader asked if I could try to explain degrees of freedom in statistics. Since then,  I’ve been circling around that request very cautiously, like it’s some kind of wild beast that I’m not sure I can safely wrestle to the ground. Degrees of freedom aren’t easy to explain. They come up in many different contexts in statistics—some advanced and complicated. In mathematics, they're... Continue Reading
In my last post, I discussed how a DOE was chosen to optimize a chemical-mechanical polishing process in the microelectronics industry. This important process improved the plant's final manufacturing yields. We selected an experimental design that let us study the effects of six process parameters in 16 runs. Analyzing the Design Now we'll examine the analysis of the DOE results after the actual... Continue Reading
I used to work in the manufacturing industry. Some processes were so complex that even a very experienced and competent engineer would not necessarily know how to identify the best settings for the manufacturing equipment. You could make a guess using a general idea of what should be done regarding the optimal settings, but that was not sufficient. You need very precise indications of the correct... Continue Reading
P values have been around for nearly a century and they’ve been the subject of criticism since their origins. In recent years, the debate over P values has risen to a fever pitch. In particular, there are serious fears that P values are misused to such an extent that it has actually damaged science. In March 2016, spurred on by the growing concerns, the American Statistical Association (ASA) did... Continue Reading
Did you know that March is Women’s History Month? The celebration was started in the 1980s by the U.S. government to pay tribute to generations of influential women. To celebrate, here’s a roundup of just some of the most influential women in statistics: Florence Nightingale While Florence Nightingale is known as the founder of modern nursing, you might not know that she is also a... Continue Reading
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
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