Design of Experiments

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

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
You’ve performed multiple linear regression and have settled on a model which contains several predictor variables that are statistically significant. At this point, it’s common to ask, “Which variable is most important?” This question is more complicated than it first appears. For one thing, how you define “most important” often depends on your subject area and goals. For another, how you collect... Continue Reading

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The college football season is here, and this raises a very important question: Is Alabama going to be undefeated when they win the national championship, or will they lose a regular-season game along the way? Okay, so it's not a given that Alabama is going to win the championship this year, but when you've won 4 of the last 7 you're definitely the odds-on favorite. However, what if we wanted to take... Continue Reading
If you’re in the market for statistical software, there are many considerations and more than a few options for you to evaluate. Check out these seven questions to ask yourself before choosing statistical software—your answers should help guide you towards the best solution for your needs! 1. Who uses statistical software in your organization? Are they expert statisticians, novices, or a mix of both?... Continue Reading
Design of Experiments (DOE) is the perfect tool to efficiently determine if key inputs are related to key outputs. Behind the scenes, DOE is simply a regression analysis. What’s not simple, however, is all of the choices you have to make when planning your experiment. What X’s should you test? What ranges should you select for your X’s? How many replicates should you use? Do you need center... Continue Reading
Design of Experiments is an extremely powerful statistical method, and we added a DOE tool to the Assistant in Minitab 17  to make it more accessible to more people. Since it's summer grilling season, 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... 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
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
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