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Project Tools

Blog posts and articles about tools and techniques that help keep Lean and Six Sigma projects on track.

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

7 Deadly Statistical Sins Even the Experts Make

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By Matthew Barsalou, guest blogger Teaching process performance and capability studies is easier when actual process data is available for the student or trainee to practice with. As I have previously discussed at the Minitab Blog, a catapult can be used to generate data for a capability study. My last blog on using a catapult for this purspose was several years ago, so I would like to revisit... Continue Reading
People who are ill frequently need medication. But if they miss a dose, or receive the wrong medication—or even get the wrong dose of the right medication—the results can be disastrous.  So medical professionals have a lot at stake in making sure patients get the right medicine, in the right amount, at the right time. But hospitals and other medical facilities are complex systems, and mistakes do... Continue Reading
This week is National Healthcare Quality Week, started by the National Association for Healthcare Quality to increase awareness of healthcare quality programs and to highlight the work of healthcare quality professionals and their influence on improved patient care outcomes. In honor of the celebration, I wanted to point you to a few case studies featuring Minitab customers in the healthcare field... Continue Reading
While most of us work in Minitab Statistical Software using our preferred language, some need to share Minitab project files or present the results in a different language. Others among us just want to play around with the languages because playing around with Minitab is fun! Thankfully, Minitab offers our statistical softwarein eight languages, including English, French, German, Japanese, Korean,... 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
How many samples do you need to be “95% confident that at least 95%—or even 99%—of your product is good? The answer depends on the type of response variable you are using, categorical or continuous. The type of response will dictate whether you 'll use: Attribute Sampling: Determine the sample size for a categorical response that classifies each unit as Good or Bad (or, perhaps, In-spec or... Continue Reading
You know what the big thing is in the data analysis world—"Big Data." Big, big, big, very big data. Massive data. ENORMOUS data. Data that is just brain-bendingly big. Data so big that we need globally interconnected supercomputers that haven't even been built yet just to contain one one-billionth of it. That's the kind of big data everybody's so excited about.  Whatever. There's no denying that... Continue Reading
I recently guest lectured for an applied regression analysis course at Penn State. Now, before you begin making certain assumptions—because as any statistician will tell you, assumptions are important in regression—you should know that I have no teaching experience whatsoever, and I’m not much older than the students I addressed. I’m just 5 years removed from my undergraduate days at Virginia Tech,... 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
by Colin Courchesne, guest blogger, representing his Governor's School research team.   High-level research opportunities for high school students are rare; however, that was just what the New Jersey Governor’s School of Engineering and Technology provided.  Bringing together the best and brightest rising seniors from across the state, the Governor’s School, or GSET for short, tasks teams of... Continue Reading
Statisticians say the darndest things. At least, that's how it can seem if you're not well-versed in statistics.  When I began studying statistics, I approached it as a language. I quickly noticed that compared to other disciplines, statistics has some unique problems with terminology, problems that don't affect most scientific and academic specialties.  For example, dairy science has a highly... Continue Reading
Just 100 years ago, very few statistical tools were available and the field was largely unknown. Since then, there has been an explosion of tools available, as well as ever-increasing awareness and use of statistics.   While most readers of the Minitab Blog are looking to pick up new tools or improve their use of commonly-applied ones, I thought it would be worth stepping back and talking about one... Continue Reading
If you've read the first two parts of this tale, you know it started when I published a post that involved transforming data for capability analysis. When an astute reader asked why Minitab didn't seem to transform the data outside of the capability analysis, it revealed an oversight that invalidated the original analysis.  I removed the errant post. But to my surprise, the reader who helped me... Continue Reading
Last time, I told you how I had double-checked the analysis in a post that involved running the Johnson transformation on a set of data before doing normal capability analysis on it. A reader asked why the transformation didn't work on the data when you applied it outside of the capability analysis.  I hadn't tried transforming the data that way, but if the transformation worked when performed as... Continue Reading
I don't like the taste of crow. That's a shame, because I'm about to eat a huge helping of it.  I'm going to tell you how I messed up an analysis. But in the process, I learned some new lessons and was reminded of some older ones I should remember to apply more carefully.  This Failure Starts in a Victory My mistake originated in the 2015 Triple Crown victory of American Pharoah. I'm no... Continue Reading