Blog posts and articles about statistical principles in quality improvement methods like Lean and Six Sigma.

Figures lie, so they say, and liars figure. A recent post at Ben Orlin's always-amusing mathwithbaddrawings.com blog nicely encapsulates why so many people feel wary about anything related to statistics and data analysis. Do take a moment to check it out, it's a fast read. In all of the scenarios Orlin offers in his post, the statistical statements are completely accurate, but the person offering... Continue Reading
Often, when we start analyzing new data, one of the very first things we look at is whether certain pairs of variables are correlated. Correlation can tell if two variables have a linear relationship, and the strength of that relationship. This makes sense as a starting point, since we're usually looking for relationships and correlation is an easy way to get a quick handle on the data set we're... Continue Reading

7 Deadly Statistical Sins Even the Experts Make

Do you know how to avoid them?

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The Olympic games are about to begin in Rio de Janeiro. Over the next 16 days, more than 11,000 athletes from 206 countries will be competing in 306 different events. That's the most events ever in any Olympic games. It's almost twice as many events as there were 50 years ago, and exactly three times as many as there were 100 years ago. Since the number of Olympic events has changed over time,... Continue Reading
My recent beach vacation began with the kind of unfortunate incident that we all dread: killing a distant relative.   It was about 3 a.m. Me, my two sons, and our dog had been on the road since about 7 p.m. the previous day to get to our beach house on Plum Island, Massachusetts. Google maps said our exit was coming up and that we were only about 15 minutes away from our palace. Buoyed by that... Continue Reading
Have you ever accidentally done statistics? Not all of us can (or would want to) be “stat nerds,” but the word “statistics” shouldn’t be scary. In fact, we all analyze things that happen to us every day. Sometimes we don’t realize that we are compiling data and analyzing it, but that’s exactly what we are doing. Yes, there are advanced statistical concepts that can be difficult to understand—but... Continue Reading
Statistics is all about modelling. But that doesn’t mean strutting down the catwalk with a pouty expression.  It means we’re often looking for a mathematical form that best describes relationships between variables in a population, which we can then use to estimate or predict data values, based on known probability distributions. To aid in the search and selection of a “top model,” we often utilize... Continue Reading
While some posts in our Minitab blog focus on understanding t-tests and t-distributions this post will focus more simply on how to hand-calculate the t-value for a one-sample t-test (and how to replicate the p-value that Minitab gives us).  The formulas used in this post are available within Minitab Statistical Software by choosing the following menu path: Help > Methods and Formulas > Basic... Continue Reading
When I blogged about automation back in March, I made my husband out to be an automation guru. Well, he certainly is. But what you don’t know about my husband is that while he loves to automate everything in his life, sometimes he drops the ball. He’s human; even I have to cut him a break every now and then. On the other hand, instances of hypocrisy in his behavior tend to make for a good story.... Continue Reading
You need to consider many factors when you’re buying a used car. Once you narrow your choice down to a particular car model, you can get a wealth of information about individual cars on the market through the Internet. How do you navigate through it all to find the best deal?  By analyzing the data you have available.   Let's look at how this works using the Assistant in Minitab 17. With the... Continue Reading
Here is a scenario involving process capability that we’ve seen from time to time in Minitab's technical support department. I’m sharing the details in this post so that you’ll know where to look if you encounter a similar situation. You need to run a capability analysis. You generate the output using Minitab Statistical Software. When you look at the results, the Cpk is huge and the histogram in... Continue Reading
If you've used our software, you’re probably used to many of the things you can do in Minitab once you’ve fit a model. For example, after you fit a response to a given model for some predictors with Stat > DOE > Response Surface > Analyze Response Surface Design, you can do the following: Predict the mean value of the response variable for new combinations of settings of the predictors. Draw... 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
In the great 1971 movie Willy Wonka and the Chocolate Factory, the reclusive owner of the Wonka Chocolate Factory decides to place golden tickets in five of his famous chocolate bars, and allow the winners of each to visit his factory with a guest. Since restarting production after three years of silence, no one has come in or gone out of the factory. Needless to say, there is enormous interest in... Continue Reading
Last Tuesday Night, Major League Baseball announced the rosters for tomorrow's All-Star game in San Diego. Immediately, as I'm sure was anticipated, people began talking about who made it and who didn't. Who got left out, and who shouldn't have made it. As a fun little exercise, I decided to take a visual look at the all-star teams, to see what kind of players were selected. I looked at position... Continue Reading
When you perform a statistical analysis, you want to make sure you collect enough data that your results are reliable. But you also want to avoid wasting time and money collecting more data than you need. So it's important to find an appropriate middle ground when determining your sample size. Now, technically, the Major League Baseball regular season isn't a statistical analysis. But it does kind... Continue Reading
In my last post, we took the red pill and dove deep into the unarguably fascinating and uncompromisingly compelling world of the matrix plot. I've stuffed this post with information about a topic of marginal interest...the marginal plot. Margins are important. Back in my English composition days, I recall that margins were particularly prized for the inverse linear relationship they maintained with... 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
It's been called a "demographic watershed".  In the next 15 years alone, the worldwide population of individuals aged 65 and older is projected to increase more than 60%, from 617 million to about 1 billion.1 Increasingly, countries are asking themselves: How can we ensure a high quality of care for our growing aging population while keeping our healthcare costs under control? The answer? More... 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