Tips and Techniques for Statistics and Quality Improvement

Blog posts and articles about using Minitab software in quality improvement projects, research, and more.

You've collected a bunch of data. It wasn't easy, but you did it. Yep, there it is, right there...just look at all those numbers, right there in neat columns and rows. Congratulations. I hate to ask...but what are you going to do with your data? If you're not sure precisely what to do with the data you've got, graphing it is a great way to get some valuable insight and direction. And a good graph to... 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
It was a wild weekend in the Big Ten. Four of the six conference games were decided by a touchdown or less, and all of those close games means we have plenty of 4th down decisions to analyze. If you're new to the Big Ten 4th Down Calculator, I've used Minitab Statistical Software to create a model to determine the correct 4th down decision. And for the rest of the college football season, I'll use... 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
I was recently asked a couple of questions about stability studies in Minitab. Question 1:  If you enter in a lower and upper spec in the Stability Study dialog window, why do I see only one confidence bound per fitted line on the resulting graph? Shouldn’t there be two? You use a stability study to analyze the stability of a product over time and to determine the product's shelf life. In order to... Continue Reading
An exciting new study sheds light on the relationship between P values and the replication of experimental results. This study highlights issues that I've emphasized repeatedly—it is crucial to interpret P values correctly, and significant results must be replicated to be trustworthy. The study also supports my disagreement with the decision by the Journal of Basic and Applied Social Psychology to b... Continue Reading
September 17 marked the release of new information from the American Community Survey (ACS) from the U.S. Census Bureau. Here’s a bar chart of what the press releases looked like for that day: Clearly there was a theme in play, one that was great news for major metropolitan areas. The Census Bureau even released a graph showing that the percentage of people within the 25 most populous metropolitan... 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 run a capability analysis and your Cpk is bad. Now what? First, let’s first start by defining what “bad” is. In simple terms, the smaller the Cpk, the more defects you have. So the larger your Cpk is, the better. Many practitioners use a Cpk of 1.33 as the gold standard, so we’ll treat that as the gold standard here, too. Suppose we collect some data and run a capability analysis using Minitab St... 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
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
This summer, I created a model to determine the correct 4th down decision. But whether it’s for business or some personal interest, creating a model is just the starting point. The real benefits come from applying your model. And for the Big Ten 4th down calculator, the time to apply the model is now! On Saturday night, Penn State and Rutgers officially kicked off conference play for the 2015 Big... 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
Whatever industry you're in, you're going to need to buy supplies. If you're a printer, you'll need to purchase inks, various types of printing equipment, and paper. If you're in manufacturing, you'll need to obtain parts that you don't make yourself.  But how do you know you're making the right choice when you have multiple suppliers vying to fulfill your orders?  How can you be sure you're... 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
Variance is a measure of how much the data are scattered about their mean. Usually we want to minimize it as much as possible. A manufacturer of screws wants to minimize the variation in the length of the screws. A restaurant owner doesn't want the taste of the same meal to vary from one day to the next. And they might not know it, but most football coaches choose a low variance strategy when they... Continue Reading