Tips and Techniques for Statistics and Quality Improvement

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

Statistical inference uses data from a sample of individuals to reach conclusions about the whole population. It’s a very powerful tool. But as the saying goes, “With great power comes great responsibility!” When attempting to make inferences from sample data, you must check your assumptions. Violating any of these assumptions can result in false positives or false negatives, thus invalidating... Continue Reading
Every day, thousands of people withdraw extra cash for daily expenses. Each transaction may be small, but the total amount of cash dispersed over hundreds or thousands of daily transactions can be very high. But every bank branch has a fixed cash flow, which must be set without knowing what each customer will need on a given day. This creates a challenge for financial entities. Customers expect... Continue Reading

7 Deadly Statistical Sins Even the Experts Make

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Data mining can be helpful in the exploratory phase of an analysis. If you're in the early stages and you're just figuring out which predictors are potentially correlated with your response variable, data mining can help you identify candidates. However, there are problems associated with using data mining to select variables. In my previous post, we used data mining to settle on the following... Continue Reading
Since the release of Minitab Express in 2014, we’ve often received questions in technical support about the differences between Express and Minitab 17.  In this post, I’ll attempt to provide a comparison between these two Minitab products. What Is Minitab 17? Minitab 17 is an all-in-one graphical and statistical analysis package that includes basic analysis tools such as hypothesis testing,... Continue Reading
October 16–22 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. This event deserves your attention because the quality of healthcare affects every one of us, and so does the cost of... Continue Reading
A reader asked a great question in response to a post I wrote about Pareto charts. Our readers typically do ask great questions, but this one turned out to be more difficult to answer than it first seemed. My correspondent wrote:  My understanding is that when you have count data, a bar chart is the way to go. The gaps between the bars emphasize that the data are not measured on a continuous scale.... Continue Reading
On the Minitab Blog, we’ve often discussed getting data into Minitab from Excel. Here's a small sampling, in case you currently have data in Excel: Minitab and Excel: Making the (Data) Connection Linking Minitab to Excel to Get Answers Fast 3 Tips for Importing Excel Data into Minitab But if your data is not in Excel to begin with, taking it into Excel to prepare it for entry into Minitab isn’t... Continue Reading
The ultimate goal of most quality improvement projects is clear: reducing the number of defects, improving a response, or making a change that benefits your customers. We often want to jump right in and start gathering and analyzing data so we can solve the problems. Checking your measurement systems first, with methods like attribute agreement analysis or Gage R&R, may seem like a needless waste... Continue Reading
We hosted our first-ever Minitab Insights conference in September, and if you were among the attendees, you already know the caliber of the speakers and the value of the information they shared. Experts from a wide range of industries offered a lot of great lessons about how they use data analysis to improve business practices and solve a variety of problems. I blogged earlier about five key... Continue Reading
I watched an old motorcycle flick from the 1960s the other night, and I was struck by the bikers' slang. They had a language all their own. Just like statisticians, whose manner of speaking often confounds those who aren't hep to the lingo of data analysis. It got me thinking...what if there were an all-statistician biker gang? Call them the Nulls Angels. Imagine them in their colors, tearing... Continue Reading
If you were among the 300 people who attended the first-ever Minitab Insights conference in September, you already know how powerful it was. Attendees learned how practitioners from a wide range of industries use data analysis to address a variety of problems, find solutions, and improve business practices. In the coming weeks and months, we will share more of the great insights and guidance shared... Continue Reading
Face it, you love regression analysis as much as I do. Regression is one of the most satisfying analyses in Minitab: get some predictors that should have a relationship to a response, go through a model selection process, interpret fit statistics like adjusted R2 and predicted R2, and make predictions. Yes, regression really is quite wonderful. Except when it’s not. Dark, seedy corners of the data... Continue Reading
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
True or false: When comparing a parameter for two sets of measurements, you should always use a hypothesis test to determine whether the difference is statistically significant. The answer? (drumroll...) True! ...and False! To understand this paradoxical answer, you need to keep in mind the difference between samples, populations, and descriptive and inferential statistics.  Descriptive Statistics and... Continue Reading
Data mining uses algorithms to explore correlations in data sets. An automated procedure sorts through large numbers of variables and includes them in the model based on statistical significance alone. No thought is given to whether the variables and the signs and magnitudes of their coefficients make theoretical sense. We tend to think of data mining in the context of big data, with its huge... Continue Reading
Today, September 16, is World Ozone Day. You don't hear much about the ozone layer any more. In fact, if you’re under 30, you might think this is just another trivial, obscure observance, along the lines of International Dot Day (yesterday) or National Apple Dumpling Day (tomorrow). But there’s a good reason that, almost 30 years ago, the United Nations designated today to as a day to raise... Continue Reading
I confess: I'm not a natural-born decision-maker. Some people—my wife, for example—can assess even very complex situations, consider the options, and confidently choose a way forward. Me? I get anxious about deciding what to eat for lunch. So you can imagine what it used to be like when I needed to confront a really big decision or problem. My approach, to paraphrase the Byrds, was "Re:... Continue Reading
The other day I was talking with a friend about control charts, and I wanted to share an example one of my colleagues wrote on the Minitab Blog.  Looking back through the index for "control charts" reminded me just how much material we've published on this topic. Whether you're just getting started with control charts, or you're an old hand at statistical process control, you'll find some valuable... Continue Reading
To assess if a process is stable and in statistical control, you can use a control chart. It lets you answer the question "is the process that you see today going to be similar to the process that you see tomorrow?" To assess and quantify how well your process falls within specification limits, you can use capability analysis. Both of these tools are easy to use in Minitab, but you first need to... 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