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Tips and Techniques for Statistics and Quality Improvement

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

What does the eyesight of a homeless person have in common with complications from dental anesthesia?  Or with reducing side-effects from cancer? Or monitoring artificial hip implants? These are all subjects of recently published studies that use statistical analyses in Minitab to improve healthcare outcomes. And they're a good reminder  that when we improve the quality of healthcare for others, we... Continue Reading
Suppose you’ve collected data on cycle time, revenue, the dimension of a manufactured part, or some other metric that’s important to you, and you want to see what other variables may be related to it. Now what? When I graduated from college with my first statistics degree, my diploma was bona fide proof that I'd endured hours and hours of classroom lectures on various statistical topics, including l... Continue Reading

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My previous post covered the initial phases of a project to attract and retain more patients in a cardiac rehabilitation program, as described in a 2011 Quality Engineering article. A Pareto chart of the reasons enrolled patients left the program indicated that the hospital could do little to encourage participants to attend a greater number of sessions, so the team focused on increasing initial... Continue Reading
Over the past year I've been able to work with and learn from practitioners and experts who are using data analysis and Six Sigma to improve the quality of healthcare, both in terms of operational efficiency and better patient outcomes. I've been struck by how frequently a very basic analysis can lead to remarkable improvements, but some insights cannot be attained without conducting more... Continue Reading
This is an era of massive data. A huge amount of data is being generated from the web and from customer relations records, not to mention also from sensors used in the manufacturing industry (semiconductor, pharmaceutical, petrochemical companies and many other industries). Univariate Control Charts In the manufacturing industry, critical product characteristics get routinely collected to ensure... Continue Reading
Do you recall my “putting the cart before the horse” analogy in part 1 of this blog series? The comparison is simple. We all, at times, put the cart before the horse in relatively innocuous ways, such as eating your dessert before you’ve eaten your dinner, or deciding what to wear before you’ve been invited to the party. But performing some tasks in the wrong order, such as running a statistical... Continue Reading
For one reason or another, the response variable in a regression analysis might not satisfy one or more of the assumptions of ordinary least squares regression. The residuals might follow a skewed distribution or the residuals might curve as the predictions increase. A common solution when problems arise with the assumptions of ordinary least squares regression is to transform the response... Continue Reading
While many Six Sigma practitioners and other quality improvement professionals like to use the Fishbone diagram in Quality Companion for brainstorming because of its ease of use and integration with other Quality Companion tools, some Minitab users find an infrequent need for a Fishbone diagram. For the more casual user of the Fishbone diagram, Minitab has the right tool to get the job done. Minitab... Continue Reading
For hundreds of years, people having been improving their situation by pulling themselves up by their bootstraps. Well, now you can improve your statistical knowledge by pulling yourself up by your bootstraps. Minitab Express has 7 different bootstrapping analyses that can help you better understand the sampling distribution of your data.  A sampling distribution describes the likelihood of... Continue Reading
Analysis of variance (ANOVA) can determine whether the means of three or more groups are different. ANOVA uses F-tests to statistically test the equality of means. In this post, I’ll show you how ANOVA and F-tests work using a one-way ANOVA example. But wait a minute...have you ever stopped to wonder why you’d use an analysis of variance to determine whether means are different? I'll also show how... Continue Reading
Among the most underutilized statistical tools in Minitab, and I think in general, are multivariate tools. Minitab offers a number of different multivariate tools, including principal component analysis, factor analysis, clustering, and more. In this post, my goal is to give you a better understanding of the multivariate tool called discriminant analysis, and how it can be used. Discriminant... 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
Once upon a time, when people wanted to compare the standard deviations of two samples, they had two handy tests available, the F-test and Levene's test. Statistical lore has it that the F-test is so named because it so frequently fails you.1 Although the F-test is suitable for data that are normally distributed, its sensitivity to departures from normality limits when and where it can be used. Leve... Continue Reading
You can use contour plots, 3D scatterplots, and 3D surface plots in Minitab to view three variables in a single plot. These graphs are ideal if you want to see how temperature and humidity affect the drying time of paint, or how horsepower and tire pressure affect a vehicle's fuel efficiency, for example. Ultimately, these three graphs are good choices for helping you to visualize your data and exa... Continue Reading
The Pareto chart is a graphic representation of the 80/20 rule, also known as the Pareto principle. If you're a quality improvement specialist, you know that the chart is named after the early 20th century economist Vilfredo Pareto, who discovered that roughly 20% of the population in Italy owned about 80% of the property at that time. You probably also know that the Pareto principle was... Continue Reading
In statistics, t-tests are a type of hypothesis test that allows you to compare means. They are called t-tests because each t-test boils your sample data down to one number, the t-value. If you understand how t-tests calculate t-values, you’re well on your way to understanding how these tests work. In this series of posts, I'm focusing on concepts rather than equations to show how t-tests work.... Continue Reading
In the first part of this series, we looked at a case study where staff at a hospital used ATP swab tests to test 8 surfaces for bacteria in 10 different hospital rooms across 5 departments. ATP measurements below 400 units pass the swab test, while measurements greater than or equal to 400 units fail the swab test and require further investigation. I offered two tips on exploring and visualizing... Continue Reading
Working with healthcare-related data often feels different than working with manufacturing data. After all, the common thread among healthcare quality improvement professionals is the motivation to preserve and improve the lives of patients. Whether collecting data on the number of patient falls, patient length-of-stay, bed unavailability, wait times, hospital acquired-infections, or readmissions,... Continue Reading
We often receive questions about moving ranges because they're used in various tools in our statistical software, including control charts and capability analysis when data is not collected in subgroups. In this post, I'll explain what a moving range is, and how a moving range and average moving range are calculated. A moving range measures how variation changes over time when data are collected as... Continue Reading
Along with the explosion of interest in visualizing data over the past few years has been an excessive focus on how attractive the graph is at the expense of how useful it is. Don't get me wrong...I believe that a colorful, modern graph comes across better than a black-and-white, pixelated one. Unfortunately, however, all the talk seems to be about the attractiveness and not the value of the... Continue Reading