Tips and Techniques for Statistics and Quality Improvement

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

Minitab’s Python connectivity feature allows users to run open-source Python scripts from within the Minitab interface and displays the results in Minitab’s output pane. Connectivity with Python means that custom analysis or graphs that are not already available in Minitab’s menus are only a few clicks away. And best of all, you don’t need to be a Python programmer to set this up.

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If you’ve ever wished for an easy way to add a custom analysis to Minitab’s interface, read on! Minitab’s new Python integration feature allows an avenue for easily running custom Python code via a button in the Minitab interface. If you’re not a programmer, fear not! I’m no programmer myself, so if I can do this, you can too.

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As we collect more and more observational data from our processes, we might need new tools to provide meaningful insights. You can add modern-day machine learning techniques alongside traditional statistical tools to analyze, improve and control your processes. Let's take a look at an example that starts with binary logistic regression and ends with Classification and Regression Trees (CART®).

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Content is available and accessible everywhere these days! A study from Nielsen found that adult Americans spend over 11 hours a day reading, listening, watching and interacting with media, which may even be higher now with so many individuals stuck at home. With the influx of content available, it might make you wonder: Is there a quantitative way to take a closer look at the text available to us?

Text mining, also...

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Trying to understand customer or patient behavior can be challenging. Oftentimes, researchers’ use survey data and frequently analyze it using regression in Minitab Statistical Software. But when the outcome variable is categorical rather than quantitative, interpreting the results of a logistic regression is not very straightforward or intuitive.

Another option for analyzing survey data in Minitab? Tap into the...

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When the data science buzz began around a decade ago, creating a predictive model was complicated work that only skilled programmers could accomplish. Today, new tools and new techniques are shifting the focus from programming details to building models that are robust, scalable and focused on creating business value.

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Were you one of the 300+ lucky practitioners, data analysts, statisticians, improvement advisers, program managers, engineers, clinical directors, coordinators and quality professionals who attended the 2019 Minitab Insights Global Conference this past fall? 

Don’t worry if you were unable to join — with the help of our expert Jenn Atlas, we’ve rounded up the 5 Key Challenges and Learnings you need to know.

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We might not be able to observe the progressive loss of cognitive and intellectual abilities someone with dementia is experiencing from the outside, but healthcare clinicians can detect it when they observe their ability to bathe, groom or dress themselves deteriorate. Minitab consultant  and Insights 2019 speaker David Patrishkoff is researching how to help with the aid of Minitab software. 

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Great stories others want to hear. Common challenges we have all faced before (or might even be facing right now). Discovering new tools in Minitab software as shared by peers. Engaging walkthroughs of finding insights in your data, and recommendations on how to act on them. All packed into a few days of learning and fun.

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You now often see “Machine Learning” appearing along with “Big Data”, “AI” or “Internet of Things” in the discussion of the digital transformation of business.

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