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Data Analysis

Blog posts and articles with tips for analyzing data for quality improvement methodologies, including Six Sigma and Lean.

A member of Minitab's LinkedIn group asked how to create a chart to monitor change by month, specifically comparing last year's data to this year's data. My last post showed how to do this using an Individuals Chart of the differences between this year's and last year's data.  Here's another approach suggested by a participant in the group.  Applying Statistical Thinking An individuals chart of the... Continue Reading
In Parts 1 and 2 of this blog series, I wrote about how statistical inference uses data from a sample of individuals to reach conclusions about the whole population. That’s a very powerful tool, but you must check your assumptions when you make statistical inferences. Violating any of these assumptions can result in false positives or false negatives, thus invalidating your results.  The common... Continue Reading

7 Deadly Statistical Sins Even the Experts Make

Do you know how to avoid them?

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Dear Readers, As 2016 comes to a close, it’s time to reflect on the passage of time and changes. As I’m sure you’ve guessed, I love statistics and analyzing data! I also love talking and writing about it. In fact, I’ve been writing statistical blog posts for over five years, and it’s been an absolute blast. John Tukey, the renowned statistician, once said, “The best thing about being a statistician... Continue Reading
by Matt Barsalou, guest blogger I know that Thanksgiving is always on the last Thursday in November, but somehow I failed to notice it was fast approaching until the Monday before Thanksgiving. This led to frantically sending a last-minute invitation, and a hunt for a turkey. I live in Germany and this greatly complicated the matter. Not only is Thanksgiving not celebrated, but also actual turkeys... Continue Reading
Minitab's LinkedIn group is a good place to ask questions and get input from people with experience analyzing data and doing statistics in a wide array of professions. For example, one member asked this question: I am trying to create a chart that can monitor change by month. I have [last year's] data and want to compare it to [this year's] data...what chart should I use, and can I auto-update it?... Continue Reading
In this day and age, it’s not uncommon that data entry errors occur in data sets that are so large that looking for and correcting the errors by hand is impractical. Fortunately, Minitab includes tools that make it easy to get your data into shape, so that you can proceed to getting the answers you need. Let’s say, for example, that you were going to look at the Global Wood Density Database. It’s... Continue Reading
Pareto charts are a special type of bar chart you can use to prioritize almost anything. This makes them very useful in making sound decisions. For example, if you have several possible quality improvement projects, but not enough time or people to do them all now, you can use a Pareto chart to identify which projects have the most potential for making meaningful improvement. Pareto charts look... Continue Reading
At the inaugural Minitab Insights Conference in September, presenters Benjamin Turcan and Jennifer Berner discussed how to present data effectively. Among the considerations they discussed was choosing the right graph. Different graphs are good for different things. Of course, opinions about which graph is best can, and do, differ. Dotplot devotees might decide that they are demonstrably... Continue Reading
Once again, with the arrival of autumn, it's time for a flu shot. I get a flu shot every year even though I know they’re not perfect. I figure they’re a relatively easy and inexpensive way to reduce the chance of having a miserable week. I’ve heard on various news media that their effectiveness is about 60%. But what does 60% effectiveness mean, exactly? How much does this actually reduce the... Continue Reading
In Part 1 of this blog series, I wrote about how statistical inference uses data from a sample of individuals to reach conclusions about the whole population. That’s a very powerful tool, but you must check your assumptions when you make statistical inferences. Violating any of these assumptions can result in false positives or false negatives, thus invalidating your results.  The common data... Continue Reading
No matter how experienced you are at analyzing data, communicating about your results can be a tremendous challenge. So it's not surprising that "Effectively Reporting Your Data Analysis" was one of the best-attended sessions at the inaugural Minitab Insights Conference last month.  The presenters, Benjamin Turcan and Jennifer Berner of First Niagara Bank, have a great deal of experience improving... Continue Reading
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
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
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
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