# Tips and Techniques for Statistics and Quality Improvement

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

At Minitab, we want our users to focus their time on drawing sensible conclusions from their data that they can use to resolve business problems or take advantage of opportunities. However, with more and more sources of data available, you often spend more time getting ready for analysis and less time interpreting it.

Here are four ideas that demonstrate how Minitab macros deliver “one-click analysis” for the...

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.

Do you have an insurance policy that will pay out if your car gets damaged? Do you pay the premium because you know your car will be damaged? No, you pay it so that if you do damage your car you will get a payment to cover the damage.

We had solar panels fitted on our property in 2011. Last year, we had a few problems with the equipment. It was shutting down at various times throughout the day, typically when it was very sunny, resulting in no electricity being generated.

In Part 1 of this blog series, I compared Six Sigma to a diamond because both are valuable, have many facets and have withstood the test of time. I also explained how the term “Six Sigma” can be used to summarize a variety of concepts, including philosophy, tools, methodology, or metrics. In this post, I’ll explain short/long-term variation and between/within-subgroup variation and how they help the Six Sigma...

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.

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?”