# Tips and Techniques for Statistics and Quality Improvement

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

"Gimme a latte grande with a double shot of statistical analysis. Hold the sugar."

I was reading an article recently about Jackson Pollock, an abstract artist famous for his “drip paintings,” and I became intrigued when I found out his art falls under the category of “process art.” As I learned more about Pollock and his “process,” I started to think about what he could teach us about process improvement.

Because I’m constantly surrounded by Lean Six Sigma methodology, value stream mapping, and endless bounds of quality tools, I can’t help but bring these ways of thinking into my everyday life.

My name is Andy Cheshire, and I currently work in Minitab's Technical Support department. Every day, I assist professionals who have questions about how to use Minitab to accomplish their analyses. Sometimes the call can be as simple as how to find a specific menu in Minitab. Other times, it can be more statistical in nature, as I try to explain how one Gage tool differs from another. The range of topics discussed...

Value stream mapping is a tremendously valuable tool for improving a process, but it requires patience and careful attention to details.  In an earlier post, I shared some value stream map guidelines to help ensure that the energy you invest pays off.  Here are some additional items to consider.

Value stream mapping is a cornerstone of the Lean process improvement methodology, and also is a recognized tool used in Six Sigma. A value stream map illustrates the flow of materials and information as a product or service moves through a process. Creating a “current state” value stream map can help you identify waste and also makes it easier to envision an improved state for process in the future.

Today we announced the winner of the Minitab Experiment Contest

Careful data analysis is always important, but sometimes we need to quickly get a sense of the relationship between variables or factors.  It’s also true that pictures speak louder than raw data – you may have analyzed every last scrap of your data and run every possible test to confirm your analysis, but an effective graph shows people what your data mean in much less time than a collection of numbers.

We use statistics because it's usually not practical to collect all of the data from an entire population. Instead, we sample the population, and then use statistics for that random sample to draw conclusions about the whole population.

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