Tips and Techniques for Statistics and Quality Improvement

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

Editor’s note: As we prepare for the inaugural Minitab Insights golf tournament in Scottsdale, Arizona on September 12, we are taking a look back at The Minitab Blog archives with some posts on using Minitab to understand how to improve our game. In this first installment, we examine how solving an age-old problem in golf is much like process engineering problems. Stay tuned each week as we cover the next step in our...

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The role of quality professionals is more critical today than it ever has been. Quality is a top driver at any company, regardless of size or industry. It’s a major factor when it comes to production numbers, profits and more. However, maintaining and improving quality is no easy task.

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If you collect and analyze real data for a living, the idea of using simulated data for a Monte Carlo simulation sounds a bit odd. How can you improve a real product with simulated data? 

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As we collect more and more observational data from our processes, we may need new tools to provide meaningful insights into this information. You can add modern-day machine learning techniques alongside traditional statistical tools to analyze, improve and control your processes.

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Dramatic cost savings. Lead time and inventory reductions. Improved transactional processes.

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Editor's note: If you would like to see these tools presented in a webinar, visit our On Demand Webinars and look for "5 Critical Tools for Your Lean Deployment."

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Can you trust your data? 

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The two previous posts in this series focused on manipulating data using Minitab’s calculator and the Data menu.

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My previous post focused on manipulating text data using Minitab’s calculator.

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It’s usually not a good idea to rely solely on a single statistic to draw conclusions about your process. Do that, and you could fall into the clutches of the “duck-rabbit” illusion shown here:

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