When regulators in pharmaceutical & healthcare ask you to validate analytical techniques, measurement systems ('Data Integrity') and process performance ('SPC'), Minitab recommends these critical checks.
Analyzing two year's of fitness tracker data on her exercise workouts - durations, types and calories burnt - Eugenie Chung, our Technical Specialist, used Minitab Statistical Software to answer: does adding resistance training improve her daily calories burnt compared to focusing on pure cardio?
Parts are to be manufactured to specifications. Unstable behaviours generate quality problems experienced by customers later on. This article touches on why and how to reduce instability with a structured quality and problem-solving approach such as DMAIC applied using the modern toolkit of Minitab.
See what goes into a great Minitab Insights Conference presentation. Great stories others want to hear. Discovering new tools in Minitab software. 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.
Definitive Screening Designs (DSDs) are a new class of designs of experiments (DoE) that have generated a lot of interest for process and product optimization. They are available in the latest version of Minitab.
The FDA recommends three stages for process validation. Let’s explore the stage goals and the types of activities and statistical techniques typically conducted within each. You can use Minitab Statistical Software to run any of the analyses here. If you don’t yet have Minitab, try it free for 30 days.
Last week on The Minitab Blog, we used Minitab Statistical Software to review analysis of covariance (ANCOVA) and blocking variables in our ongoing design of experiment (DOE) on how to get the longest drive in your golf game. Now let’s finish it. We’re ready to review and present our results.
Previously in our Minitab designed experiment on driving the golf ball as far as possible from the tee, we tested our four experimental factors and determined how many runs we needed to produce a complete data set. Now let’s analyze the data and interpret the covariates and blocking variables.