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.
Research institutions and museums seek to provide the most accurate data possible to record the past and choose where to explore in the future. Modeling the accuracy of data is essential and nailing down geographical locations of specimen samples as exactly as possible is vital to do it effectively.
At its core, all Machine Learning algorithms follow a two-part process. First a sequence of increasingly complex functions is fit to part of the data (training data set). Then each model in the sequence is evaluated on how well it performs on the data that was held out (the holdout set).
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.
Rafael's previous employers used Minitab, giving him ample opportunity to figure out how to define experiments and variables to optimize detergent formulas most effectively. But Rafael's new employers didn't use Minitab. His boss challenged him to prove results before considering the investment.
In our continuing effort to use experimental design to understand how to drive the golf ball the farthest off the tee, we have decided each golfer will perform half the possible combinations of high and low settings for each factor. But how many times should each golfer replicate their runs to produce a complete data set?