Optimize Energy Consumption and Yield in Energy-Intensive Industries With Statistical Methods
See how statistical methods like DOE can help energy-intensive industries optimize energy use and yield while reducing costs and environmental impact.
Predictive Analytics: The Perfect Partner to Help Plan Your DOE
Read and learn about the benefits of Design of Experiments. See how Minitab can help in the initial planning phase and throughout the entire process.
Celebrate the Holidays: Using DOE to Bake a Better Cookie
Have you ever used Design of Experiments (DOE) to bake the best holiday cookies? Learn how you can with Minitab Statistical Software in this blog.
7 Top Talks from the Minitab Insights Conference
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.
Besides Traditional Designs, Definitive Screening Designs can help Process & Product Optimization
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.
Process Validation Tools For Clinical Approval: An Example For Passing the 3 FDA Stage Goals
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.
Guest Post: Location Matters – Data Mining Research to Enhance Accuracy, Benefit Future Studies
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.
Guest Post: It’s Tough to Make Predictions, Especially about the Future (even with Machine Learning)
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).
Concluding Our Golf DOE: Time to Quantify, Understand and Optimize
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.
ANCOVA and Blocking: 2 Vital Parts to DOE
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.