Beyond the Hype: Machine Learning for Manufacturing Performance
Machine learning utilizes analytical data to discover insights that can create a more efficient manufacturing process and solve problems in a matter of seconds. These tools can be used in countless constructive ways in the manufacturing industry.
Guest Post: Predictive Analytics Accelerates Problem Solving
Adam Russell, Global Operations Master Black Belt explains how Tate & Lyle deploys Continuous Improvement (CI) tools, including Minitab and Salford Predictive Modeler (SPM), to challenging engineering and manufacturing problems.
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.
Vive la France! World Cup Bar Chart and Pie Charts Show Where Reader Loyalty Lies by Country and Industry
The World Cup has finally concluded with France becoming champion for the second time. Previously we polled The Minitab Blog readers on who they thought would win. Now, it’s time to look at the results and see how many of our readers have the ability of Nostradamus (Did I mention he’s French?).
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.
You Use Minitab. Your New Job Doesn't (YET). What Do You Do?
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.
Mulligan? How Many Runs Do You Need to Produce a Complete Data Set?
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?
The Human Element Behind Machine Learning
Editor's note: Bill Kahn runs the statistical modeling group for consumer banking at Bank of America. His team uses a broad range of statistical and machin