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?
A (Golf) Course in Design of Experiments
As we prepare for the inaugural Minitab Insights golf tournament in Scottsdale, Arizona on September 12, we are taking a look back at this series on using Minitab to improve our game. In this first installment, we examine how solving an age-old problem in golf is much like process engineering.
How Can You Fix the Process and Improve Product Development with Simulated Data? See All the Scenarios with Monte Carlo
How do you commit to realistic forecasts and timelines when resources are limited or gathering real data is too expensive or impractical? Can simulated data be trusted for accurate predictions? That’s when Monte Carlo Simulation comes in. Check out this step-by-step guide.
Step 1: Pinpoint Waste. Step 2: Deal with It. 5 More Critical Lean Tools
Step 1: Pinpoint Waste. Step 2: Deal with It. 5 More Critical Lean Tools
Drive Efficiency in Your Process: 5 Critical Lean Tools
Drive Efficiency in Your Process: 5 Critical Lean Tools
Getting the Most Out of Your Text Data Part III
Getting the Most Out of Your Text Data Part III
Getting the Most Out of Your Text Data, Part 2
Getting the Most Out of Your Text Data, Part 2
Cp and Cpk: Two Process Perspectives, One Process Reality
Cp and Cpk: Two Process Perspectives, One Process Reality
Attribute Acceptance Sampling for an Acceptance Number of 0
Attribute Acceptance Sampling for an Acceptance Number of 0