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.
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?
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.
Power and Sample Size – Your Insurance Policy for Statistical Analysis
When we do statistical analyses, like hypothesis testing and design of experiments, we are using a sample of data to answer questions about all of our data. The reliability of these answers is affected by the size of the sample we analyze. To minimize the risk of doing unreliable statistical analysis we can use the Power and Sample size before collecting any data to determine how much data is needed to have a good chance of finding that effect, if it exists. The minimum recommended value for this is 80%.
Learning Design of Experiments with Paper Helicopters and Minitab
The paper helicopter provides a way to quickly explain basic DOE concepts. It also offers an easy-to-do experiment you can analyze using Minitab.
Using Designed Experiments (DOE) to Minimize Moisture Loss
Using Designed Experiments (DOE) to Minimize Moisture Loss
How Taguchi Designs Differ from Factorial Designs
How Taguchi Designs Differ from Factorial Designs
8 Expert Tips for Excellent Designed Experiments (DOE)
8 Expert Tips for Excellent Designed Experiments (DOE)