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)
How to Identify the Most Important Predictor Variables in Regression Models
How to Identify the Most Important Predictor Variables in Regression Models
DOE Center Points: What They Are & Why They're Useful
DOE Center Points: What They Are & Why They're Useful
Applying DOE for Great Grilling, part 2
Applying DOE for Great Grilling, part 2
A DOE in a Manufacturing Environment (Part 2)
A DOE in a Manufacturing Environment (Part 2)