Whitepaper
Power of a Statistical Test
The power of a statistical test gives the likelihood of rejecting the null hypothesis when the null hypothesis is false. Just as the significance level (alpha) of a test gives the probability that the null hypothesis will be rejected when it is actually true, power quantifies the chance that the null hypothesis will be rejected when it is actually false.
Smita Skrivanek
How to Conduct an MSA When the Part is Destroyed
This article uses an example of a manufacturer of prosthetic devices to explore measurement system analysis when the part is destroyed during measurement.
The Use of Dummy Variables in Regression Analysis
An explanation of how to incorporate categorical variables in a regression analysis properly using bit-wise encoding, otherwise known as dummy variables.
Best Practices of Project Management and Tracking Tools
How to select the best project management tool for your team based on ease of configuration, visual management, integrations, and reporting capabilities.
MoreSteam
Simpson's Paradox (and How to Avoid Its Effects)
What is Simpson's Paradox? Follow an example of Simpson's Paradox and learn practical ways to avoid distorting causal relationships when analyzing a dataset.
Blended Learning Manifesto
The blended learning model is more efficient and effective than traditional instructor-led classroom training. Blended learning is the best approach for teaching Lean Six Sigma.
Bill Hathaway
Effectiveness of the Blended Learning Model
Self‐directed learning in combination with facilitated simulations and practice exercises provides an effective, scalable model for building capability.
Gauge R&R Acceptability
Why do we accept the measurement system while more than 50% of the measurements are out of control?
Charting Non-Normal Data
We should always first challenge the notion of homogeneity, especially if it's a service or transactional process, and ask whether there are sub‐populations that are responsible for the more extreme values.
Statistics and Simulations
With the rapid increase (and the concurrent decrease in cost) of computing power, the use of stochastic simulation has played an increasingly large role in statistics
