One of the principles for understanding data is that while some data contain signals, all data contain noise. Therefore, before you can detect the signals you’ll have to filter out the noise. This act of filtration is the essence of all data analysis techniques.

When administrative and managerial data are placed on an XmR chart, the first reaction will frequently be that the limits are far too wide: “We have to react before we get to that limit.”

Everybody wants to have good measurements. To this end, many recommend a regular schedule of recalibration. While this sounds reasonable, it can actually degrade the quality of the measurements.

C hallenges abound for today’s manufacturers. Labor shortages and rising labor costs require innovative solutions to maintain productivity with fewer staff. Inflation continues to exert pressure on raw material costs, squeezing margins.

The engineer came into the statistician’s office and asked, “How can I compare a couple of averages? I have 50 values from each machine and want to compare the machines.”

Many countries face the reality of demographic aging: Fertility is plummeting and people are living longer. This raises critical challenges for the labor market, healthcare, and long-term care markets, as well as retirement systems and financial planning.

In statistics class we learn that we can reduce the uncertainty in our estimates by using more and more data.

Modern fighter jets contain hundreds or even thousands of sensors. Some of those sensors collect data every second, others every nanosecond.

As companies and government agencies push forward with return-to-office (RTO) mandates, they risk exacerbating a workplace problem that many have failed to address adequately: gender discrimination.

Amid uncertainty in manufacturing, AI adoption, labor market fluctuations, and salary disparities across industries and geographic regions, quality professional compensation can be difficult to calculate.