Many practitioners have been taught to describe a process using sigma levels. Yet these levels are commonly misinterpreted. This article will help you to understand the problem and learn more appropriate ways of describing your process.

Most data in business and industry belong to the category known as observational data. These data are the voice of your processes because they are the result of ordinary operations rather than an experiment.

Performance indexes use the global standard deviation statistic to describe the past. Capability indexes use a within-subgroup measure of dispersion to characterize the process potential. However, some within-subgroup measures are better than others.

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.