Your software routinely gives you four descriptive statistics for your data: the average, the standard deviation, the skewness, and the kurtosis. Of these only the average is easy to understand.

The first part of this series introduced measurement systems analysis for attribute data, or attribute agreement analysis.

Measurement error is ubiquitous. As a result, over the past 250 years, different areas of science and engineering have come up with many different ways to deal with the problem of measurement error. One approach to the problem of measurement error was developed durin

When we talk about measurement system analysis (MSA), people tend to focus on attribute agreement analysis because it is usually quicker and easier to do than a gauge repeatability and reproducibility (gauge R&R) study.

Measurement systems analysis (MSA) for attributes, or attribute agreement analysis, is a lot like eating broccoli or Brussels sprouts. We must often do things we don't like because they are necessary or good for us.

To date, this series focused on relatively simple data analyses, such as learning one summary statistic about our data at a time.

For more than 40 years it has been common to use the precision to tolerance ratio (P/T ratio) to compare the standard deviation of measurement error with the specified tolerance for a particular product.

In our last article, we discussed how to determine how many people drink pumpkin spice lattes in a given time period without learning t

In my article, “Tightened 100% Inspection” (Quality Digest, March 29, 2021), we found that the excess costs associated with tightened specification limits a

How many people drink pumpkin spice lattes in October, and how would you calculate this without learning specifically who is drinking them, and who is not?