Ever since 1935 people have been trying to fine-tune Walter Shewhart’s simple but sophisticated process behavior chart. One of these embellishments is the use of two-sigma “warning” limits.

As the foundations of modern science were being laid, the need for a model for the uncertainty in a measurement became apparent. Here we look at the development of the theory of measurement error and discover its consequences.

In memory of Al Phadt, Ph.D.

The shape parameters for a probability model are called skewness and kurtosis. While skewness at least sounds like something we might understand, kurtosis simply sounds like jargon.

The computation for skewness does not fully describe everything that happens as a distribution becomes more skewed. Here we shall use some examples to visualize just what skewness does—and does not—involve.

Does your use of probabilities confuse your audience? Sometimes even using numbers can be misleading.

There are four major questions in statistics. These can be listed under the headings of description, probability, inference, and homogeneity. An appreciation of the relationships between these four areas is essential for successful data analysis.

The cumulative sum (or Cusum) technique is occasionally offered as an alternative to process behavior charts, even though they have completely different objectives. Process behavior charts characterize whether a process has been operated predictably.

Last month we found that capability and performance indexes have no inherent preference for one probability model over another.