Clear thinking and simplicity of analysis require concise, clear, and correct notions about probability models and how to use them.

So far in this series our focus has remained on statistical process control (SPC) in manufacturing.

When do we need to fit a lognormal distribution to our skewed histograms? This article considers the basic properties of the lognormal family of distributions and reveals some interesting and time-saving characteristics that are useful when analyzing data.

You are assigned a new task to demonstrate that an existing process will have the capability to meet newer and tighter specifications.

Just a few decades ago, today’s personal technology was a science fiction pipe dream.

Parts 1, 2, and 3 of our series on statistical process control (SPC) have shown how data can be thoughtfully used to enable learning and improvement—and consequently, better product quality and lower production costs.

Fourteen years ago, I published “Do You Have Leptokurtophobia?” Based on the reaction to that column, the message was needed.

Data overload has become a common malady. Modern data collection technologies and low-cost database storage have motivated companies to collect data on almost everything. The result? Data overload.

We are one year away from the 100th anniversary of the creation of the control chart: Walter Shewhart created the control chart in 1924 as an aid to Western Electric’s manufacturing operations.

In last month’s column, we looked at how process-hyphen-control algorithms work with a process that is subject to occasional upsets.