One hundred years ago this month, Walter Shewhart wrote a memo that contained the first process behavior chart. In recognition of this centennial, this column reviews four different applications of the techniques that grew out of that memo.

In less than two months we will celebrate the 100th anniversary of the invention of the control chart, a tool most often associated with statistical process control (SPC). Considering SPC from our modern perspective made us ask, “Is SPC still relevant?”

When presented with a collection of data from operations or production, many will start their analysis by computing descriptive statistics and fitting a probability model to the data. But before you do this, there’s an easy test that you need to perform.

Over the past two months we’ve considered the properties of lognormal and gamma probability models. Both of these families contain the normal distribution as a limit.

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.