Statistics Article

Scott A. Hindle’s picture

By: Scott A. Hindle

In all walks of life, being wrong can come with a penalty. It’s also true that, if you’re lucky, you sometimes get away with it without anybody being the wiser. To understand what this means in relation to the capability indexes Cp and Cpk, read on.

Scott A. Hindle’s picture

By: Scott A. Hindle

Part two of this four-part series on process capability concluded with Alan just about to meet Sarah for a second time. He thought he was making good progress with his analysis of Product 874 data until he was asked to assess process capability, even though it can’t be assessed for an unstable process.

Scott A. Hindle’s picture

By: Scott A. Hindle

In part one of this four-part series, we considered the basics of process capability, as witnessed through the learning curve of Alan in his quest to determine the product characteristics of the powder, Product 874. We pick up with Alan here as he prepares for his second meeting with his colleague Sarah, to discuss his preliminary results.

Scott A. Hindle’s picture

By: Scott A. Hindle

In my August 2015 article, “Process Capability: How Many Data?” I discussed whether 30 data were the “right” number in an analysis of process capability. In this four-part series, the focus is on understanding what process capability is and the pitfalls associated with it, along with how it can help manufacturers develop process knowledge, reach better decisions, and take better actions.

Barbara A. Cleary’s picture

By: Barbara A. Cleary

Approaching the end of the school year means focusing on graduation rates, dropout rates, and other data suggesting trends for students. Opportunities for considering statistics abound, but one must examine the way that these statistics are actually used by asking the right questions about the data.

Ken Levine’s picture

By: Ken Levine

How do you determine the “worst case” scenario for a process? Is it by assuming the worst case for each process task or step? No. The reason is that the probability of every step having its worst case at the same time is practically zero. What we’re looking for is a value that will occur a very small percentage of the time, but still be a possibility.

Ken Voytek’s picture

By: Ken Voytek

In a recent post, I examined the differences in productivity across small and large manufacturing firms, and noted that there were differences across manufacturers in terms of size. But it’s also clear from the literature that productivity differs across companies even in the same industry.

Brooke Pierce’s picture

By: Brooke Pierce

The healthcare industry is in a state of constant change, and with change comes opportunity. With the passage of the Affordable Care Act (ACA) and the Medicare Access and CHIP Reauthorization Act (MACRA), healthcare providers are, or will be, paid differently for their services. No longer can they rely on the volume of services rendered to generate sustained income.

Multiple Authors
By: Donald S. Holmes, A. Erhan Mergen

Regression analysis is used in a variety of manufacturing applications. An example of such an application would be to learn the effect of process variables on output quality variables. This allows the process control people to monitor those key variables and keep the output variables at the desired level.

Fred Schenkelberg’s picture

By: Fred Schenkelberg

What if all failures occurred truly randomly? Well, for one thing the math would be easier.

The exponential distribution would be the only time to failure distribution—we wouldn’t need Weibull or other complex multi-parameter models. Knowing the failure rate for an hour would be all we would need to know, over any time frame.

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