Statistics Article

Donald J. Wheeler’s picture

By: Donald J. Wheeler

Good measurements are like apple pie and motherhood. Who could ever be against having good measurements? Since we all want good measurements, it sounds reasonable when people are told to check out the quality of their measurement system before putting their data on a process behavior chart. Fortunately, this is simply one more bit of advice that is completely unnecessary.

Catherine Beare’s picture

By: Catherine Beare

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Although efforts have been made to create policies that support a bias-free workplace, there is still a considerable way to go toward achieving the gender equality that organizations are striving for. Due in part to a lack of clear measurement and transparency, many companies and industries as a whole are still lagging behind in the effort to have women and men equally represented, valued, and rewarded in the workplace.

Stanford News Service’s picture

By: Stanford News Service

Most leadership advice is based on anecdotal observation and basic common sense. Stanford Graduate School of Business professor Kathryn Shaw tried a different tack: data-driven analysis.

Joby George’s picture

By: Joby George

Having difficulty managing quality and quality-related data? You’re not alone. Many manufacturers struggle with this these issues due to paper-based or other disparate systems being used to track, manage, and report on quality events. Walk about a production room floor, and there’s a good chance you’ll see a few three-ring binders or folders stuffed with handwritten, quality-related worksheets and forms.

Eston Martz’s picture

By: Eston Martz

If you were among the 300 people who attended the first-ever Minitab Insights conference last month, you already know how powerful it was. Attendees learned how practitioners from a wide range of industries use data analysis to address a variety of problems, find solutions, and improve business practices. For those who weren’t there, here are five helpful, challenging, and thought-provoking ideas and suggestions that we heard during the event.

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.

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