Statistics Article

Scott A. Hindle’s picture

By: Scott A. Hindle

I recently got hold of the set of data shown in figure 1. What can be done to analyze and make sense of these 65 data values is the theme of this article. Read on to see what is exceptional about these data, not only statistically speaking.


Figure 1: Example data set.

Scott Weaver’s picture

By: Scott Weaver

The Atlantic hurricane season is now upon us, and the National Oceanic and Atmospheric Administration (NOAA) has just released its 2018 seasonal hurricane outlook which calls for a slightly above average season. The potential range of activity indicates that we could expect 10 to 16 named storms, with five to nine becoming hurricanes and one to four becoming major hurricanes.

But remember, it only takes one bad storm to wreak havoc, or in the case of the 2017 hurricane season, three.

Anthony D. Burns’s picture

By: Anthony D. Burns

Quality is related to processes. A process is “a series of actions or steps taken in order to achieve a particular end.” It doesn’t matter whether the process is the handling of invoices, customers in a bank, the manufacture or assembly of parts, insurance claims, the sick passing through a hospital, or any one of thousands of other examples. A process involves movement and action in a sequential fashion.

Cheryl Pammer’s picture

By: Cheryl Pammer

Confidence intervals show the range of values we can be fairly, well, confident, that our true value lies in, and they are very important to any quality practitioner. I could be 95-percent confident the volume of a can of soup will be 390–410 ml. I could be 99-percent confident that less than 2 percent of the products in my batch are defective.

Demonstrating an improvement to the process often involves proving a significant improvement in the mean, so that’s what we tend to focus on—the center of a distribution.

Rip Stauffer’s picture

By: Rip Stauffer

A lot of people in my classes struggle with conditional probability. Don’t feel alone, though. A lot of people get this (and simple probability, for that matter) wrong. If you read Innumeracy by John Allen Paulos (Hill and Wang, 1989), or The Power of Logical Thinking by Marilyn vos Savant (St.

Mike Richman’s picture

By: Mike Richman

There are many subjects that we cover regularly here at Quality Digest. Chief among these are standards (ISO 9001 or IATF 16949, for example) methodologies (such as lean, Baldrige, or Six Sigma), and test and measurement systems (like laser trackers or micrometers). One topic, however, is consistently at the very top of the list when it comes to audience popularity—industrial statistics, including statistical process control (SPC).

Evan McLaughlin’s picture

By: Evan McLaughlin

As the vice president of quality for a $1.5 billion-dollar industrial corporation, Hermann Miskelly is responsible for leading its continuous improvement effort. Now in his 10th year of a lean Six Sigma deployment, he has overseen the execution of more than 4,000 major improvement projects and another 6,000 small improvement projects. Here are three key insights he shared about managing continuous improvement projects, with the help of Companion by Minitab®.

Minitab Inc.’s picture

By: Minitab Inc.

Anticipating challenges is always a daunting task for continuous improvement professionals. Unforeseen inefficiencies in process or defects in product development can throw timelines and associated costs into disarray. How to commit to realistic forecasts and timelines when resources are limited, or gathering real data is too expensive or impractical? Can simulated data be trusted for accurate predictions? That’s when Monte Carlo simulation comes in.

Larry Silverberg’s picture

By: Larry Silverberg

Some 20 years ago, my colleague Chau Tran and I developed a way to simulate the trajectories of millions of basketballs on the computer.

We went to the coaches and assistant coaches at North Carolina State University, where we are based, and told them we had this uncommon ability to study basketball shots very carefully.

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NIST’s picture

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