Statistics Article

Anthony Chirico’s picture

By: Anthony Chirico

Everybody wants to design and conduct a great experiment! To find enlightenment by the discovery of the big red X and perhaps a few smaller pink x’s along the way. Thoughtful selection of the best experiment factors, the right levels, the most efficient design, the best plan for randomization, and creative ways to quantify the response variable consume our thoughts and imagination. The list of considerations and trade-offs is quite impressive.

Scott A. Hindle’s picture

By: Scott A. Hindle

Walter Shewhart, father of statistical process control and creator of the control chart, put a premium on the time order sequence of data. Since many statistics and graphs are unaffected by this, you might wonder what the fuss is about. Read on to see why.

Minitab Inc.’s picture

By: Minitab Inc.

Machine learning as a tool in your analytical toolkit can help accelerate the discovery of insights in data that can create a more efficient manufacturing process and drive innovation.

Minitab Inc.’s picture

By: Minitab Inc.

Process validation is vital to the success of companies that manufacture pharmaceutical drugs, vaccines, test kits, and a variety of other biological products for people and animals. According to FDA guidelines, process validation is “the collection and evaluation of data, from the process design state through commercial production, which establishes scientific evidence that a process is capable of consistently delivering a quality product.”

David Currie’s picture

By: David Currie

This is the second article in a three-part series to help readers distinguish good metrics from bad. In part one we discussed good metrics. Here, we will look at a bad metric and consider how to change it into a useful, good metric. A bad metric is one that fails in one or more of the attributes of a good metric and is often not usable for the purpose it was intended.

Anthony Chirico’s picture

By: Anthony Chirico

Perhaps the reader recognizes d2 as slang for “designated driver,” but quality professionals will recognize it as a control chart constant used to estimate short-term variation of a process. The basic formula shown below is widely used in control charting for estimating the short-term variation using the average range of small samples. But what exactly is d2 and why should we care?

Minitab Inc.’s picture

By: Minitab Inc.

Choosing the correct linear regression model can be difficult. Trying to model it with only a sample doesn’t make it any easier. Let’s review some common statistical methods for selecting models, complications you may face, and look at some practical advice for choosing the best regression model.

Dirk Dusharme @ Quality Digest’s picture

By: Dirk Dusharme @ Quality Digest

In this episode we look at a history of quality, how you serve your customer in the housing industry, and what makes a good review.

“Young couples ‘trapped in car dependency’”

Building entry-level housing along highways may give couples the chance to buy a home, but at what cost to them and the environment?

David Currie’s picture

By: David Currie

Metrics are an important part of an effective quality management system (QMS). They are necessary to understand, validate, and course-correct the QMS. They should be used to verify that it is achieving the goals and objectives defined by management. In an ISO 9001 system, metrics must be available to assess risk, and validate changes made to the QMS and individual processes. Metrics are also used to validate improvement and verification of corrective action implementation during the management review.

Dirk Dusharme @ Quality Digest’s picture

By: Dirk Dusharme @ Quality Digest

In this episode we look at data, data, more data, and then... engineering the perfect human?

“Your Data Are Your Most Valuable Assets”

Just what the heck is Quality 4.0? Remember this acronym: CIA. No, not that CIA. Nicole Radziwill explains.

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