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Archive for the ‘Statistics’ Category

Using Macros to automate Minitab to produce scorecards and metrics.

Icon Written by Rick Haynes on May 3, 2012 – 7:54 pm

I promised a few Master Black Belts to provide information of writing Macros in Minitab.  They have described a real labor intensive effort to produce scorecards using Excel macros.  I do understand the issues. You can do a lot with Minitab Macros.  First, there is a help function just for macro functions.  Now that Minitab [...]

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Using Captivate in the teaching of Minitab for lean six sigma as a self-paced training lesson

Icon Written by Rick Haynes on April 30, 2012 – 7:44 pm

In the development of our upcoming blended learning Black Belt course we found a great software for producing software demonstrations.  Across the e-learning business, it is the choice for nearly all participants. The software is called Captivate and it is an Adobe product.  Since it is an Adobe software, it runs a bit different from standard [...]

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When is it acceptable to treat attribute data as continuous data?

Icon Written by Rick Haynes on April 30, 2012 – 6:38 pm

During a Master Black Belt class we examined a data set that involved attendance at ASQ meetings.  We were treating this data as continuous data, which was challenged.  Since the data is a count of people, it is truly an attribute count metric.  It is probability following a Poisson distribution, but I am still going [...]

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Logistic Regression – Residual Analysis

Icon Written by Rick Haynes on April 12, 2012 – 11:50 am

I have been working with a client that is needing to model a process that generates attribute data ranging from 100% to 0%, which was not a problem but just part of their process.  Their process was targeted at 50% but different product lines were performing differently but they could not predict the output enough [...]

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RYG performance scorecards observation

Icon Written by Rick Haynes on March 4, 2012 – 7:19 pm

I visited a client last week to work with their black belt candidates.  It was a great visit because their students were doing very well and I found very few conceptual errors.  Most will be getting certification within 6 months of the training.  Quite good. But one student presented a copy of a daily production [...]

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How to find out if an extreme point is a special cause

Icon Written by Rick Haynes on February 25, 2012 – 9:38 am

Many students of Lean Six Sigma struggle with the task of determining if an extreme value is a random event or a special-cause event.  It may sound easy in class, but it is not a simple task. First of all, every data point has conditions that can be assigned as causes: A new operator was [...]

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An interesting article about data mining of web data

Icon Written by Rick Haynes on February 23, 2012 – 4:36 pm

A good friend of mine forwarded me a link to an article titled  “How Target Figured out my daughter was pregnant before her father did” This article derives from a NY Times article. In the article it talks about the daughter receiving a mailed advertisement flier for baby stuff from Target, when she was young and [...]

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A graphic to show how a probability plot works

Icon Written by Rick Haynes on February 17, 2012 – 10:42 am

I created this graphic for our upcoming e-learning class.  I thought I would share it.

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Electronic Media in Lean Six Sigma

Icon Written by Rick Haynes on February 5, 2012 – 3:40 pm

At Smarter Solutions we routinely discuss a move to electronic media for our Lean Six Sigma and consulting efforts.  We have converted one book to an electronic format, with good reviews, but wonder about the applicability of text books to electronic media. The top Lean Six Sigma programs still provide courses with Instructor Lead Classes. [...]

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When is there no need to baseline a process?

Icon Written by Rick Haynes on January 31, 2012 – 11:36 am

In a coaching session with a Green Belt (GB) student I was shown a case where there is no need to baseline the process.  Their process was experiencing 100% defectives for an entire year.  Now, it was not a manufacturing process so the interpretation of defective did not mean that the product was scrapped.  In [...]

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