MiC Quality BI-MONTHLY UPDATE APRIL 2008 Click here for the Web version of the current issue
 MiC Quality

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Our Visits: Toronto and Cornell University

In this issue:

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:: Face to Face Visits: Toronto and Cornell

We have recently returned from our face to face visits where we meet some of our students and business customers. This helps us understand how we can improve our existing online courses as well as generate ideas for new ones.

Our trip started in Toronto where we visited a company that specializes in precision silicone rubber products. We saw how they were using Measurement Systems Analysis to carry out precise measurements of small flexible components.

Near Toronto we visited a Fortune 500 company that manufactures power plants, that gave us an insight into how Advanced Statistics can improve the reliability of heat exchanger tubes.

In Ithaca, NY we visited Cornell University where we were privileged to see the cutting edge technology being used in the NanoScale Science and Technology Facility. This helped us understand how our Design of Experiments course can help with the exacting business of producing features that are less than 10 nanometers in size.

The visits are invaluable in showing how we can improve our courses, but it is a great pleasure to actually meet some of you face to face.

:: Six Sigma Glossary: Cohen's Kappa
 Cohen's Kappa

Cohen's kappa is used to compare the degree of consensus between raters (inspectors) in, for example, Measurement Systems Analysis. It uses a contingency table approach.

Two raters inspect 150 parts independently and make the following determinations:

 Bret Reject Accept Total Reject 20 19 39 Alice Accept 1 110 111 Total 21 129 150

The expected values in each cell would be:

 Bret Reject Accept Total Reject 5.46 33.54 39 Alice Accept 15.54 95.46 111 Total 21 129 150

These are the values that would give the same totals if the determinations were made by pure chance and is calculated from:

(row total x column total)/overall total

The Kappa statistic is calculated from:

 Kappa = Actual - Expected Trials - Expected = 130 - 100.92 150 - 100.92 = 0.593

where:

 Actual the number of times the appraisers agreed (110 + 20 = 130) Expected the number of times they would have agreed by chance (5.46 + 95.46) Trials the number of trials

The value of Kappa will be between 0 and 1.

If the results were made by chance, neither rater showing judgment the value would be zero. If the raters were in perfect agreement, the number of agreements would equal the number of trials and Kappa would be 1.