ANOVA for Tailgate Samples

Automotive inspection, TS 16949, IATF 16949

Q: I have a question that is related to comparison studies done on incoming inspections.

My organization has a process for which it receives a “tailgate” sample from a supplier and then compares that data with three samples of the next three shipments to “qualify” them. The reason behind this comparison is to determine if the production process of the vendor has changed significantly from the “tailgate” sample, or if they picked the best of the best for the “tailgate.”

It seems a student’s t-test for comparing two means might be a simple and quick evaluation, but I believe an ANOVA might in order for the various characteristics measured (there are multiple).

Can an expert provide some statistician advice to help me move forward in determining an effective solution?

A: Assuming the data is continuous,  ANOVA (or MANOVA for multiple responses) should be employed. Since the tailgate sample is a control, Dunnett’s multiple comparison test should be used if the p-value from ANOVA is less than 0.05.  If the data is discrete (pass/fail), then comparing the lots would require the use of a chi-square test.

Steven Walfish
Secretary, U.S. TAG to ISO/TC 69
Principal Statistician, BD

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Design of Experiments (DOE)

ISO 13485, medical devices, medical device manufacturing, design of experiments

Q: I am looking for research articles or review papers on Design of experiments (DOE) specially focused on Response surface methods, Split Plot designs, MANOVA, and Repeated measures designs and analysis.  Any help to locate these articles will be greatly appreciated.

A: Thank you for contacting ASQ. I received your request for information on the topic of Design of Experiments (DOE).  Design of Experiments is defined as “a method for carrying out carefully planned experiments on a process.  By using a prescribed plan for the set of experiments and analyzing the data according to certain procedures, a great deal of information can be obtained from a minimum number of experiments” (from The Quality Toolbox, 2nd Ed. by Nancy R. Tague, Quality Press, 2005).

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