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Austin is the capital and heart of Texas. MSA training helps Austin students avoid making wrong decisions from bad data. | |||||||||||||||||||||||||
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Price | $349 / student | ||||||||||||||||||||||||
Duration | 1 day | ||||||||||||||||||||||||
Format | In-person, instructor-led slideshow with exercises and a quiz to test comprehension. | ||||||||||||||||||||||||
Materials | Each student will receive a 3-ring binder containing print-outs of the slideshow. Assuming satisfactory quiz results graded a few days after class, each student will receive a Certificate of Completion. | ||||||||||||||||||||||||
Class Size | 3 - 8 students | ||||||||||||||||||||||||
Start Day |
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City | Austin, TX |
The following dates are recommended because they have a low minimum student requirement,
or students are enrolled but not yet enough to hold a class. Please choose a date for your Measurement System Analysis (MSA) Training class. Dates are formatted as year-month-day:
If none of the recommended dates work with your schedule, that's ok. Please choose a date from the list below:
Who is Measurement System Analysis (MSA) Training for?
Quality, Manufacturing, and Product Engineering, plus Continuous Improvement teams are the most common users of measurement system analysis. Internal customer managers, Plant Managers, SQA, and Procurement teams should also understand MSA concepts. So, if your are in or want to be in one of those functions, then MSA training is for you. In our course, Austin students learn:
Measurement System Analysis (MSA) Training Course Overview:
MSA training describes Measurement System Analysis - quantifying measurement error help Austin students avoid making wrong decisions from bad data. Money is wasted when wrong decisions are made from bad data. In factories, good material is wrongfully scrapped. Defective parts are shipped. Wasting money this way extends beyond the factory to transactional processes as well. To avoid it, measurement systems must be analyzed and their error quantified. Our 1-day training equips your team to avoid this waste. It includes:
Accuracy vs. precision. | ||
Calibration and its limitations. | ||
Bias. | ||
Stability. | ||
Linearity. | ||
Repeatability and reproducibility. | ||
Destructive testing techniques. | ||
Analysis of variance. | ||
Graphical analysis. | ||
Test for comprehension. |