Measurement System Assessment (MSA)
Nick, Burn, Course Author
Principal Statistician at Predictum Inc.
1/2 day
On-premises or virtual
Available on request
About This Course
Measurement system bias and variation is a contributing factor to overall process variation and capability. An understanding of this contribution helps drive efforts for continuous improvement to achieve better product and process results.
The assessment of the measurement systems that are used to inform technicians, engineers, scientists, and researchers of the state of equipment, product, and processes is crucial to ensure that there is confidence in the results and data that are being used to make data-based decisions.
Measurement System Assessment (MSA) provides participants with the experimental designs and analytical methods for assessing the performance capabilities of quantitative variables gauge measurement systems. A working knowledge of basic statistics and of JMP statistical software is assumed.
- MSA Concepts
1.1 Measurement System Terminology
1.2 Calibration
1.3 Statistical Propertires
1.4 Measurement System Properties - Measurement System Location Errors
2.1 Bias
2.2 Linearity
2.3 Stability - Measurement System Dispersion Errors
3.1 Terminology
3.2 Variables Gauge Repeatability and Reproducibility Studies
3.3 Graphical Analyhsis of GR&R Study Results
3.4 Interpretation of Analytical AIAG and EMP Study Results
3.5 Assessing Interactions
Optional Add-ons:
- Short Variables Gauge Studies
- Binary Attributes Gauge Studies
- Customization with client data
Instructors
Nick worked at Nortel (14 years) in various manufacturing operations of high-tech components, as an independent consultant (5 years) providing training to automotive and high-tech companies, and at Abbott Laboratories (18 years) as a quality engineer supporting manufacturing and as a principal statistician supporting clinical studies in R&D. Nick was presented with an Abbott Chairman’s Award in 2007 as a team member for the successful FDA submission of waived status for a flagship product.
Nick is a part-time professor in the Department of Chemical and Biological Engineering at the University of Ottawa where he teaches undergraduate and graduate courses in the design of experiments, statistical process control, and statistical analysis of clinical study data. Nick has been recognized with several commendation letters from the Dean of Engineering for the high-quality teaching he has provided to his students.
Nick holds a B.A.Sc. in Chemical Engineering from the University of Ottawa, an M.Eng. in Chemical Engineering from McMaster University, and an M.Sc. in Mathematics Specialization in Biostatistics from the University of Ottawa.
“I really liked how organized the course was. I learned a lot about DOE which is very important in the field I want to work in.” Student, uOttawa.
“Nick Burn’s expertise in statistics has been invaluable to our team at Redlen. His deep understanding of statistical concepts, coupled with years of practical application, has greatly benefited everyone involved.” S.R., Redlen Technologies.
Who Should Attend
Engineers, scientists and technicians responsible for product and process development
Duration
1 half-day session
Delivery Methods
On premises or virtual
Prerequisites
Familiarity with computers and spreadsheet software, such as Microsoft Excel.
A working knowledge of basic statistics and of JMP statistical software is assumed.
JMP is required if you want to actively participate in the course. Don’t have JMP? A free trial version is available for your work or personal computer.
Pricing
Individual and group pricing available on request
