All design and/or manufacturing companies perform design verification and/or process validation studies. A clear understanding of relevant statistical principles and statistical methods ensures that such studies are efficient and accurate. In addition, all validated processes must be monitored to ensure their continued suitability (per the FDA).
The statistical methods used for such activities are easily misused when their fundamental principles are not well understood. Mistakes in usage can lead to new products being launched that should have been kept in R&D; or, conversely, can lead to erroneously deciding to not launch a new product. And failure to monitor production processes accurately can lead to a slow decline in product quality.
This seminar provides a thorough, practical introduction to the relevant statistical methods and principles that will help ensure that outputs from R&D, Product Transfer, Manufacturing Engineering, and Production are consistently of high quality.
Areas Covered in the Session :
- FDA, ISO 9001/13485, and MDD requirements
- Statistically valid rationales for sample sizes
- The interpretation of statistical significance and statistical non-significance
- The impact of normality and non-normality
- Tests of Normality
- Transformations to Normality
- Concepts of “Confidence” and “Reliability” (a.k.a., %-in-specification)
- Concepts of “Quality” and “Variability” and “Process”
- Risk management
Who Should Attend :
- QA/QC Supervisors
- Process Engineers
- Manufacturing Engineers
- QC/QC Technicians
- Manufacturing Technicians
- R&D Engineers
DAY 1: 9 AM to 6 PM CT
STATISTICAL ANALYSIS OF DESIGN VERIFICATION DATA AND PROCESS VALIDATION RESULTS
– Regulatory requirements
– Basic vocabulary and concepts
– How to interpret Linear Regression Correlation coefficients
– How to calculate Confidence Intervals (for proportions & for measurements)
– How to perform and interpret simple t-Tests of Statistical Significance, including consideration of “p-values” and sample-size, and the concepts of “superiority” and “non-inferiority”.
– How to understand the output of an ANOVA calculation
– Calculation of confidence and reliability ( = % in-specification) for
– Attribute data
– MTTF & MTBF (Mean time to Failure, and Mean time between Failure)
– Normally-distributed variables data (including Tests of Normality)
– Non-normal data (including Transformations to Normality)
– Non-normal data that cannot be transformed to normality
DAY 2: 9 AM to 6 PM CT
STATISTICAL PROCESS CONTROL (SPC) AND PROCESS CAPABILITY INDICES
– Understanding Quality
– Process Variation
– Statistical Process Control
– Types of Control Charts and how to construct them: XbarR, XbarS, XmR, P, and U
– Control Limits: Calculation & Re-calculation
– Out of Control: How to Detect It, & What to Do if Detected?
– Sample Issues: Random, Sub-grouping, & Sample Size
– Capability Indices and how to calculation them
– Non-normal Data, and its impact on SPC
– How to Initiate & Implement a Successful SPC Program
John N. Zorich
His experience as an instructor in statistics includes having given 3-day workshop/seminars for the past several years at Ohlone College (San Jose CA), 1-day training workshops in SPC for Silicon Valley Polytechnic Institute (San Jose CA) for several years, several 3-day courses for ASQ Biomedical, numerous seminars at ASQ meetings and conferences, and half-day seminars for numerous private clients. He creates and sells formally-validated statistical application spreadsheets that have been purchased by more than 75 companies, world-wide.
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Homewood Suites by Hilton, Kingwood section
Houston, TX, USA | February 7 – 8, 2019