The calculations used in many statistical tests and methods require that the inputted data be “normally distributed”. This webinar explains what it means to be “normally distributed”, how to assess normality, how to test for normality, and how to transform non-normal data into normal data, and how to justify the transformations to internal and external quality system auditors.
Being able to assess whether data is “normally distributed”, and to be able to "transform to normality" is critical to ensuring that a company's “valid statistical techniques” are “suitable for their intended use” (as required by the FDA). Therefore, it is critical to a company's success. Most users of statistics make the error of assuming normality, in order to simplify their statistical analyses. However, most data sets in industry are not normally distributed, and not noticing that oftentimes results in rejecting lots that should have passed, failing processes that actually met their validation criteria, or keeping products in R&D long after they should have been transferred to Manufacturing.
Such calculations include those for Student's t-Tests, ANOVA tables, F-tests, Normal Tolerance limits, and Process Capability Indices. Unless the raw data used in such calculations is “normally distributed”, the resulting conclusions may be incorrect.
Dimensional data (length, width, height) are typically normally distributed. But many other types of data sets are almost always non-normal, such as: tensile strength, burst pressure, and time or cycles to failure. Some non-normal data can be transformed into normality, in order to then allow statistical calculations to be valid when run on the transformed data.Areas Covered in the Webinar:
From Medical Device, Pharmaceutical, and any Industry that performs standard statistical analyses.
Statistical Consultant and Trainer, Statistical Consultant
John Zorich has spent almost 40 years in the medical device manufacturing industry; the first 20 years were as a "regular" employee in the areas of R&D, Manufacturing, QA/QC, and Regulatory; the next 15 years were as a consultant in the areas of QA/QC and Statistics. These last few years were as a trainer and consultant in the area of Applied Statistics only. His consulting clients in the area of statistics have included numerous start-ups as well as large corporations such as Boston Scientific, Novellus, and Siemens Medical. His experience as an instructor in applied statistics includes having given annual 3-day seminars for many years at Ohlone College (San Jose CA), and previously having given that same course for several years for Silicon Valley ASQ Biomedical. He's given numerous statistical seminars at ASQ meetings and conferences. And he creates and sells validated statistical software programs that have been purchased by more than 110 companies, world-wide.
Registrants may cancel up to two working days prior to the course start date and will receive a letter of credit to be used towards a future course up to one year from date of issuance. FDATrainingAlert would process/provide refund if the Live Webinar has been cancelled. The attendee could choose between the recorded version of the webinar or refund for any cancelled webinar. Refunds will not be given to participants who do not show up for the webinar. On-Demand Recordings can be requested in exchange.
Webinar may be cancelled due to lack of enrolment or unavoidable factors. Registrants will be notified 24hours in advance if a cancellation occurs. Substitutions can happen any time.
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