\n

\n#### Why should you attend:

\n\n

\n< h4>Areas Covered in the Session:\n\n

\n#### Who will benefit:

\n\n

\n\nThe 2-day seminar explains how to apply stat istics to manage risks and verify/validate processes in R&\;D\, QA/QC\, and Manufacturing\, with examples derived mainly from the medical device design/manufacturing industry. The flow of topics over the 2 days is as fo llows:

\n\n- \n
- ISO standards and FDA/MDD regulations regarding t he use of statistics. \n
- Basic vocabulary and concepts\, including distributions such as binomial\, hypergeometric\, and Normal\, and transf ormations into Normality. \n
- Statistical Process Control \n
- Statistical metho ds for Product/Process Qualification \n
- Metrology: the statistical analysis of measurement uncertainty\, and how it is used to establish QC specifications \n
- How to craft "\;statistically valid conclusi on statements"\; (e.g.\, for reports) \n
- Summary recommendatio ns \n

\n

Almost all design and/or manufacturing companies evaluate product and processes eith er to manage risks\, to validate processes\, to establish product/process specifications\, to QC to such specifications\, and/or to monitor complian ce to such specifications.

\n\nThe various statistical methods used to support such activities can be intimidating. If used incorrectly or ina ppropriately\, statistical methods can result in new products being launch ed that should have been kept in R&\;D\; or\, conversely\, new products not being launched that\, if analyzed correctly\, would have met all requ irements. In QC\, mistakenly chosen sample sizes and inappropriate statist ical methods may result in purchased product being rejected that should ha ve passed\, and vice-versa.

\n\nThis seminar provides a practical ap proach to understanding how to interpret and use more than just a standard tool-box of statistical methods\; topics include: Confidence intervals\, t-tests\, Normal K-tables\, Normality tests\, Confidence/reliability calcu lations\, Reliability plotting (for extremely non-normal data)\, AQL sampl ing plans\, Metrology (i.e.\, statistical analysis of measurement uncertai nty )\, and Statistical Process Control. Without a clear understanding and correct implementation of such methods\, a company risks not only signifi cantly increasing its complaint rates\, scrap rates\, and time-to-market\, but also risks significantly reducing its product and service quality\, i ts customer satisfaction levels\, and its profit margins.

\n\n\n< h4>Areas Covered in the Session:\n\n

- \n
- FDA\, ISO 9001/13485\, and MDD requirements related to statistical methods \n
- How to app ly statistical methods to manage product-related risks to patient\, doctor \, and the designing/manufacturing company \n
- Design Control proce sses (verification\, validation\, risk management\, design input) \n < li>QA/QC processes (sampling plans\, monitoring of validated processes\, s etting of QC specifications\, evaluation of measurement equipment)\n
- Manufacturing processes (process validation\, equipment qualification) \n

\n

- \n
- QA/QC Sup ervisor \n
- Process Engineer \n
- Manufacturing Engineer \n
- QC/QC Technician \n
- Manufacturing Technician \n
- R&a mp\;D Engineer \n

\n

\n\
n\n#### Day 1 Schedule

\n\n

Lecture 1:\n

Lecture 2:\n

Lecture 3:\n

Lecture 4:\n< p>**Normality Tests and Normality Transformations**\n\n Lecture 5:\n

Lecture 6:\n

Lecture 7:\n

Lecture 8:\n

\nLecture 1:\n

**Regula
tory Requirements**

Lecture 2:\n

**Vocabulary
and Concepts**

Lecture 3:\n

**Confidence Int
ervals (attribute and variables data)**

Lecture 4:\n< p>

**Statistical Process Control (with focus on Xba
rR charts)**

Lecture 6:\n

**Confidence/Reliab
ility calculations for Proportions**

Lecture 7:\n

< strong>Confidence/Reliability calculations for Normally distributed data ( K-tables)

\n\nLecture 8:\n

**Process Capability
Indices calculations(Cp\, Cpk\, Pp\, Ppk)**

\n\n#### Day 2 Schedule

\n\n

Lecture 1:\n

Lectur e 2:\n

Lecture 3:\n

Lecture 4:\n

Lecture 5:\n

Lecture 6:\n

Lecture 7:\n

\n\n
\nLecture 1:\n

Lectur e 2:\n

**Confidence/Reliability calculations for MTTF and MTBF (th
is typically applies only to electronic equipment)**

Lecture 3:\n

**Statistical Significance: t-Tests and related "
\;power"\; estimations**

Lecture 4:\n

**M
etrology (Gage R&\;R\, Correlation\, Linearity\, Bias\, and Uncertainty
Budgets)**

Lecture 5:\n

**QC Sampling Plans
(C=0 and Z1.4 attribute AQL plans\, and alternatives to such plans)\, incl
uding OC curves\, AQL vs. LQL/LTPD\, AOQL\, and calculation of acceptance
rates.**

Lecture 6:\n

**Statistically valid s
tatements for use in reports**

Lecture 7:\n

**Summary and Implementation Recommendations**

\n \;\n

**John N. Zorich\, has spent 35 years in the medical device manufacturing industry\; the
first 20 years were as a "\;regular"\; employee in the areas of R&
amp\;D\, Manufacturing\, QA/QC\, and Regulatory\; the last 15 years were a
s consultant in the areas of QA/QC and Statistics. His consulting clients
in the area of statistics have included numerous start-ups as well as larg
e corporations such as Boston Scientific\, Novellus\, and Siemens Medical.
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 Insti
tute (San Jose CA) for several years\, several 3-day courses for ASQ Biome
dical\, numerous seminars at ASQ meetings and conferences\, and half-day s
eminars for numerous private clients. He creates and sells formally-valida
ted statistical application spreadsheets that have been purchased by more
than 75 companies\, world-wide**

\n

\n \; SUMMARY:Applied Statistics\, with Emphasis on Verification\, Validation\, S ample Size\, and Risk Management BEGIN:VALARM ACTION:DISPLAY TRIGGER:-PT1H SUMMARY:Applied Statistics\, with Emphasis on Verification\, Validation\, S ample Size\, and Risk Management END:VALARM END:VEVENT END:VCALENDAR