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    Valid Statistical Rationales for Sample Sizes

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    Website https://www.traininng.com/webinar/valid-statistical-rationales-for-sample-sizes-200422live?ourglocal | Want to Edit it Edit Freely

    Category Supervisor, Process Engineer, Manufacturing Engineer, Manufacturing Technician

    Deadline: December 03, 2018 | Date: December 05, 2018

    Venue/Country: Online, U.S.A

    Updated: 2018-11-13 17:50:42 (GMT+9)

    Call For Papers - CFP

    Overview

    This webinar explains the logic behind sample-size choice for several statistical methods that are commonly used in verification or validation efforts, and how to express a valid statistical justification for a chosen sample size.

    The statistical methods discussed during the webinar include the following:

    Confidence intervals

    Process Control Charts

    Process Capability Indices

    Confidence / Reliability Calculations

    MTBF Studies ("Mean Time Between Failures" of electronic equipment)

    QC Sampling Plans

    Why should you Attend

    Almost all manufacturing and development companies perform at least some verification testings or validation studies of design-outputs and/or manufacturing processes, but it is sometimes difficult to explain the rationale for the sample sizes used in such efforts. This webinar provides guidance on how to justify such sample sizes, and thereby indirectly provides guidance on how to choose sample sizes.

    Areas Covered in the Session

    Introduction

    Examples of regulatory requirements related to sample size rationale

    Sample versus Population

    Statistic versus Parameter

    Rationales for sample size choices when using

    Confidence Intervals

    Attribute data

    Variables data

    Statistical Process Control C harts (e.g., XbarR)

    Process Capability Indices (e.g., Cpk )

    Confidence/Reliability Calculation

    Attribute data

    Variables data (e.g., K-tables)

    Significance Tests ( using t-Tests as an example )

    When the "significance" is the desired outcome

    When "non-significance" is the desired outcome (i.e., "Power" analysis)

    AQL sampling plans

    Examples of statistically valid "Sample-Size Rationale" statements

    Who Will Benefit

    QA/QC Supervisor

    Process Engineer

    Manufacturing Engineer

    QA/QC Technician

    Manufacturing Technician

    R&D Engineer

    Speaker Profile

    John N. 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.


    Keywords: Accepted papers list. Acceptance Rate. EI Compendex. Engineering Index. ISTP index. ISI index. Impact Factor.
    Disclaimer: ourGlocal is an open academical resource system, which anyone can edit or update. Usually, journal information updated by us, journal managers or others. So the information is old or wrong now. Specially, impact factor is changing every year. Even it was correct when updated, it may have been changed now. So please go to Thomson Reuters to confirm latest value about Journal impact factor.