Faculty: Elaine Eisenbeisz ‎ ‎ ‎‎ ‎ ‎ |‎ ‎ ‎ ‎ ‎ ‎ ‎ ‎ ‎ Code: FDB3132


  • Date: 5/18/2023 11:00 AM - 5/18/2023 12:30 PM
  • Location: Online Event

Tickets


Ticket Type
Qty
Live- Single
$250.00
0
For ONE Participant – Live session only
Live Corporate
$700.00
0
For Maximum of 10 participants – Live session only
Recording - Single
$390.00
0
Recording access for ONE participant (viewer) – Unlimited viewing access for 6 months
Recording - Corporate
$1400.00
0
Recording access (Multiple licenses) for up to 10 participants – Unlimited viewing access for 6 months

Total

$0.00

Description

Attendees of this webinar will learn specific concepts and formulas commonly used to measure the ability of a process to produce output within customers’ specification limits. The focus of this webinar is on providing the information needed for attendees to know the appropriate measures and formulas to use for the various types of process data (attribute or variable).

Why You Should Attend:

Companies involved on manufacturing and development perform inspection and testing for acceptance as relates to design, quality, or consumer criteria. These tests and inspections are performed on samples which may be collected at various times in the manufacturing process, including design verification, process validation, or quality control of incoming or outgoing product.

Learn the theory, terminology, regulatory requirements, best practices, and of course, the formulas for calculating process capability indices.

Learning Objectives:

  • Understand the calculations used for different types of data collected in the process
  • Perform calculations with the formulas presented.
  • Terminology used in process capability and testing.
  • Regulatory requirements of processes used in capability/reliability testing.
  • Learn when a transformation of data is needed, and how to transform it.

Areas Covered in the Session :

  • Regulatory Requirements
  • Theory and Terminology
  • Process Capability Indices
  • Data Types (Attribute vs. Variable)
  • Evaluate Data for Normality
  • Transforming Data

Who Should Attend:

  • QA/QC Supervisors
  • Process Engineers
  • Manufacturing Engineers
  • QA/QC Technicians
  • Manufacturing Technicians
  • R&D Engineers

Course Director: Elaine Eisenbeisz

 

Elaine Eisenbeisz is a private practice statistician and owner of Omega Statistics, a statistical consulting firm based in Southern California. Elaine has over 30 years of experience in creating data and information solutions for industries ranging from governmental agencies and corporations, to start-up companies and individual researchers.

Elaine’s love of numbers began in elementary school where she placed in regional and statewide mathematics competitions. She attended University of California, Riverside, as a National Science Foundation scholar, where she earned a B.S. in Statistics with a minor in Quantitative Management, Accounting. Elaine received her Master’s Certification in Applied Statistcs from Texas A&M, and is currently finishing her graduate studies at Rochester Institute of Technology. Elaine is a member in good standing with the American Statistical Association as well as many other professional organizations. She is also a member of the Mensa High IQ Society. Omega Statistics holds an A+ rating with the Better Business Bureau.

Elaine has designed the methodology for numerous studies in the clinical, biotech, and health care fields. She currently is an investigator on approximately 10 proton therapy clinical trials for Proton Collaborative Group, based in Illinois. She also designs and analyzes studies as a contract statistician for nutriceutical and fitness studies with QPS, a CRO based in Delaware. Elaine has also worked as a contract statistician with numerous private researchers and biotech start-ups as well as with larger companies such as Allergan and Rio Tinto Minerals. Not only is Elaine well versed in statistical methodology and analysis, she works well with project teams. Throughout her tenure as a private practice statistician, she has published work with researchers and colleagues in peer-reviewed journals. Please visit the Omega Statistics website at www.OmegaStatistics.com to learn more about Elaine and Omega Statistics.