Almost all design and/or manufacturing companies evaluate product and processes in order to either manage risks, validate processes, establish product/process specifications, QC to such specifications, and/or monitor compliance to such specifications. The various statistical methods used to support such activities can be intimidating. If used incorrectly or inappropriately, statistical methods can result in new products being launched that should have been kept in R&D; or, conversely, they can result in deciding a new product is not ready for launch, but whose product reliability or process capability is actually very good. In QC, mistakenly chosen sample sizes and inappropriate statistical methods may result in product being rejected that should have passed, and vice-versa.

This seminar provides a practical approach to understanding how to interpret and use a standard tool-box of statistical methods, including confidence intervals, t-tests, Normal K-tables, Normality tests, Confidence/Reliability calculations, Tolerance Limits, Reliability Plotting, AQL sampling plans, measurement equipment analysis (including Gage R&R), and Statistical Process Control (including Cpk/Ppk calculations). Without a clear understanding and correct implementation of such methods, a company risks not only significantly increasing its complaint rates, scrap rates, and time-to-market, but also risks significantly reducing its product and service quality, its customer satisfaction levels, and its profit margins.

Seminar Fee Includes:
AM-PM Tea/Coffee
Seminar Material
USB with seminar presentation
Hard copy of presentation
Attendance Certificate
$100 Gift Cert for next seminar

The seminar includes a strong focus on risk-management, regulatory compliance, and sample-size determination/justification.

At the start of the course, a large suite of statistical application spreadsheets is given to all students, in order to assist them in understanding and applying the course concepts and methods after returning home. Additionally, the instructor is available indefinitely by email, free of charge, to answer short questions related to the course topics, and/or to perform statistical analysis on data-sets that are provided to him in an Excel spreadsheet (in such cases, the data should be presented as a set of numbers and their QC/Design specifications, without any explanation regarding the identity of the company's product that generated them; therefore signing an NDA would not be necessary).

Learning Objectives:

  • How to apply "confidence" and "risk management" to virtually all statistical techniques
  • Know when to use "exact" methods rather than "approximation" methods
  • Know when to use commercial software (e.g., Minitab, StatGraphics) and when to use Excel
  • How to explain statistics to management
  • How to interpret regulatory requirements related to statistics
  • How to determine the smallest sample size needed to achieve a desired outcome.

Areas Covered :

  • Basic regulatory requirements related to statistics (for medical devices and pharmaceuticals)
  • Basic statistical concepts and vocabulary
  • Normality, Normality Tests, and Normality Transformations
  • Statistical Process Control
  • Process and Product Capability assessments including: Confidence/Reliability Calculations, Tolerance Limits, Cpk/Ppk, and Reliability Plotting
  • Statistical Significance tests (including testing for "Superiority", "Non-inferiority", and "TOST")
  • Statistical Power
  • Metrology (statistical analysis of measurement uncertainty, including Gage R&R and "guard-banding")
  • QC Sampling Plans (AQL vs. LQL vs AOQL vs. other alternatives)
  • Statistical justification for Process Validation sample sizes and the use of only 3 Lots
  • Examples of "statistically valid rationales" regarding sample sizes.

Who will Benefit:

  • R&D Manager
  • QA/QC Manager
  • Manufacturing Manager
  • R&D Engineer
  • Manufacturing Engineer
  • Process Engineer
  • Validation Engineer
  • QC/QC Technician
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Day 01(8:30 AM - 6:00 PM)
  • 08.30 AM - 08.59 AM – Registration, Meet & Greet
  • 9:00 AM -10:30 AM
    • Introduction.
    • Regulatory Requirements
      • 21CFR211
      • ISO 13485
      • IVDD
      • 21CFR820
    • Basic Statistical Concepts
      • Sample vs. Population
      • Statistic vs. Parameter
      • Law of Large Numbers
      • Binomial Distribution
      • Hypergeometric Distribution
      • Normal vs. Non-Normal Distribution
      • Central Limit Theorem
      • Standard Deviation vs. Standard Error
  • 10:30 AM-10:40 AM break
  • 10:40 AM-12:00 PM
    • Correlation Coefficients vs. Coefficients of Determination
    • Confidence Intervals (attribute and variables)
      • Attribute
      • variables
  • 12:00 PM -1:00 PM Lunch
  • 1:00 PM -2:30 PM
    • Normality Tests and Normality Transformations
    • Statistical Process Control
      • Mathematical definition of Quality
      • Common vs. Special causes of variation
      • In-control vs. Out-of-control
      • XbarR charts
      • Control chart limits vs. Specification Limits
      • Calculation of control chart limits
      • Rules for "out of control"
      • Rational subgrouping
      • Sample Size
      • Other uses for SPC
  • 2:30 PM -2:40 PM Break
  • 2:40 PM -4:30 PM
    • Statistical Process Control (continued)
    • Confidence/Reliability Calculations
      • Regulatory requirements for Reliability, in relation to Risk Management
      • Definition of Confidence
      • Attribute data
      • Variables data, using K-tables
        • Affect of non-Normality
        • Transformation to Normality
        • How to improve accuracy when specification is 2-sided
        • Tolerance Limits
    • Variables data, using Process Capability Indices
      • 6-sigma
      • Cp, Cpk
      • Pp, Ppk
      • Confidence Limits on Cpk and Ppk
      • % In-Specification vs. values of Cpk or Ppk
      • How to improve accuracy when specification is 2-sided
  • 4:30 PM -4:40 PM break
  • 4:40 PM -6:00 PM
    • Confidence/Reliability Calculations (continued)
    • Process Capability Indices (Cp, Cpk, Pp, Ppk)
    • Q&A
Day 02(8:30 AM - 6:00 PM)
  • 08.30 AM - 08.59 AM – Registration, Meet & Greet
  • 9:00 AM -10:30 AM
    • Reliability Plotting
      • Introduction
      • Transformations
      • Comparison to K-tables
      • Examples
    • Process Validation:
      • Justification for size per validation lot
      • Justification for use of only 3 lots
      • Exam calculations
      • Variables data
      • Attribute data
  • 10:30 AM-10:40 AM break
  • 10:40 AM-12:00 PM
    • Statistical Significance
      • Regulatory requirements
      • Definition of Significance
      • t-Test as an example of a Significance Test
      • Null Hypothesis
      • 1-tailed vs. 2-tailed
      • P-value vs. Significance
      • The problem with using Z-tables
      • Meaning of Significance
      • Testing for Superiority
      • Testing for Non-Inferiority
      • TOST
  • 12:00 PM -1:00 PM Lunch
  • 1:00 PM -2:30 PM
    • Statistical Power
      • Regulatory requirements
      • Explanation of Power, using t-Tests
      • Calculation of Power using software
      • Sample size
      • Affect of non-Normality
      • What level of Power is acceptable?
    • Metrology (statistical analysis of measurement uncertainty)
      • MSA-4
      • Regulatory requirements
      • Accuracy vs. Precision vs. Resolution
      • Calibration
      • Gage Correlation
      • Gage Bias
      • Gage Linearity
      • Gage R&R
      • Guard Banding
      • Uncertainty Budget
      • Dry-lab equivalent to a Gage R&R
  • 2:30 PM -2:40 PM Break
  • 2:40 PM -4:30 PM
    • QC Sampling Plans
      • Regulatory requirements
      • Attribute vs. Variables plans
      • AQL vs. LQL
      • ANSI Z1.4
      • C=0
      • Classic AQL QC Sampling Plans
      • OC curves
      • Consumer Risk
      • AOQL
      • Better alternatives to AQL-based QC specifications
    • Statistically Valid Conclusion Statements
  • 4:30 PM -4:40 PM break
  • 4:40 PM -6:00 PM
    • Summary and recommendations
    • Q&A (including analysis of any data brought by students)
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John N Zorich

John N Zorich
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.

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