Regulatory agencies mandate formal protocols for Performance Qualification (PQ) studies to determine whether products/processes meet the desired requirements. When planning PQ studies, sample size determination is critical to ensure that study results will be meaningful. Methods to determine appropriate sample sizes for various types of problems will be covered.

This 2-day seminar will provide a 12-step process to assist you in writing/reviewing protocols for PQ studies with a focus on sample size justification, acceptance criteria and statistical analysis using Minitab v17. Validation of software will not be covered.

Course Information:

Participants are requested to bring a laptop with Minitab Version 17 software installed.

Learning Objectives:

Upon completing this course participants will be able to:

  • Determine a Representative Sample for Process Qualification (PQ) study.
  • Develop product/process acceptance criteria statements
  • Determine appropriate Sample Size to meet PQ acceptance criteria
  • Understand how to Analyze PQ data
  • Understand how to calculate and interpret statistical tolerance limits
  • Determine and verify the appropriate distribution of the data
  • Interpret probability plots
  • Apply normalizing transformations
  • Handle censored data and mixed failure modes.
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

Who will Benefit:

This course is designed for people tasked with writing or reviewing Performance Qualification protocols. The following personnel will benefit from the course:

  • Scientists
  • Product and Process Engineers
  • Quality Engineers
  • Personnel involved in product development and validation
  • Manufacturing engineers
  • Production engineers
  • Design engineers
  • Process owners
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Day 01(8:30 AM - 4:30 PM)
  • Registration Process - (8:30 am till 8:45 am)
  • Lecture 1: Introduction
    • Course Objectives
    • Selecting Representative Sample
    • Confidence Level Definition
    • General Flow Chart for 12-step Protocol Development
  • Lecture 2: Sample Size Determination for Performance Qualification Study
    • General Purpose
    • Acceptance Criteria
    • Attribute (Pass/Fail) Response
    • Censored (Variable) Response
  • Lecture 3: Non-Censored (Variables) Response
    • Tolerance Limits Defined
    • One-Sided vs Two-Sided Limits
    • Normality Assumptions
    • Nonparametric Tolerance Limits
Day 02(8:30 AM - 4:30 PM)
  • Lecture 4: Analyzing PQ Data using Minitab
    • Statistical Tolerance Limits
    • Confidence Level
    • Percentage of Population captured
  • Lecture 5: Distribution Fitting using Graphical Methods
    • Histograms
    • Boxplots
    • Probability Plots
  • Lecture 6: Statistical Tests for Distribution Fit
    • Anderson-Darling
    • Ryan-Joiner
    • Kolmogorov-Smirnov
  • Lecture 7: Normalizing Transformations
    • Lognormal
    • 3-Parameter Lognormal
    • Box-Cox
    • Johnson
  • Lecture 8: “Non-Normal” Statistical Tolerance Limits
    • Weibull
    • 3-Parameter Weibull
    • Smallest Extreme Value
    • Mixed populations or multiple failure modes
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Jerry Phillips

Jerry Phillips,
M.S. Statistics, President and Founder, Phillips Statistical Consulting, LLC

Jerry Phillips, M.S. Statistics, is the president and founder of Phillips Statistical Consulting, LLC. Jerry recently retired as Sr. Manager., Biostatistics, from Baxter Healthcare Corporation, a highly-regulated manufacturer and distributor of disposable infusion systems and hardware devices. During his 35+ year tenure, he participated on cross-functional teams that included engineering, manufacturing, quality and medical professionals to development new products that meet regulatory requirements. Jerry also developed and delivered statistical training worldwide that enabled thousands of engineers and scientists to interpret and report statistical results in lay terms. He is continuing to provide statistical consulting and training services through Phillips Statistical Consulting, LLC.

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