Medical Devices Training

This 4-day track provides participants with the foundation for effectively using statistical methods found in the medical devices industry to analyze, improve, and validate medical device processes.

Participants will learn to use data analysis techniques to understand variation and defects, determine the useful life of a product, assess if a process is capable of meeting customer specifications, and monitor the stability of a validated process. Analytical and statistical principles will be presented through real-world case studies, examples, and exercises

This course is most appropriate for process engineers, R&D team members, and other quality professionals who need to understand how to apply statistical tools to a medical device process.

Training Track

DAYS 1 - 2

Statistical Tools for Medical Devices - Minitab Essentials

In this 2-day foundational course you will learn to minimize the time required for data analysis by using Minitab to import data, develop sound statistical approaches to exploring data, create and interpret compelling graphs, and export results. Analyze a variety of real-world medical device data sets to learn how to align your applications with the right statistical tool and interpret statistical output to reveal problems with a process or evidence of an improvement. Learn the fundamentals of important statistical concepts, such as hypothesis testing and confidence intervals, and how to uncover and describe relationships between variables with statistical modeling tools.

This course places a strong emphasis on making sound decisions based upon the practical application of statistical techniques commonly found in the medical device industry.

Topics Include:

  • Importing and Formatting Data
  • Bar Charts
  • Histograms
  • Boxplots
  • Pareto Charts
  • Scatterplots
  • Tables and Chi-Square Analysis
  • Measures of Location and Variation
  • t-Tests
  • Proportion Tests
  • Tests for Equal Variance
  • Equivalence Tests
  • Power and Sample Size
  • Correlation
  • Simple Linear and Multiple Regression
  • One-Way ANOVA
  • Multi-Variable ANOVA

Prerequisites: None