Industrial Statistics

Day 1

• Basic Probability and Statistics
  • Deterministic versus probabilistic thinking
  • The normal curve: Its history and mathematics
  • The nature of variability
  • Means and standard deviations
  • Using normal curves, means, and standard deviations to predict probabilities of occurrence
  • Confidence levels
• Minimizing Variability
  • Product and process design
  • Identifying sources of variability
  • Identifying potential key performance parameters
  • The concept of a capable process
  • Approaches for minimizing variability
• Basic Statistics Test Approaches
  • The z-test
  • The t-test
  • Analysis of variance (ANOVA)
  • Fractional factorial experiments and Taguchi testing
  • Case studies

Day 2

• Detection versus Prevention Process and Design Approaches
  • Detection-oriented systems
  • Prevention-oriented systems
  • Collecting and using nonconformance data
• Test and Inspection
  • The nature of inspection
  • Sampling plans
  • Inspection shortfalls
  • The fallacy of redundant inspection
  • Statistical process control
  • Statistical process control implementation
  • Development, qualification, and acceptance testing
  • Probabilities of passing receiving, in-process, and final acceptance testing
  • Operating characteristic curves
• Applications
  • Product nonconformance considerations
  • Improving processes with statistical tools
  • Using Excel’s built in statistical analysis features
  • Case studies
• Course Wrap-up: Recap, Q/A, and Evaluations