Industrial Statistics Training

Industrial Statistics Training

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Industrial Statistics Training Course Description

This Industrial Statistics Training course brings together important concepts that allow engineering and operations organizations to understand industrial statistics concepts. The focus is on applying these concepts to optimize processes, implement statistical process control, and use statistical concepts in assessing product and process performance. The Industrial Statistics Training course utilizes real-life case studies to help you understand these technologies. At the end of the Industrial Statistics Training course, you will have an understanding of the key industrial statistics tools, technologies, terminology, and capabilities.

Tailored Classes

Onsite classes can also be tailored to meet your needs. You might shorten a 5-day class into a 3-day class, or combine portions of several related courses into a single course, or have the instructor vary the emphasis of topics depending on your staff and site’s requirements.

Industrial Statistics TrainingRelated Courses

Industrial Statistics Training


Duration: 2-3 days


After completing this course, attendees will be able to:

•Work together in an effective team environment to implement industrial statistical concepts.
•Use the technologies presented in this course to identify key product design and manufacturing process tolerances and control limits.
•Reduce or eliminate areas of specification non-compliance.
•Proactively design test and inspection approaches that are consistent with product and process capabilities.

Course Outline:

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

•Product nonconformance considerations
•Improving processes with statistical tools
•Using Excel’s built in statistical analysis features
•Case studies

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