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Industrial Statistics

Statistical analysis is a powerful tool for analyzing processes and product performance.  The subject can be intimidating, and for that reason do not use statistics in assessing process control and manufacturing performance.  Industrial Statistics is presented from a nonstatistician's perspective with an emphasis on how to use statistical concepts in a practical manner.

Industrial Statistics is a 16-hour program that provides participants with an in-depth understanding of how to:

  • Recognize and allow for the fundamental nature of variability.

  • Determine population means and standard deviations.

  • Develop a normal curve.

  • Recognize when distributions may not be normal.

  • Use statistical analysis to determine probabilities of occurrence based on different statistical tests.

  • Use ANOVA techniques to determine if variance is due to randomness or special causes.

  • Recognize when statistical process control might be an appropriate process control technique.

  • Implement statistical process control.


  • Industrial Statistics (approximately 200 presentation charts and related class materials), ManufacturingTraining Seminars.

Who Should Attend

Manufacturing managers, production supervisors, manufacturing engineers, industrial engineers, quality engineers, project engineers, and design engineers should attend this training.

Industrial Statistics Syllabus

  • Class 1:  Basic Statistical Concepts.   Probability.  Statistics.  Descriptive and predictive statistics.  The mean.  Standard deviations.  Bar charts and histograms.  The Pareto concept.  Sample applications.  Using Excel statistical features.  Class exercise.

  • Class 2:  Probability Concepts.  Correlating probability and standard deviations.  The z-test.  The t-test.  When z-tests and t-tests are appropriate.  Determining the likelihood that a sample is representative of the parent population.  Using Excel statistical features.  Class exercise.

  • Class 3:  Analysis of Variance.  Normal distributions and the nature of randomness.  Setting up an experiment to assess randomness and special causes.  The f-test.  Assumptions and perspectives inherent to analysis of variance.  Using Excel statistical features.  Class exercise.

  • Class 4: Taguchi Testing.  Taguchi applications background.  Orthogonality.  Selecting the appropriate orthogonal array.  Simplifying the Taguchi approach.  Sample applications.  Using Excel statistical features.  Class exercise.

  • Class 5:  Sampling Plans.  MIL-STD-105 and statistical sampling.  Risks inherent to statistical sampling.  Sample selection issues.  Reduced sampling concepts.  Consumer versus producer risk.  Using Excel statistical features. Creating an Operating Characteristic curve. 

  • Class 6:  Statistical Process Control.  Statistical process control history.  Xbar-r, np, and other statistical process control charts.  Identifying statistical process control application points.   Statistical process control implementation steps.  Overcoming organizational resistance to statistical process control implementation.  Class exercise.  Course review.  Final examination.


The above training can be customized to meet your requirements.

Need a guest speaker for an important luncheon or dinner meeting?  Please contact us.

Any questions?  Please call us at 909 204 9984 or contact us via email.

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