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 16hour program that provides
participants with an indepth 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.


Materials
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 ztest. The
ttest. When ztests and ttests 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 ftest. 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. MILSTD105 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. X_{bar}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.
