Prerequisites: Students should have good basic algebra skills.

Contact Hours: 24 to 40 hours

Textbook: Students will be provided with a copy of detailed presentation notes (about 600 pages). They may also be provided with a copy of an appropriate statistics text like: Freund and Simon, Modern Elementary Statistics, ISBN 0-13-858291-2; Johnson, Miller and Freund’s Probability and Statistics for Engineers, ISBN 0-13-721408-1; or Ostle, Turner, Hicks, and McElrath, Engineering Statistics: The Industrial Experience, ISBN 0-534-26538-3.

Course Description: This course presents the fundamental concepts of data analysis required to prepare students for advanced topics like acceptance sampling, statistical process control, reliability, and design of experiments. The material covered includes graphical presentation methods, basic concepts of counting (permutations and combinations) and probability, the discrete probability distributions of quality (hypergeometric, binomial, and Poisson), the normal, Student’s t, chi-square, and F distributions. Students will learn to use these distributions to construct confidence intervals and perform hypothesis tests to make data based decisions. Examples will be taken from acceptance sampling and SPC applications. Introductions will be presented to linear regression, correlation, analysis of variance, and reliability. Extensive homework assignments will be given.

• Use simple graphical techniques to perform preliminary analysis of data.
• Use the counting rules to solve permutation and combination problems associated with SPC run rules and experimental design.
• Calculate and interpret statistics for measuring location and variation.
• Apply and interpret the basic discrete probability distributions and apply them to attribute acceptance sampling and SPC (np, p, c, and u) charts.
• Set up, maintain, and operate basic SPC control charts for attributes and variables.
• Use the normal distribution to estimate the fraction defective of a process and set specs to achieve a desired fraction defective.
• Use approximations between the various distributions to obtain numerical solutions and to approach problems from various viewpoints.
• Use normal probability plots to test data for normality.
• Calculate and interpret process capability statistics (cp, cpk, Pp, Ppk) and understand how to evaluate the capability of non-normal data.
• Perform hypothesis tests and construct confidence intervals for means, standard deviations, and fractions.
• Identify and interpret cases of Type 1 and Type 2 testing errors.
• Fit lines and simple curves to data using linear regression.
• Evaluate linear regression models for assumption validity, goodness of fit, and model significance.
• Perform and interpret simple one-way and two-way analysis of variance problems.
• Calculate sample sizes for hypothesis tests and confidence intervals for measuring means, standard deviations, and fractions defective.
• Perform basic calculations for reliability using the exponential, normal, and Weibull models.

Basic Quality Statistics for Six Sigma

This course was developed for GE Lighting for training Six Sigma Black Belts in statistical methods to support Six Sigma quality improvement programs. Paul has presented this course to GE Lighting Technology organizations in Cleveland and Mexico, to GE Six Sigma Black Belts from all over the country, and to other local companies like Swagelok, Royal/Dirt Devil, and STERIS. The course provides a detailed introduction to the methods and applications of probability and statistics for quality improvement. The material covered includes graphical presentation methods, basic concepts of counting (permutations and combinations) and probability, the discrete probability distributions of quality (hypergeometric, binomial, and Poisson), the normal, Student’s t, chi-square, and F distributions. Students will learn to use these distributions to construct confidence intervals and perform hypothesis tests to make data based decisions. Introductions will be presented to linear regression, correlation, analysis of variance, and reliability. Examples are taken from acceptance sampling, statistical process control, design of experiments, and reliability engineering. Extensive homework assignments will be given.

 Last Revised: 01/26/2002. Counter started on 02/09/02.