engineering, applied statistical consulting,
and training services for R&D, product, process,
and manufacturing engineering organizations.
Course Description: This
course presents the fundamental concepts of statistical data analysis
and interpretation for quality engineering including acceptance
sampling, statistical process control, reliability, and design of
experiments. The material covered includes: graphical data presentation
methods; basic concepts of counting (permutations and combinations);
the discrete probability distributions (hypergeometric, binomial, and
Poisson); and the continuous probability distributions (normal,
Student’s t, chi-square, and F). Students will learn to use these
distributions to describe the data from various types of processes and
use the data to construct confidence intervals and perform hypothesis
tests to make data-based decisions. Extensive examples will be taken
from acceptance sampling and SPC applications. Introductions will be
presented to linear regression, correlation, analysis of variance,
sample size calculations, and reliability. The use of statistical
software (e.g. MINITAB) to solve problems may be integrated into the
course and is highly recommended.
Prerequisite: Students should
have good basic algebra skills.
Textbook: Quality Engineering
Statistics by MM&B Inc. The Customer may also provide
students with a copy of an appropriate reference quality engineering
statistics text, e.g. Ostle, Turner, Hicks, and McElrath (1996),
Engineering Statistics: The Industrial Experience, Duxbury Press, ISBN
Contact Hours: 32 to 40 hours.
Students have better command and retention of the material if the
course is delivered over an extended period of time, such as in one
four-hour session each week for nine weeks.
Homework: Six assignments
requiring about 2-6 hours each (3 hours nominal) will be given. The
Customer may decide if students are required to do the homework
although students’ post-course statistics skills are strongly and
positively correlated to the amount of homework that they do.
Upon completion of this course
students should be able to:
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
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
Use approximations between the various distributions
to obtain numerical solutions and to approach problems from various
Use normal probability plots and quantitative methods
to test data for normality.
Calculate and interpret process capability and
performance 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.
Use the results from hypothesis tests and confidence
intervals to make data-based decisions.
Identify and interpret cases of Type 1 and Type 2
Fit lines and simple curves to data using linear
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
Estimate product reliability from experimental data
using the exponential, normal, and Weibull models.