Six Sigma Course Descriptions

IEE 572 Design of Engineering Experiments (or DOE)

This is a basic course in designing experiments and analyzing the resulting data. It is intended for engineers, physical/chemical scientists and scientists from other fields such as biotechnology and biology. The course deals with the types of experiments that are frequently conducted in industrial settings. The prerequisite background is a basic working knowledge of statistical methods. A formal course in engineering statistics at the level of ECE 380 is the official prerequisite, but this specific course isn’t essential. You will need to know how to compute and interpret the sample mean and standard deviation, have previous exposure to the normal distribution, be familiar with the concepts of testing hypotheses (the t-test, for example), constructing and interpreting a confidence interval, and model-fitting using the method of least squares. Most of these ideas will be reviewed as they are needed. The course objective is to learn how to plan, design and conduct experiments efficiently and effectively, and analyze the resulting data to obtain objective conclusions. Both design and statistical analysis issues are discussed. Opportunities to use the principles taught in the course arise in all phases of engineering work, including new product design and development, process development, and manufacturing process improvement. Applications from various fields of engineering (including chemical, mechanical, electrical, materials science, industrial, etc.) will be illustrated throughout the course. Computer software packages (Design-Expert, Minitab) to implement the methods presented will be illustrated extensively, and you will have opportunities to use it for homework assignments and the term project.

IEE 570 ADV Quality Control

Process monitoring with control charts (Shewhart, cusum, EWMA), feedback adjustment and engineering process control, process capability, autocorrelation, selected topics from current literature.

IEE 578 Regression Analysis

This is a basic course in regression analysis and model-building for engineers and physical/chemical scientists. Specifically, it focuses on building empirical models for relating an observed response to one or more predictor or regressor variables. Regression methods based on linear least squares are the primary technique presented, although some attention will be given to other parameter estimation techniques. The course prerequisite is one previous course in engineering statistics. You do not need previous exposure to regression, but introductory knowledge of hypothesis testing, confidence intervals, and familiarity with matrix algebra is required. Modern regression analysis requires use of the computer. The software package utilized in this course is Minitab. We will also illustrate some SAS output for features not supported by Minitab, but you will not be required to learn SAS. You will be expected to interpret Minitab output for exams. Other statistical packages may be used for your homework, but you will need to interpret Minitab output.

IEE 581 Six Sigma Methodology

The six sigma process improvement strategy of define, measure, analyze, improve, and control (DMAIC). Integrates and deploys statistical methods and other six sigma problem solving via the DMAIC framework.

IEE 585 Six Sigma Capstone Experience

The DMAIC (define, measure, analyze, improve, control) improvement strategy is applied in the formulation and execution of a six sigma project.