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 working knowledge of statistical methods. A formal course in engineering statistics at the level of IEE 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 and scientific work, including technology development, 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, JMP) to implement the methods presented will be illustrated extensively, and you will use these packages for homework assignments and the term project. Most problems are too tedious to work manually.
All experiments conducted by engineers and scientists are designed experiments; some of them are poorly designed, and others are well-designed. Well-designed experiments allow you to obtain reliable, valid results faster, easier, and with fewer resources than with poorly-designed experiments. You will learn how to plan, conduct and analyze experiments efficiently in this course. A well-designed experiment can lead to reduced development lead time for new processes and products, improved manufacturing process performance, and products that have superior function and reliability.
The course schedule and outline contains assigned reading topics from the textbook and suggested homework problems. I don’t collect or grade homework. Many of the assigned problems are worked in the student solutions manual. Please contact me or the course TAs if you have difficulty getting the correct answer or if you don’t understand the details of problem solution. The textbook contains a lot of worked examples. Making sure that you understand how those problems were solved is a good starting point for study. Please stay current with the lecture, reading material and homework – falling behind can have significantly bad consequences.
In addition to the textbook reading assignments you may also want to read some of the supplemental text material for each chapter. This material is found on the World Wide Web page for the book maintained by the publisher, John Wiley & Sons. See the text Preface for more details. The JMP and Design-Expert computer software packages can be used to solve most of the problems in the textbook.