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 introduction level of statistics 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.

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 introduction level of statistics 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 decoration, 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.

1. To give the student a solid foundation to deal with the design and
analysis of experiments
2. To provide the student with some useful methods in his/her professional
practice as engineering in the general areas of product and process
design, process improvement, and quality engineering
3. To provide examples which are based on real-world applications of
experimential design

1. To give the student a solid foundation to deal with the design and
analysis of experiments
2. To provide the student with some useful methods in his/her professional
practice as engineering in the general areas of product and process
design, process improvement, and quality engineering
3. To provide examples which are based on real-world applications of
experimental design

Design and Analysis Of Experiments (8th edition)
Douglas C. Montgomery
歐亞書局有限公司
ISBN:978-0-470-39882-1

Design and Analysis Of Experiments (8th edition)
Douglas C. Montgomery
European Books Bureau Co., Ltd.
ISBN:978-0-470-39882-1