Design and Analysis of Experiments
Adam Kashlak (University of Alberta)Jan 1, 2021 — Jun 1, 2021
About the course
We will cover classical and modern methods of experimental design starting with one-way ANOVA and Cochran’s Theorem. From there, we will consider multi-factor ANOVA using a variety of combinatorial tools such as Graeco-Latin squares and incomplete block designs. There will be a brief interlude on multiple testing followed by 2 and 3 level factorial designs, fractional factorial designs, and blocking within such designs. Then, response surface designs—i.e. quadratic polynomial surfaces used for optimization of industrial processes–will be discussed. Lastly, more advanced topics will be touched on such as prime-level factorial designs and the Plackett-Burman design, which involves Hadamard matrices. Interesting datasets, connections to optimal coding theory, and at-home experiments will also be discussed. For study purposes, discussion questions will be included with the lectures and solutions will be discussed in class.
Registration
This course is available for registration under the Western Dean's Agreement. To register, you must obtain the approval of the course instructor and you must complete the Western Dean's agreement form , using the details below. The completed form should be signed by your home institution department and school of graduate studies, then returned to the host institution of the course.
Enrollment Details
- Course Name
- Design and Analysis of Experiments
- Date
- Jan 1, 2021 — Jun 1, 2021
- Course Number
- STAT568
- Section Number
- STAT568 - R1
- Section Code
- 45966
Instructor(s)
For help with completing the Western Dean’s agreement form, please contact the graduate student program coordinator at your institution. For more information about the agreement, please see the Western Dean's Agreement website
Other Course Details
Reference texts
- My online lecture notes at https://sites.ualberta.ca/~kashlak/data/stat568.pdf.
- Supplementary texts:
- Wu, CF Jeff, and Michael S. Hamada. Experiments: planning, analysis, and optimization. Vol. 552. John Wiley & Sons, 2011.