The course will cover most of the material in the text, Chapters 1-15. The students will be required to use statistical computer software to complete many homework assignments and the project.
Overview
Syllabus
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Statistics is often taught as though the design of the data collection and the data cleaning have already been done in advance. However, as most practicing statisticians quickly learn, typically problems that arise at the analysis stage, could have been avoided if the experimenter had consulted a statistician before the experiment was done and the data were conducted. This course is created to provide an understanding of how experiments should be designed so that when the data are collected, these shortcomings are avoided.
This course covers the following topics:
- Understanding basic design principles
- Working in simple comparative experimental contexts
- Working with single factors or one-way ANOVA in completely randomized experimental design contexts
- Implementing randomized blocks, Latin square designs and extensions of these
- Understanding factorial design contexts
- Working with two level, 2k, designs
- Implementing confounding and blocking in 2k designs
- Working with 2-level fractional factorial designs
- Working with 3-level and mixed-level factorials and fractional factorial designs
- Simple linear regression models
- Understanding and implementing response surface methodologies
- Understanding robust parameter designs
- Working with random and mixed effects models
- Understanding and implementing nested and split-plot and strip-plot designs
- Using repeated measures designs, unbalanced AOV and ANCOVA
Taught by
Dr. James L Rosenberger
