## About this Course

Welcome to the course notes for **STAT 503: Design of Experiments**. These notes are designed and developed by Penn State's Department of Statistics and offered as open educational resources. These notes are free to use under Creative Commons license CC BY-NC 4.0.

This course is part of the Online Master of Applied Statistics program offered by Penn State's World Campus.

#### Currently enrolled?

If you are a current student in this course, please see Canvas for your syllabus, assignments, lesson videos and communication from your instructor.

#### How to enroll?

If you would like to enroll and experience the entire course for credit please see 'How to enroll in a course' on the World Campus website.

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Course Overview
Section* *

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, \(2^k\), designs
- Implementing confounding and blocking in \(2^k\) 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