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STAT 344
Statistical Design of Experiments Fall 2020
Division III Quantative/Formal Reasoning

Class Details

How do you get informative research results? By doing the right experiment in the first place. We’ll explore the techniques used to plan experiments that are both efficient and statistically sound, the analysis of the resulting data, and the conclusions we can draw from that analysis. We’ll look at classical tools like one- and two-way ANOVA and fractional factorial designs, but we’ll also look at optimal design, and see how these two frameworks differ in their philosophy and in what they can do. Throughout the course, we’ll make extensive use of R to work with real-world data.
The Class: Format: lecture; Introductory lectures will be available asynchronously as text and video; synchronous sessions will discuss questions from lecture, dive further into the material, and work on examples. You'll use chat and discussion boards to build community, study with classmates, and ask questions outside of class time. There will also be optional synchronous office hours/review sessions.
Limit: 15
Expected: 15
Class#: 2663
Grading: yes pass/fail option, yes fifth course option
Requirements/Evaluation: Homework problems; quizzes; a final project (on a topic that interests you!). You'll be given the opportunity to assess your own work and resubmit/reattempt assignments as you gain mastery of a topic. Participation matters! Engagement with your peers is an important part of learning, of being a statistician in the Real World...and of your evaluation in this course. While most assignments will be submitted (and graded) individually, you'll be responsible for giving and receiving peer feedback, contributing to live and online discussions, and working together with classmates on practice problems.
Prerequisites: STAT 201, 202, or equivalent, or permission of instructor
Enrollment Preferences: Statistics majors, seniors
Distributions: Division III Quantative/Formal Reasoning
QFR Notes: This course uses mathematical tools and computing programs to design experiments, analyze their results, and assess their effectiveness. We'll also emphasize choosing appropriate mathematical tools and interpreting their results in a real-world context.

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