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STAT  Design and Analysis of Experiments

Texts:  

A First Course in Design and Analysis of Experiments, G. W. Oehlert (2010). Out of print, freely available online: http://users.stat.umn.edu/~gary/Book.html

Field Experiments: Design, Analysis, and Interpretation, A.S. Gerber and D.P. Green (2012).  Electronic course reserve available for 2-hour checkout; see user guide for electronic course reserves.

Equipment: Access to a computer with an R installation and Internet access will be necessary.  If you do not have access to a computer, you can borrow one from the University library.  See https://studenttech.berkeley.edu/hardware-lending for more details.  The Student Technology Equity Program is another good resource.  Feel free to contact the instructor if you have concerns about your access to needed technology.

This is a working draft of the syllabus and is subject to change.

Learning goals

By the end of the semester you should be able to:

1. Understand why careful design of experiments can aid us in learning from data.

2. Confidently analyze data from a designed experiment and describe the meaning of the results.

3.  Evaluate the pros and cons of different potential experimental designs and recommend a design given information about the question of interest and study constraints.

4.  Design, plan, and execute a small laboratory-style experiment to answer a question of interest.

Lectures:

· To get the full benefit of each lecture, you should read the supporting material in advance.

· Lecture notes will be posted to bCourses in advance so you can follow along.  Answers to certain discussion questions may be omitted from the notes, but versions with the answers provided will be posted within a few days after each lecture.

· Lecture audio and projector video will be recorded using a classroom document camera and posted afterwards for asynchronous viewing on bCourses.  However, I cannot yet vouch for the quality of the recording and recommend in-person attendance for at least the first few lectures.

Labs:

· You may attend a lab for which you are not officially enrolled, physical space permitting.

· Lab content will focus on data analysis, visualization, working on practice problems, and reviewing content from lecture.

· Attendance at lab will not be formally monitored, but experiments will be run in lab on Sep 25, Oct 30, and Nov 20 and attendance is expected particularly on these dates. Results from the experiments may be needed to do homework problems.

Assessment:

Homework

We anticipate giving seven homework assignments during the semester. Homework will be posted to bCourses and will generally be due 1 week later. All homework is due via Gradescope unless otherwise noted.   Homework will be a combination of computational exercises and data analysis using the computer. Mathematical computations can be handwritten, all data analysis must be type written. R code must be given in the back of homework (full .Rmd source is acceptable).  

Exams

A take-home midterm will be given.  The completion period will run from noon PDT Wednesday October 11^th  to noon PDT Saturday October 14^th.  This assignment must be conducted independently by each student and submitted via Gradescope.
The final exam will be held from 8:00-11:00 AM PST on Wednesday December 13^th.  The location will be announced later in the semester.

Final project

All students will complete a final project in which they will design, run, and analyze data from their own experiment on a topic of interest to them. Projects will be done in small groups of 3 students. The project will have intermediate steps along the way to help you pace yourself and make sure that your experiment is progressing successfully. You will be graded on the quality of the writeup as well as the quality of the design and analysis. You will not be graded on the outcome of the experiment.

Each member of the group will also individually evaluate the other members of the group, and the evaluations will not be shared with other members of the group. If there is a problem that appears based on the evaluations, different students within the group may receive different grades, though I expect this to be uncommon. This can be true even if no member of the group “complains” about other students, but merely that I found that the division was inequitable, or that the group did not jointly contribute to the components enough to deserve equal division of the grade (good or bad).

You will be given the option either to form your own group or to be placed into a group by the instructors.  After the initial proposal is turned in, an opportunity will be provided to switch groups if it turns out that your group is not working well. After this point, it will be difficult to change group membership. If after this point you feel that there is a problem in your group, you may discuss it with me privately to find an equitable solution with respect to finishing the project.  

The anticipated deadlines associated with the different portions of the project are as follows:

· Tuesday, September 12, 11:59 PM PDT – project groups formed.

· Thursday, September 21, 11:59 PM PDT – initial proposal due.

· Thursday, Nov 2, 11:59 PM PDT – design proposal due.

· Thursday, Nov 9, 11:59 PM PST – protocol due.

· Thursday, December 14, 11:59 PM PST – final report due.

· Friday, December 15, 11:59 PM PST – self-evaluation due.

Overall score

Your letter grade for the course will be based on a weighted average of your scores for all work in the semester as follows.

· Homework (each assignment weighted equally): 42%

· Take-home midterm: 10%

· Group project: 33%

· Final exam: 15%

Grades will not be curved.  Students scoring 90% or above overall will receive letter grades in the A-range, students scoring 75%-90% will receive letter grades in the B-range, and students scoring 60%-75% will receive letter grades in the C-range.

Policies

Possibility of revisions to course policies

All course policies, including assessment, are subject to change during the semester in response to unforeseen events including but not limited to public health directives, natural disasters, and medical emergencies among members of the course staff.

Late Assignments

All students will have 5 late days that they may use for turning in homework after the due date. This will take the place of any extensions due to sickness or conflicts, unless there are extenuating circumstances, so use them wisely. To use a late day, you must submit a Google Form requesting a late day before the homework is due, or you risk receiving a large penalty or a zero.  Late day requests by email will not be answered. Late days cannot be used for the group project or the midterm, and they cannot be requested once homework solutions are posted. Late days are counted at the 24-hour-period level after the time the assignment was due. E.g., the first assignment is due at 11:59pm, September 7. If you turn it in at 1am, September 8, it counts as use of one late day, and if you turn it in at 1am, September 9, it counts as use of two late days.

Regrade requests

Regrade requests on an assignment are due within one week of the release of the graded assignments and the solutions (if applicable).  Regrade requests should be submitted through Gradescope.  In writing a regrade request, please be specific about the nature and exact location of the error you feel the grader has made, with reference to the solutions if available.

Academic Honesty Policy

The student community at UC Berkeley has adopted the following Honor Code: “As a member of the UC Berkeley community, I act with honesty, integrity, and respect for others.” My expectation is that you will adhere to this code. Beyond the importance of respecting your fellow students, acting with integrity in completing course assignments helps ensure that they achieve their purpose, which is to help you learn and develop valuable statistical understanding and skills.

· Homework must be done independently. If you get stuck or want to explore alternative approaches, feel free to discuss issues with students or course staff (including on the online forum); however, you may not do the homework jointly, nor may you ask for or share complete code or solutions.  Sharing solutions or obtaining and/or using solutions from previous years or from the Internet, if such are available, is considered cheating.

· During the take-home midterm, you must not consult with any other person besides the course staff, although you will be allowed free use of books, class notes, and online resources.

· On all written assignments, including the homework, you should include a section listing all the sources you drew on in producing your answers; on the homework, you should also list the names of other students with whom you consulted.

Anyone caught cheating will be given a score of zero (0) on the assignment/exam and will be reported to the University’s Office of Student Conduct.

Inclusivity and Accommodation

My hope is to establish a learning environment in this course that welcomes diversity of thought, perspective, and experience, and to be respectful of your individual identity as a student.  I am happy to use your preferred name and/or personal pronoun.   If you feel uncomfortable due to anything that is said in class, or if you feel that your performance in the course is being impacted by experiences outside of class, please do not hesitate to reach out to me about your concerns.  

In addition, if you need accommodations for any physical, psychological, or learning disability, please speak to me after class or during office hours. Please note that you must make arrangements in a timely manner through DSP so that I can make the appropriate accommodations.

Acknowledgments

Most of the materials used in this course, including this syllabus, are close adaptations from materials originally created or compiled and generously shared by Prof. Elizabeth Purdom.  In writing this syllabus I also adapted content from Prof. Chris Paciorek, and from Prof. Monica Linden of Brown University.

Calendar

The following is only a guide, and there is likely to be slight variation as the semester progresses.

The reading described below is a guide to where the relevant material can be found in the book for the subjects described under ‘Topic.’  It is not a prescription to when you should actually read the material; that is left to your discretion. Updated reading guide will also posted on bCourses.

O=Oehlert, G&G = Gerber & Green, *=reading not from book, available on bCourses.