Instructor: Jeff Hamrick, Ph.D., CFA, FRM
Instructor: Jeff Hamrick, Ph.D., CFA, FRM
Office: Masonic 211
Office Hours: After class for one hour at the Presidio, or by appointment.
Cell Phone: 617/943-4619
Office Phone: 415/422-6810
Email Address: email@example.com
Class Location: Presidio Campus
Class Time: 10:00 a.m. - 12:30 p.m., Tuesdays and Thursdays
ON COURSE GOALS. Any student who successfully completes this course should:
analytics and I am affiliated with both the Master of Science in Business Analytics (MSAN) and
Master of Science in Financial Analysis (MSFA) programs at the University of San Francisco.
Please call me Jeff. My office is located in room 211 of the Masonic (MA) building at the cor-
ner of Masonic and Turk. My e.mail address is firstname.lastname@example.org. My cell phone number is
617/943-4619 and my office number is 415/422-6810. If you're unable to discuss academic issues
with me at the Presidio campus before or after class, let me know and we may be able to schedule
an appointment (possibly over Google Hangout) at an alternate time.
you are a candidate for the Master of Science in Analytics at the University of San Francisco.
Linear Regression Analysis (MSAN 601) is a prerequisite for this course.
until Tuesday, March 14, 2013. We will meet at the Presidio Campus. We will primarily use the
sixth, seventh, eighth, and ninth chapters of the third edition of Multivariate Statistical Methods:
A Primer by Bryan F.J. Manly (ISBN 1-58488-414-2) and chapters 2, 5, 7, 8, and 9 of the second
edition of Analysis of Multivariate Social Science Data by David J. Bartholomew, Fiona Steele,
Irini Moustaki, and Jane Galbraith (ISBN 978-1-58488-960-1). You are responsible for the
material in all readings assigned for this course, regardless of whether or not the ma-
terial from those readings is included in my in-class lectures.
computing and graphics. The R language is used by many professional statisticians and is making
deep inroads in industry as well. R is equipped with a wide variety of statistical and graphical
techniques. It supports linear and nonlinear modeling, classical statistical tests, time series anal-
ysis, classication analysis, clustering, and much more. It will be extensively used in the MSAN
program. A set of screencast tutorials related to R will be available on my YouTube channel and
Consequently, you may only miss class under the most dire of circumstances. These circumstances
should be both unusual and documentable. For example, having a bad cold is documentable but
not unusual. On the other hand, being kidnapped by aliens is unusual but is most likely not doc-
umentable. A single absence, for any reason, is acceptable and will not be penalized.
Each absence in excess of one absence will cause your nal letter grade in this class
to be lowered by one level (e.g., an A- will become a B+.)
that laptop before the course begins. You will be expected to use R on quizzes and on the final
examination, and sometimes we will use R in class. I would ask you to be respectful of your class-
mates and to refrain from surng the web, checking out Facebook, tweeting people your various
tweets, etc. during the middle of my lectures.
including tasks for you to perform in R) that I will make up myself or assign from the Manly or
Bartholomew textbooks. You must turn in your own write-up of each assignment, though you may
work with colleagues on the homework problems up until 48 hours before the homework assignment
is due. To reiterate: during the 48 hours prior to the start of the class during which
the homework assignment is due, you may not confer, work with, or write up your
homework assignments with anybody from the class. You also may not confer with
any third party, in fact. I will grade a random subset of the problems you turn in each week,
and sometimes I might grade all of them. To facilitate efficient grading, your weekly homework
should have the following properties:
work problems in great detail. Instead, feel free to come to my oce hours or to schedule an
individual appointment with me to discuss the homework. I will not accept late homework
assignments under any circumstances.
day period to schedule an appointment with Kirsten Keihl, the assistant for the M.S. in Analytics
program. You will have as much time as you like to take each quiz. While you can use your laptop
during the quiz, you are on your honor not to consult with any other resources on the Internet.
You also may not use your textbook, class notes, cheat sheets, etc. The weekly quiz will focus on
material that we have recently discussed in class -- generally, the topics from the past few lectures.
The weekly quizzes will be centered on denitions, concepts, and simple computations, as well as
interpretation of statistical output. At the end of the course, I will drop your lowest quiz grade.
in this course on May 14, 2013 during the regular course time, with some possibility for extra
time (say, 10:00 a.m. - 2:00 p.m.). The final examination will focus on concepts, i.e., you will not
be expected to engage in tons of routine calculations, but you will be expected to know certain
formulas and relationships and you will be expected to interpret the outputs of various multivariate
statistical analyses. In addition, you will be expected to use R to assist you with various statistical
ment of their own learning. If students put as much effort into actually learning material as they
did worrying about their grades, their performance would be much better. Nevertheless, part of
my job is to assign grades fairly and in a manner that reflects the high academic standards at the
University of San Francisco and in the MSAN program. In this class, we will use the standard
ten-point scale. "Plus" or "minus" grades will be assigned to students with grades close to the
extremes of each ten-point bracket (plus or minus three points from the boundary of each bracket).
Your grade in this course will be computed according to the following weights:
Homework Sets 25%
Final Examination 40%
of the whole person -- the University of San Francisco has an obligation to embody and foster the
values of honesty and integrity. The university upholds standards of honesty and integrity from all
members of the academic community, including faculty, students, and sta. All students are ex-
pected to know and to adhere to the university's honor code. You can find the full text of the code
online at http://www.usfca.edu/catalog/policies/honor/. Specically, while you are required
to work in groups with students on the homework assignments, you should not allow your name
to be placed on a group write-up if it does not reflect your own understanding of the material and
if you have not made an honest, equitable contribution to the group effort. Copying answers from
other students or sources during a quiz or examination is a violation of the university's honor code
and will be treated as such. You are also, of course, bound to the terms of the MSAN Code of
Conduct that you signed prior to matriculating in the analytics program. All incidents of cheating
or other academic misconduct will be reported to the director of the MSAN program.
you may have a disability, please contact USF Student Disability Services (SDS) at 415/422-2613
within the first week of class, or immediately upon onset of the disability, to speak with a disability
specialist. If you are determined eligible for reasonable accommodations, please meet with your
disability specialist so they can arrange to have your accommodation letter sent to me, and we will
discuss your needs for this course. For more information, please visit http://www.usfca.edu/sds/
or call 415/422-2613.