Syllabus

MSAN 601 — Linear Regression Analysis

Instructor: Jeff Hamrick, Ph.D., CFA, FRM 
Course Syllabus
Fall 2012
SUMMARY INFORMATION
Instructor: Jeff Hamrick, Ph.D., CFA, FRM
Office: Masonic 211
Office Hours: Over Google Hangout, Thursdays and Mondays, 7:30 p.m. - 9:30 p.m.

Cell Phone: 617/943-4619 Office Phone: 415/422-6810 Email Address: jhamrick@usfca.edu

Class Location: Presidio Campus
Class Time: 10:00 a.m. - 12 noon, Wednesdays and Fridays

ON COURSE GOALS. Any student who successfully completes this course should:

  • Understand the structural forms of classical simple and multiple linear regression models, as well as the assumptions underlying these models;

  • Be familiar with the principles underlying parameter estimation for these classes of models;

  • Formulate and test hypotheses using econometric models, as well as use models for both

    prediction and explanation;

  • Be able to use R to load and clean data, fit regression models, and generate various outputs

    like ANOVA tables, confidence intervals for parameters, diagnostic assessments, etc.;

  • Be able to check or to test whether or not fitted residuals conform to the assumptions that

    underlie classical regression;

  • Rigorously identify and manage outliers and influential observations, all while understanding

    the potential implications of leaving them uncensored;

  • Be able to modify regression models to encourage fitted residuals to more closely conform

    to the assumptions that underlying classical regression, i.e., handle multicollinearity, het-

    eroscedasticity, autocorrelation, a non-normal error term, and specification errors;

  • Understand how to use dichotomous dependent variables, indicator (or dummy) independent

    variables, and interaction terms;

  • Place regression analysis within economic, financial, and business contexts, i.e., be an analyst

    who uses regression as a tool rather than an analyst who treats regression as a black box; and

  • Be able to communicate the results of complete and well-reasoned regression analysis both

          orally and in writing.

ABOUT ME. My name is Jeff Hamrick. I’m a term assistant professor of finance and business 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 jhamrick@usfca.edu. 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.

ABOUT YOU. You should be hard-working and enthusiastic about learning and, in most cases, you are a candidate for the Master of Science in Business Analytics at the University of San Francisco. Ideally, you will have taken and passed some sort of introductory statistics class prior to attempting this course, as well as all three “intensive” (or boot camp) courses for the analytics program. See me immediately if you do not satisfy both of these properties!

ABOUT US. We will meet to talk about finance, econometrics, and regression models from Wednesday, August 23, 2012 to Saturday, September 29, 2012. We will meet at the Presidio Campus. We will use the first edition of Applied Econometrics with R by Christian Kleiber and Achim Zeileis (ISBN 978-0387773162) and the third edition of Introductory Econometrics: A Modern Approach by Jeffrey Wooldridge (ISBN 978-0324289787). I will also be using the fifth edition of Introductory Econometrics with Applications by Ramu Ramanathan (ISBN 978-0030343421), the second edition of Introductory Econometrics for Finance by Chris Brooks (ISBN 978-0521694681), and the fourth edition of Econometric Methods by Jack Johnston and John Dinardo (ISBN 978- 0079131218) as other references in this course.

ON R. R is a powerful open-source programming language and software environment for statistical 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 analysis, classification analysis, clustering, and much more. It will be extensively used in the MSAN program. A set of screencast tutorials related to R will be made available on my YouTube channel and Blackboard.

ON ATTENDANCE. This course meets for only seven weeks. It will be short and intense. 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 documentable.

To this end, the following attendance policy is in place for this course and it cannot be modified for any reason. Your first two absences in this class are excused. For each absence in excess of two absences, your final grade in this course will be reduced by 5%.

ON LAPTOPS. In general, I want you to have a laptop in class and I want you to install R on 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 classmates and to refrain from surfing the web, checking out Facebook, tweeting people your various tweets, etc. during the middle of my lectures.

ON HOMEWORK. Every week, there will be a collection of homework problems (generally including regressions for you to run using R) that I will assign from the Johnston and Dinardo textbook and supplement from the Kleiber/Brooks/Ramanathan textbooks. You must work on these problem sets in groups of size three to four and turn in a single assignment. While I encourage you to work with your colleagues on the assigned problems as you prepare a common write-up, make sure that you are learning the material individually rather than passively learning while somebody else does the work. Each day, your group (which can vary from assignment to assignment) will turn in the entire collection of problems and I will grade a random subset of them, or all of them. To facilitate efficient grading, your weekly homework should have the following properties:

  1.  Each problem should be started on a separate piece of paper.
  2.  Different parts of the same problem do not need to be started on separate pieces of paper. 
  3.  Turn in the problems in the same order in which they were assigned.
  4.  Staple your homework assignment in the upper left-hand corner.
  5.  In general, do not print out entire data sets.
  6.  In general, do not print out reams and reams of R outputs. Everything should be orderly and easy for me to read.

Unfortunately, we won’t have enough time to do homework problems in class or to discuss homework problems in great detail. Instead, feel free to come to my virtual office hours or to schedule an individual appointment with me. I will not accept late homework assignments under any circumstances.

ON QUIZZES. We will take a quick quiz at the beginning of class every Wednesday. Each in-class quiz will focus on material that we have recently discussed in class (generally, the topics from the prior day). The in-class quizzes will be centered on definitions, concepts, and simple computations, as well as interpretation of pre-generated statistical output. At the end of the course, I will drop your lowest quiz grade.

ON THE FINAL EXAMINATION. There will be a final written comprehensive examination in this course on October 12 during the regular course time, with some possibility for extra time (say, 10:00 a.m. - 1:00 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 regressions.

ON GRADING. I’ve noticed that students are often too focused on grades, to the great detri- 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:

Component

   Weight

Weekly Homework Sets 

Quizzes

Final Examination

   30% 

   30% 

   40%

ON CHEATING. As a Jesuit institution committed to cura personalis—the care and education 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 staff. 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/. Specifically, 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.

ON DISABILITIES. If you are a student with a disability or disabling condition, or if you think 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.