University of Minnesota
Course Information for Stat 8053
Fall Semester, 2013
2:30-3:20 MWF (09/04/2013 - 12/11/2013), 151 Ford Hall

General Information

The instructor is S. Weisberg, 312 Ford Hall, 612-625-8355. I don't have fixed office hours for this course, but will happy to make appointments. Send an email to mailto:sandy@umn.edu.

Textbooks and Course Outline

These books cover most, but not all, of the material in this course. In 2013, the topics covered in lecture include: discrete regression methods (logistic, Poisson, multinomial, ordinal regression and latent class analysis); nonparametric regression (kernel, loess and spline smoothing); additive and generalized additive models using splines; other regression methods (L1 and other robust regression methods; classification trees with boosting and bagging; neural networks; regularization methods); multivariate methods (MANOVA, principal component analysis, factor analysis, discriminant analysis and clustering). Depending on the size of the class, additional topics will be covered by student presentation, usually including nonlinear regression, missing data, and causal inference.

Computing

Computing will use the program R, which is available on the School Linux network, and can also be downloaded for your own computer from http://www.r-project.org. Familiarity with R from Stat 8051-2 is assumed. The handout http://www.stat.umn.edu/~sandy/courses/8051/handouts/usingR/from Stat 8051 fall 2007 might be helpful if you are new to R, or if you would like to install LATEX on your Mac or PC.

If you will be using your own computer, you should download a number of packages. These will get you started:

> install.packages(c("faraway", "alr4", "lsmeans", "poLCA"), dependencies=TRUE)

Web Site

The web site for this class http://z.umn.edu/stat8053 includes copies of the handouts and assignments.

Homework and Presentations

There will be 6 homework sets in this class. Applied statistics is a collaborative endeavor, and on homework students are strongly encouraged to work together. However, each student must submit their own homework paper.

All students are required to make at least one presentation. You will be divided into 6 groups of 2 or 3 to put together homework solutions. One person selected by me will lead a class discussion of the homework on the due date and will present solutions to the more interesting problems. All the members of the group will read and grade the homework. The non-presenters will be required to make a presentation on an assigned topic sometime during the course. Details will follow shortly. Topics may include: (1) missing data; (2) causal inference; (3) instrumental variables; (4) Tobit models; (5) seemingly unrelated regressions. If you have a topic that is relevant to this course that you would like to present, either one of these or something else, let me know during the first week of class.

Students prepare a handout similar to the ones I give out in class. I will provide the format I use for handouts on the website. I use Sweave for reproducible handouts. Look at http://www.stat.umn.edu/~charlie/Sweave/ for a discussion. You can get the Sweave manual by googling sweave manual.

The handouts I provide in this course are primarily for reference and not for presentation, and are therefore in a less-than-optimal form for a presentation. To make presentations you should learn to use the LATEX Beamer package to produce higher quality slides; http://z.stat.umn.edu/beamer might help you get started.

Exams and Grading

Homework, including your class presentation, will account for 40% of the grade.

A mid-term exam, covering the material from the Faraway book and other regression topics, will count for 30% of the grade. The midterm exam is partly collaborative: students are divided into groups of 3 or 4 to work on a data analysis problem, but each student must prepare their own written solution based on the collaborative work.

The final exam, covering the whole course, will also count for 30% of the grade. Collaboration on the final exam is absolutely prohibited. Any students who work together on exams will be given a failing grade in the course.

Students who fail to complete all work will receive a grade based on the work done. Grades of incomplete will be given only in extraordinary circumstances, and only after negotiation with the instructor.

Inquiries regarding any changes of grade should be directed to the instructor of the course; you may wish to contact the Student Conflict Resolution Center (SCRC) in 254 Appleby (624-7272) for assistance.

Students are responsible for all information disseminated in class and all course requirements, including deadlines and examinations. The instructor will specify whether class attendance is required or counted in the grade for a class.

A student is not permitted to submit extra work in an attempt to raise his or her grade, unless the instructor has specified at the outset of the class such opportunities will be afforded to all students.

Scholastic misconduct is broadly defined as ``any act that violates the right of another student in academic work or that involves misrepresentation of your own work. Scholastic dishonesty includes, (but is not necessarily limited to): cheating on assignments or examinations; plagiarizing, which means misrepresenting as your own work any part of work done by another; submitting the same paper, or substantially similar papers, to meet the requirements of more than one course without the approval and consent of all instructors concerned; depriving another student of necessary course materials; or interfering with another student's work."

Students with disabilities that affect their ability to participate fully in class or to meet all course requirements are encouraged to bring this to the attention of the instructor so that appropriate accommodations can be arranged. Further information is available from Disabilities Services (230 McNamara).

University policy prohibits sexual harassment as defined in the December 1998 policy statement, available at the Office of Equal Opportunity and Affirmative Action. Questions or concerns about sexual harassment should be directed to this office, located in 419 Morrill Hall.

Disability Access Statement

This publication/material, and all other handouts in Statistics 8053, is available in alternative formats upon request. Please contact School of Statistics, 313 Ford, 625-8046.

Student Mental Health and Stress Management

As a student you may experience a range of issues that can cause barriers to learning, such as strained relationships, increased anxiety, alcohol/drug problems, feeling down, difficulty concentrating and/or lack of motivation. These mental health concerns or stressful events may lead to diminished academic performance or reduce a student's ability to participate in daily activities. University of Minnesota services are available to assist you with addressing these and other concerns you may be experiencing. You can learn more about the broad range of confidential mental health services available on campus via http://www.mentalhealth.umn.edu/.

Return to Stat 8053 home.


S Weisberg
2013-09-27