No class will be held on Monday, October 2.
The paper grader is Casey Li, lixx0334@stat.umn.edu. Her office hours are M3-4 & Tu11-12, in 352 Ford. You can talk to her about homework, graded and ungraded, and any other aspect of the course.
We will explain in class what you need to do to get R working on your computer. Also, you can download from home.comcast.net/~lthompson221/Splusdiscrete2.pdf the book S-PLUS (and R) Manual to Accompany Agresti’s Categorical Data Analysis (2002) 2nd edition, by Laura A. Thompson, 2006. Be warned, though, that this supplement makes the computing seem a lot harder than necessary.
There are many other useful books on getting started with R (including free books), that you can find from the ``Manuals'' or ``Books'' items on the R website.
For the project, you will need to write a paper of about 1500 words in any of the following three general areas. (1) You can read a published theoretical paper about some categorical data methodology. Examples of papers you can use are in the bibliography to the textbook, but you are not limited to these papers. Your project will be a summary of this paper. (2) You can write an R function to implement the analysis of a particular type of catgorical data problem. (3) You can analyze or reanalyze a data set of interest to you. In any of these cases, you need approval from the instructor for your project well before the due date.
Separate grading scales will be used for 5421 graduate and 5421 undergraduate students and for 8421 students. For 5421 students, Exam 1 and 2 will each be worth 25% of the grade, the final exam is 35% and homework 15%. For 8421, Exam 1 and 2 will each be worth 20%, the final exam 30%, homework 15% and the project 15%.
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.
Academic integrity is essential to a positive teaching and learning environment. All students enrolled in University courses are expected to complete coursework responsibilities with fairness and honesty. Failure to do so by seeking unfair advantage over others or misrepresenting someone else’s work as your own, can result in disciplinary action. The University Student Conduct Code defines scholastic dishonesty as follows:
Scholastic Dishonesty: Scholastic dishonesty means plagiarizing; cheating on assignments or examinations; engaging in unauthorized collaboration on academic work; taking, acquiring, or using test materials without faculty permission; submitting false or incomplete records of academic achievement; acting alone or in cooperation with another to falsify records or to obtain dishonestly grades, honors, awards, or professional endorsement; altering forging , or misusing a University academic record; or fabricating or falsifying data, research procedures, or data analysis.
Within this course, a student responsible for scholastic dishonesty can be assigned a penalty up to and including an "F" or "N" for the course. If you have any questions regarding the expectations for a specific assignment or exam, ask.
This course will follow the book, starting at the beginning and ending at the end, but not all sections will be assigned. This is a very fast-paced course.