The handout www.stat.umn.edu/~sandy/courses/8051/handouts/usingR/ from Stat8051 fall 2007 might be helpful if you are new to R, or if you would like to install LATEX on your Mac or PC.
The Presentation The presentation should be a Beamer/Power Point presentation on the computer. You will also need to prepare a handout that summarizes what you have done. If the handout has computer output, it should either be annotated on the handout, or else discussed in class so the other students can add annotation as you go over the handout. Students who are not presenters may have different solutions to problems and these should be discussed during the presentation.
Presentation Tools You are not required to use Beamer, but if you choose to do so the presentation at www.stat.umn.edu/~sandy/courses/8801/handouts/03.Beamer/beamer.pdf might help you get started. If you would like to use Sweave as well for ``reproducible" handouts, look at www.stat.umn.edu/~charlie/Sweave/.
Grading The group of three for the week can decide among themselves who will grade what. In previous years the leader did all the grading for the week. Each assignment is worth ten points; the grader should email me the grades within a week..
What should be on the presentation Let's take Assignment #1 as an example. The first problem concerns a data problem for which a linear mixed model is probably appropriate. The assigned problem is relatively vague, but the homework assignment gives a hint to the general procedure you should follow. You may need to use several R packages beyond the standard packages, and it is assumed that you have seen some of these in 8051/2. In any case, all data-analysis problems, the bulk of this course, will consist of (1) initial exploration of the data, the understand the basic structure. Occasionally data sets used in homework have gross errors and missing values, and you need to find these. After you are confident about the structure of the data, you can proceed to (2) modeling, but you would want to be able to answer questions like: is it reasonable for this factor to be random, that term to enter the model linearly, and so on. This will become easier as the course goes on. Your presentation will present some R details, lots of graphs, and words that summarize your findings.
The presentation for the second problem should be much like the first. The questions are more general, so you need to decide on which methods/summaries are useful, and what you have learned from the analysis.
The third problem requires you to derive a likelihood function,
but not do any data analysis. You will probably want to begin
with the likelihood function for a usual GLMM (or maybe for any
mixed-model with a linear predictor
with
random). The twist in the problem is that the predictors
are unobservable, but rather you only observe sums of the
predictors. If you can write down the likelihood function you
would then want to give as much detail as you can about how you
might maximize it. Again, this was not discussed in class, so
you will have to find this information on your own.
If there are more students than homework assignments, some students will never get to be the ``Leader" and present homework. These students will, however, be required to give a presentation on another topic during the second half of the course. Details will follow later.
Class will not meet on Friday, November 4.
A mid-term exam, covering the material from the Faraway book, will count for 30% of the grade. The final exam, covering the whole course, will also count for 30% of the grade.
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.
Collaboration on the exam is absolutely prohibited. Any students who work together on exams will be given a failing grade in the course.