|
STATS
330/762 Course Information 2011 Lecturer:
Alan
Lee Office Hours: Office hours are 10:30 - 12:00
Tuesday and Thursday. Students may expect to find me in my office and
available for consultation during these times. Outside office hours I don't
guarantee to be in, but welcome enquiries if I am. Alternatively, make an
appointment with our Departmental Manager Karen McDonald in Rm 202. Lectures: Monday Tuesday and Thursday at
8:00 am. Monday and Thursday in MLT1 and Tuesday in Eng1404. First class meeting is on Monday
18th July. Note that the lectures for the third week will be taken
by Arden Miller, the rest by Alan Lee. Tutorials: Every week on Thursday we have a
two hour-long tutorial sessions: the first from 12 to 1 and the second from 4
to 5. They are held in the first floor tutorial laboratory in Building 303S,
Room 303-130. I operate these as drop-in sessions, so you can come at anytime
during the two hours. Usually a worksheet is available for you to work
through, so you can develop the R skills required for the current
assignment. Help is also available for
any aspect of the course. NB:
Tutorials begin in the second week. Course Content: This course provides an
introduction to the process and procedures of statistical modelling. The
topics to be covered include graphical methods, multiple regression,
regression diagnostics, analysis of variance and analysis of covariance. We
also consider some extensions of this kind of analysis to generalized linear
models, including log-linear models and logistic regression models, with
particular emphasis on the analysis of contingency tables. Computing: To do the assignments you will
need to use a computer. You can either use one of the University computer
laboratories, or your own personal computer.
Some help on computing issues is available in the large computer
laboratory in the basement of the Building 303 Extension The computer language used in the
course is R. If you are using your own computer, you will need to load R onto
it. See the course website for instructions. Assignments: For students enrolled in STATS 330,
will be five assignments. The due dates are given in the Course Planner
below. The assignments will typically call for a computer analysis of a set
of data. I much prefer that they be typed, using Word or Latex. Students
enrolled in STATS 762 will do an extra assignment. Test: Instead of a lecture, there will
be a test of one hour's duration on Tuesday Sept 13, at the usual lecture
time and place. The test will be "closed book". Students enrolled
in STATS 762 will also sit an extra test in week 10 of the semester, time and
place to be arranged. Examination: The final examination for both
STATS 330 and STATS 762 will be held at a time and place to be arranged. It will also be "closed book",
and be of 3 hours duration. The exam will be partly multiple-choice. Texts: The coursebook for this course is
available on the class web page, and also is available for purchase at the
Student Resource Centre. In addition, electronic copies of all the lecture
slides (with voice-over) are available on the class web page. A reading list
is also given below. Web Page: All the course materials are
available on the Web. Follow the link on the class Cecil page. All
assignments will be distributed via the Web and via CECIL. There is also a
bulletin board, which you should consult regularly. You can also access the
course page via the URL www.stat.aucklan Assessment: The final mark for the year is
calculated on the basis of the assignments, the test and the end of year
examination. The assessment components for both STATS 330 and STATS 762 are
valued as follows (total 100%) Assignments: 20% Test 20% Examination 60% In order to pass the paper you
must get 50% out of the total of 100%. Note: It is very important that
you attempt ALL of the assignments and sit the test. Assignments are a very
important part of this course as they give you practice in applying the
theory and techniques presented in lectures to actual problems. You will find
it difficult to master the ideas discussed in the course without the practice
you get from doing the assignments. Collaboration: It is my view that discussion with
other students is an important part of the learning process and I encourage
you to discuss problems with each other (and me!) However, you must not copy
the details of another person's assignment. In other words, you can work
together to decide how to do an assignment, but you must write up your own
solutions. You must not collaborate during tests and examinations. Reading List: I have found the following books
useful in the preparation of the course. Some of them are classic works - the
material in this course is very traditional, apart from the use of R. A Agresti, (2002). Categorical
Data Analysis, 2nd Ed, Wiley. JM Chambers, WS Cleveland, B
Kleiner and PA Tukey, (1983). Graphical Methods for Data Analysis, Duxbury
Press. JM Chambers and TJ Hastie, (1992).
Statistical Models in S, S Chatterjee, AS Hadi (2006).
Regression Analysis by Example (4th Ed), Wiley. WS Cleveland, (1994). The Elements
of Graphing Data (revised Ed), Hobart Press. WS Cleveland, (1993). Visualizing
Data, RD Cook and RD Cook and P Dalgaard, (2002). Introductory
Statistics with R, Springer. AJ Dobson, (2002). An Introduction
to Generalized Linear Models (2nd Ed), Chapman & Hall. NR Draper and H Smith, (1998). Applied
Regression Analysis (3rd Ed), Wiley. J Fox, (1997). Applied Regression
Analysis, Linear Models, and Related Methods, Sage Publications. J Fox, (2002). An R and S-Plus
Companion to Applied Regression, Sage Publications. T Hastie and RJ. Tibshirani,
(1990). Generalized Additive Models. Chapman and Hall. T Hastie, R Tibshirani and J
Friedman. (2009). The Elements of Statistical Learning : Data Mining,
Inference, and Prediction (2nd ed). Springer. DW Hosmer and DG Kleinbaum and M Klein, (2002).
Logistic Regression : a Self-Learning Text. DC Montgomery, EA. Peck and GG
Vining. (2001). Introduction to Linear Regression Analysis (3rd Ed), Wiley. P Murrell (2006). R Graphics.
Chapman and Hall WN Venables and BD Ripley, (2004).
Modern Applied Statistics with S, 4th Ed, Springer. WN Venables and DM Smith, (2002).
Introduction to R, Springer. Course Planner: Chapters refer to
chapters in the coursebook.
|
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||