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STATS 330 Course
Information 2005 Lecturer: Alan Lee Room 721.332, Tamaki
Campus Office Hours: Office hours are Lectures: Tuesday, Wednesday and Thursday at 9:00 am in SLT1. Tutorials: Every week there are two class tutorials, both on
Thursdays in the teaching laboratory on the first floor of the Building 303
Extension. The first tutorial is from 12 noon - 1pm, and the second from Course Content: This course provides an introduction to the
process and procedures of statistical data analysis. The topics to be covered
include graphical methods, multiple regression, regression diagnostics,
analysis of variance and analysis of covariance. We will also consider some
extensions of this kind of analysis to generalized linear models, including
log-linear models and logistic regression models. Computing: To do the assignments you will need to use a
computer. You have two options: either use the large computer laboratory in
the basement of the Building 303 Extension, or use your personal computer.
The computer language used in the course is R. Assignments: There 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. Test: Instead of a lecture, there will be a test of one
hour's duration on Thursday September 15. The test will be "closed
book". Examination: The final examination will be held at a time to
be arranged. It will also be "closed book", and of 3 hours
duration. The exam will be partly multiple choice. Texts: You can purchase the coursebook
for the course from the Resource Centre, although this is not mandatory.
Electronic copies of all the lecture slides are available on the class web
page, see below. A reading list is also given below. Web Page: All the course materials are available on the Web
from the Departmental home 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 access the course page either through the departmental
home page, through CECIL, or via the URL www.stat.auckland.ac.nz/~lee/330/ 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 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. AC Atkinson, (1982). Plots, Transformations and
Regression: A Introduction to Graphical methods of Diagnostic Residual
Analysis. 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, WS Cleveland, (1985). The Elements of Graphing
Data, 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, (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. (2001). The Elements of
Statistical Learning : Data Mining, Inference, and
Prediction. 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. 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:
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