|
STATS 330 Course
Information 2012 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 108, Commerce A. k.macdonald@auckland.ac.nz Lectures: Monday
Tuesday and Thursday at 8:00 am. Monday and Thursday in OGHLECTH and Tuesday
in Eng3404. First class meeting is on Monday 16th July. Note that
the first four lectures will be given by Peter Mullins. Tutorials: Every
week we have a three hour-long tutorial sessions: Wednesday 11-12, Friday
10-11 and Friday 2-3. They are held in the basement tutorial laboratory in
Building 303S, Room 303S-B75. I operate these as drop-in sessions, so you can
come at anytime during these three 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. Learning Outcomes: At the
conclusion of the course, you should have be able to
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 303S. 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. These must be typed, using Word or Latex. Test: Instead
of a lecture, there will be a test of one hour's duration on Tuesday Sept 11,
at the usual lecture time and place. The test will be "closed
book". 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
course book for this course is available on the class web page, and a
hard-copy version is available free of charge at the Statistics Department
office in Commerce A. 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 STATS 330 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 - most of the material in this course is very
traditional, apart from the use of R. J Adler
(2010). R in a Nutshell. O’Reilly. 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. B Efron and RJ Tibshirani (1993).
An Introduction to the Bootstrap. Chapman and Hall, London. 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. FE
Harrell (2001). Regression Modeling Strategies. Springer, New York. 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. P Murrell
(2009). Introduction to Data Technologies. 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.
|
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||