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STATS
762 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. Most of the lectures are in common
with STAT 330. There are extra lectures dealing with the geometric approach
to least squares, and an extra assignment requiring a higher level of
mathematical and computing knowledge. 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 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: There will be five data-analytic 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. There is also a theoretical assignment dealing with the
geometric material. Tests: Instead of a lecture on September
11, there will be a test of one hour's duration, at the usual lecture time
and place. The test will be "closed book". There will be an
additional test in week 10 of the semester, time and place to be arranged. Examination: The final examination for STATS
762 will be held at a time and place to be arranged. It will 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 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 - 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 course book.
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