Unit convenor and teaching staff 
Unit convenor and teaching staff
Unit convenor
Gillian Heller
Contact via 98508541
Office 619, 12 Wally's Walk (E7A)
TBA
Tutor
Busayasachee PuangNgern
Level 6, 12 Wally's Walk (E7A)
Lecturer
Ian Marschner
Contact via 98508557
Office 533, 12 Wally's Walk (E7A)
TBA


Credit points 
Credit points
4

Prerequisites 
Prerequisites
Admission to MRes

Corequisites 
Corequisites
STAT710

Cobadged status 
Cobadged status
STAT811 is cotaught with STAT711

Unit description 
Unit description
This unit starts with the classical normal linear regression model. The family of generalized linear models is then introduced and maximum likelihood estimators are derived. Models for counted responses, binary responses, continuous nonnormal responses and categorical responses; and models for correlated responses, both normal and nonnormal, and generalized additive models, are studied. All models and methods are illustrated using data sets from scientific disciplines such as biology, marine science and medicine. SAS software is used.

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Assignment submission Assignments should be submitted electronically on iLearn, by the given due date and time.
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Extensions and penalties Extensions to assignments are at the discretion of the unit coordinator. It is the responsibility of the student to prove that there has been unavoidable disruption. In the absence of an approved extension, the penalty for late assignments will be 5% of earned mark per day, up to maximum of 50%.
Examination There will be a twohour sitdown examination, and a takehome examination which you have four days to complete. You will be permitted to bring an A4 sheet of notes, handwritten or typed, on both sides, into the sitdown examination. The sitdown examination will be timetabled in the official University examination timetable. The timing of the takehome examination will be determined in class, once the draft University timetable has been published. A tentative handin date of 20 November has been set.
Supplementary examinations will be held in the week 11  15 December. Should you be granted a supplementary examination, you will be required to be available at the specified time in that week.
Name  Weighting  Hurdle  Due 

Assignment 1  15%  21 August  
Assignment 2  15%  2 October  
Assignment 3  15%  30 October  
Take home exam  30%  20 November (tentative date)  
Exam  25%  S2 exam period 
Due: 21 August
Weighting: 15%
Due: 2 October
Weighting: 15%
Due: 30 October
Weighting: 15%
Due: 20 November (tentative date)
Weighting: 30%
Due: S2 exam period
Weighting: 25%
Lectures and tutorials are at the following times:
Lecture: Tuesday 68pm, E3B 218
Tutorial: Tuesday 89pm, 6 Eastern Rd (E4B) 306 Faculty PC Lab
External students are expected to study the course notes and attempt the tutorials, weekly. They are also welcome to optionally attend the weekly lectures and tutorials:
Course notes: Course notes are available on iLearn, prior to the lecture. Tutorial solutions are posted on iLearn.
Required and recommended resources
There is no prescribed text for this unit. The following are useful references:
1. McCullagh, P. and Nelder, J.A. (1989). Generalized Linear Models, 2nd edition, Chapman & Hall.
2. Dobson, A. J. and Barnett, A. G. (2008). An Introduction to Generalized Linear Models, 3rd edition, Chapman & Hall.
3. De Jong, P. and Heller, G.Z. (2008). Generalized Linear Models for Insurance Data, Cambridge University Press.
4. Lindsey, J.K. (1997). Applying Generalized Linear Models, Springer.
5. Faraway, J. J. (2016). Extending the linear model with R: generalized linear, mixed effects and nonparametric regression models. CRC Press.
6. Stasinopoulos MD, Rigby RA, Heller GZ, Voudouris V, De Bastiani F (2017). Flexible Regression and Smoothing: Using GAMLSS in R. CRC Press.
7. Wood, Simon N. (2017). Generalized additive models: an introduction with R, 2nd edition. CRC Press.
Some references to texts on Generalized Linear Models are given on http://www.statsci.org/glm/books.html
http://en.wikipedia.org/wiki/Generalized_linear_model
We will be providing R code in the notes, as an alternative to SAS. R is freely downloadable from the CRAN website. We recommend use of the RStudio interface, also freely downloadable.
We will be using iLearn for posting of course notes, assignments, solutions and data sets, and online discussions. You are encouraged to use the forums for discussions on the course material. Remember that if you are confused about something, the chances are that other students are also confused. Everybody benefits from the discussions, and you should not be embarrassed to admit that you do not understand a concept.
Audio recordings of the lectures (Echo) will be available on the iLearn site.
Week 
Topics 

1 
The classical normal linear model 

2 
Introduction to GLMs: The framework of generalized linear models is introduced, and the theory behind maximum likelihood estimation of the parameters started. 

3 
Maximum likelihood estimation of the parameters; Poisson regression for count data 

4 
Inference; comparison of models The deviance as a measure of fit; hypothesis testing 
Assn 1 due 
5 
Model checking: Definition of residuals in GLMs; checking for violation of model assumptions 

6 
Model selection; overdispersion: Selection of models via AIC; the phenomenon of overdispersion; compound Poisson models to overcome it; the negative binomial model for counts 

7 
Binary responses: logistic regression 


Session 2 Break 

8 
Logistic regression contd; Zeroinflated models; Generalized additive models 
Assn 2 due 
9 
Regression models for ordinal and categorical responses 

10 
Correlated data: Models for longitudinal data, and other data structures in which there is clustering or correlation between observations 

11 
Correlated data 

12 
Correlated data 
Assn 3 due 
13 
Revision 

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Date  Description 

29/07/2017  Lecture venue updated 