Students

STAT8111 – Generalized Linear Models

2022 – Session 2, In person-scheduled-weekday, North Ryde

General Information

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Unit convenor and teaching staff Unit convenor and teaching staff Convenor/Lecturer
Thomas Fung
thomas.fung@mq.edu.au
Contact via Email
Room 626, 12 Wally's Walk
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Lecturer
Iris Jiang
iris.jiang@mq.edu.au
Contact via Email
See iLearn for details
Lecturer
Benoit Liquet-Weiland
benoit.liquet-weiland@mq.edu.au
Contact via Email
See iLearn for details
Credit points Credit points
10
Prerequisites Prerequisites
((Admission to MAppStat or MScInnovationStat or GradCertAppStat or GradDipAppStat or MDataSc) and ((STAT806 or STAT810 or STAT6110) and STAT6175)) or (admission to MMarScMgt or MConsBiol or GradDipConsBiol and (STAT830(Cr) or STAT8830(Cr))) or (Admission to MBusAnalytics and BUSA8000 and ECON8040))or (Admission to MActPrac and (STAT806 or STAT810 or STAT8310))
Corequisites Corequisites
Co-badged status Co-badged status
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 non-normal responses and categorical responses; and models for correlated responses, both normal and non-normal, and generalised additive models, are studied. All models and methods are illustrated using data sets from disciplines such as biology, actuarial studies and medicine.

Important Academic Dates

Information about important academic dates including deadlines for withdrawing from units are available at https://www.mq.edu.au/study/calendar-of-dates

Learning Outcomes

On successful completion of this unit, you will be able to:

  • ULO1: Formulate a generalized linear model and derive its maximum likelihood estimators.
  • ULO2: Answer research questions by exploring data graphically; selecting and applying appropriate modelling techniques; appraising underlying model assumptions and goodness of fit, and modifying the analysis if required.
  • ULO3: Perform model selection and test hypothesis.
  • ULO4: Apply the generalized additive model to incorporate nonlinear forms of the predictors and use random effects or generalized estimating equations to model correlated data.
  • ULO5: Use statistical software to create model output and interpret them.

General Assessment Information

ASSIGNMENT SUBMISSION: Assignment submission will be online through the iLearn page.

Submit assignments online via the appropriate assignment link on the iLearn page. A personalised cover sheet is not required with online submissions. Read the submission statement carefully before accepting it as there are substantial penalties for making a false declaration.

  • Assignment submission is via iLearn. You should upload this as a single PDF file.
  • Please note the quick guide on how to upload your assignments provided on the iLearn page. 
  • Please make sure that each page in your uploaded assignment corresponds to only one A4 page (do not upload an A3 page worth of content as an A4 page in landscape). If you are using an app like Clear Scanner, please make sure that the photos you are using are clear and shadow-free.
  • It is your responsibility to make sure your assignment submission is legible.
  • If there are technical obstructions to your submission online, please email us to let us know.

You may submit as often as required prior to the due date/time. Please note that each submission will completely replace any previous submissions. It is in your interests to make frequent submissions of your partially completed work as insurance against technical or other problems near the submission deadline.

LATE SUBMISSION:  

Unless a Special Consideration request has been submitted and approved a 5% penalty (of the total possible mark) will be applied each day a written assessment is not submitted up until the 7th day (including weekends). After the 7th day a grade of '0' will be awarded even if the assessment is submitted. Submission time for all written assessments is set at 11:55 pm. A 1-hour grace period is provided to students who experience a technical concern around the original due date, i.e. on-time submission. Late submissions that are more than an hour late to the original due date do not receive a 1-hour grace period. 

Assessments where Late Submissions will be accepted.

In this unit late submissions will be accepted as follows:

  • Assignment 1 -- YES, Standard Late Penalty applies;
  • Assignment 2 -- YES, Standard Late Penalty applies;
  • Assignment 3 -- YES, Standard Late Penalty applies.

FINAL EXAM POLICY: There is no final exam for this unit.

Assessment Tasks

Name Weighting Hurdle Due
Assignment 1 30% No Week 4
Assignment 2 40% No Week 9
Assignment 3 30% No Week 13

Assignment 1

Assessment Type 1: Quantitative analysis task
Indicative Time on Task 2: 10 hours
Due: Week 4
Weighting: 30%

 

Assignment

 
On successful completion you will be able to:
  • Formulate a generalized linear model and derive its maximum likelihood estimators.
  • Answer research questions by exploring data graphically; selecting and applying appropriate modelling techniques; appraising underlying model assumptions and goodness of fit, and modifying the analysis if required.
  • Perform model selection and test hypothesis.
  • Use statistical software to create model output and interpret them.

Assignment 2

Assessment Type 1: Quantitative analysis task
Indicative Time on Task 2: 12 hours
Due: Week 9
Weighting: 40%

 

Assignment

 
On successful completion you will be able to:
  • Formulate a generalized linear model and derive its maximum likelihood estimators.
  • Answer research questions by exploring data graphically; selecting and applying appropriate modelling techniques; appraising underlying model assumptions and goodness of fit, and modifying the analysis if required.
  • Perform model selection and test hypothesis.
  • Use statistical software to create model output and interpret them.

Assignment 3

Assessment Type 1: Quantitative analysis task
Indicative Time on Task 2: 10 hours
Due: Week 13
Weighting: 30%

 

Assignment

 
On successful completion you will be able to:
  • Formulate a generalized linear model and derive its maximum likelihood estimators.
  • Answer research questions by exploring data graphically; selecting and applying appropriate modelling techniques; appraising underlying model assumptions and goodness of fit, and modifying the analysis if required.
  • Perform model selection and test hypothesis.
  • Apply the generalized additive model to incorporate nonlinear forms of the predictors and use random effects or generalized estimating equations to model correlated data.
  • Use statistical software to create model output and interpret them.

1 If you need help with your assignment, please contact:

  • the academic teaching staff in your unit for guidance in understanding or completing this type of assessment
  • the Writing Centre for academic skills support.

2 Indicative time-on-task is an estimate of the time required for completion of the assessment task and is subject to individual variation

Delivery and Resources

Classes 

Lectures: There is 1 x 2 hr lecture each week.

SGTAs: There is 1 x 1 hr SGTA class each week. 

Course notes 

Course notes are available on iLearn, prior to the lecture. SGTA solutions are posted on iLearn.

Required and recommended resources

There is no prescribed text for this unit. The following are useful references:

  1. Fahrmeir, L., Kneib, T., Lang, S. and Marx, B. (2013). Regression: Models, Methods and Applications, Springer.   
  2. Faraway, J. J. (2016). Extending the linear model with R: generalized linear, mixed effects and nonparametric regression models. CRC Press.  
  3. De Jong, P. and Heller, G.Z. (2008). Generalized Linear Models for Insurance Data, Cambridge University Press.  
  4. Wood, Simon N. (2017). Generalized additive models: an introduction with R, 2nd edition. CRC Press.  
  5. Stasinopoulos M. D., Rigby R. A., Heller G. Z., Voudouris V., De Bastiani F. (2017). Flexible Regression and Smoothing: Using GAMLSS in R. CRC Press.  
  6. Dobson, A. J. and Barnett, A. G. (2018). An Introduction to Generalized Linear Models, 4th edition, Chapman & Hall.  
  7. Lindsey, J.K. (1997). Applying Generalized Linear Models, Springer.   
  8. McCullagh, P. and Nelder, J.A. (1989). Generalized Linear Models, 2nd edition, Chapman & Hall.

Recommended web sites

A comprehensive list of online resources for self-learning R, is given on iLearn.

www.gamlss.com

www.statsci.org/glm/

en.wikipedia.org/wiki/Generalized_linear_model

Technology used

We will be using R, which is freely downloadable from the CRAN website. We recommend the use of the RStudio interface, also freely downloadable. 

iLearn

We will be using iLearn for posting 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 lectures 

Audio recordings of the lectures (Echo) will be available on the iLearn site.

Unit Schedule

Unit Schedule

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

Assignment 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; Zero-inflated models; Generalized additive models

  

9

Regression models for ordinal and categorical responses

Assignment 2 due

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

 

13

No Lecture

 Assignment 3 due

Policies and Procedures

Macquarie University policies and procedures are accessible from Policy Central (https://policies.mq.edu.au). Students should be aware of the following policies in particular with regard to Learning and Teaching:

Students seeking more policy resources can visit Student Policies (https://students.mq.edu.au/support/study/policies). It is your one-stop-shop for the key policies you need to know about throughout your undergraduate student journey.

To find other policies relating to Teaching and Learning, visit Policy Central (https://policies.mq.edu.au) and use the search tool.

Student Code of Conduct

Macquarie University students have a responsibility to be familiar with the Student Code of Conduct: https://students.mq.edu.au/admin/other-resources/student-conduct

Results

Results published on platform other than eStudent, (eg. iLearn, Coursera etc.) or released directly by your Unit Convenor, are not confirmed as they are subject to final approval by the University. Once approved, final results will be sent to your student email address and will be made available in eStudent. For more information visit ask.mq.edu.au or if you are a Global MBA student contact globalmba.support@mq.edu.au

Academic Integrity

At Macquarie, we believe academic integrity – honesty, respect, trust, responsibility, fairness and courage – is at the core of learning, teaching and research. We recognise that meeting the expectations required to complete your assessments can be challenging. So, we offer you a range of resources and services to help you reach your potential, including free online writing and maths support, academic skills development and wellbeing consultations.

Student Support

Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/

The Writing Centre

The Writing Centre provides resources to develop your English language proficiency, academic writing, and communication skills.

The Library provides online and face to face support to help you find and use relevant information resources. 

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Student Enquiries

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IT Help

For help with University computer systems and technology, visit http://www.mq.edu.au/about_us/offices_and_units/information_technology/help/. 

When using the University's IT, you must adhere to the Acceptable Use of IT Resources Policy. The policy applies to all who connect to the MQ network including students.

Changes since First Published

Date Description
01/08/2022 A new unit guide is needed as there is a change in the Teaching Team.