Students
STAT711 – Generalized Linear Models
2014 – S2 Evening
General Information
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| Unit convenor and teaching staff |
Unit convenor and teaching staff
Unit convenor
Gillian Heller
Contact via gillian.heller@mq.edu.au
E4A 533
Friday 9-11am
Lecturer
Ian Marschner
Contact via ian.marschner@mq.edu.au
E4A 540
11am Wednesday
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|---|---|
| Credit points |
Credit points
4
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| Prerequisites |
Prerequisites
Admission to MRes
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| Corequisites |
Corequisites
STAT710
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| Co-badged status |
Co-badged status
STAT811 is co-taught with STAT411 and STAT711
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| 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 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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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:
- Formulate a generalized linear model; estimate the parameters using SAS or other appropriate statistical software; perform diagnostic model checking; perform model selection; and interpret the model parameters.
- Derive the maximum likelihood estimators for a generalized linear model, and test hypotheses.
- Carry out in-depth graphical data exploration, and perform appropriate data transformations.
- Formulate and estimate a model for correlated data, using random effects or generalized estimating equations, as appropriate; interpret the model parameters.
- Formulate and estimate a generalized additive model.
- Write a well-structured technical report on statistical analysis performed.
- Write a report on statistical analysis performed, for a non-statistical audience.
General Assessment Information
In order to pass the unit, students need to perform satisfactorily on all components of assessment (assignments and examinations).
Assignment submission Assignments should be submitted to the lecturer, by 6pm on the due date. On-campus students are expected to submit assignments at the lecture; external students should email or mail them.
Extensions and penalties Extensions to assignments is at the discretion of the lecturer. It is the responsibility of the student to prove that there has been unavoidable disruption. Marks will be deducted for late submissions in the absence of an approved extension.
Examination There will be a two-hour sit-down examination, and a take-home 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 sit-down examination. Please note that students who have not performed satisfactorily in the assignments, will not be permitted to sit either the sit-down or the take-home examination. Any student who is to be excluded from the examinations, will be notified in writing of this after the due date of the last assignment. The sit-down examination will be timetabled in the official University examination timetable. The timing of the take-home examination will be determined in class, once the draft University timetable has been published. A tentative hand-in date of 24 November has been set.
Assessment Tasks
| Name | Weighting | Due |
|---|---|---|
| Assignment 1 | 15% | August 25 |
| Assignment 2 | 15% | October 13 |
| Assignment 3 | 15% | November 3 |
| Take home exam | 30% | November 24 |
| Exam | 25% | S2 exam period |
Assignment 1
Due: August 25
Weighting: 15%
On successful completion you will be able to:
- Formulate a generalized linear model; estimate the parameters using SAS or other appropriate statistical software; perform diagnostic model checking; perform model selection; and interpret the model parameters.
- Derive the maximum likelihood estimators for a generalized linear model, and test hypotheses.
- Carry out in-depth graphical data exploration, and perform appropriate data transformations.
- Write a well-structured technical report on statistical analysis performed.
- Write a report on statistical analysis performed, for a non-statistical audience.
Assignment 2
Due: October 13
Weighting: 15%
On successful completion you will be able to:
- Derive the maximum likelihood estimators for a generalized linear model, and test hypotheses.
- Carry out in-depth graphical data exploration, and perform appropriate data transformations.
- Formulate and estimate a model for correlated data, using random effects or generalized estimating equations, as appropriate; interpret the model parameters.
- Write a well-structured technical report on statistical analysis performed.
- Write a report on statistical analysis performed, for a non-statistical audience.
Assignment 3
Due: November 3
Weighting: 15%
On successful completion you will be able to:
- Formulate a generalized linear model; estimate the parameters using SAS or other appropriate statistical software; perform diagnostic model checking; perform model selection; and interpret the model parameters.
- Derive the maximum likelihood estimators for a generalized linear model, and test hypotheses.
- Carry out in-depth graphical data exploration, and perform appropriate data transformations.
- Formulate and estimate a model for correlated data, using random effects or generalized estimating equations, as appropriate; interpret the model parameters.
- Formulate and estimate a generalized additive model.
- Write a well-structured technical report on statistical analysis performed.
- Write a report on statistical analysis performed, for a non-statistical audience.
Take home exam
Due: November 24
Weighting: 30%
On successful completion you will be able to:
- Formulate a generalized linear model; estimate the parameters using SAS or other appropriate statistical software; perform diagnostic model checking; perform model selection; and interpret the model parameters.
- Derive the maximum likelihood estimators for a generalized linear model, and test hypotheses.
- Carry out in-depth graphical data exploration, and perform appropriate data transformations.
- Formulate and estimate a model for correlated data, using random effects or generalized estimating equations, as appropriate; interpret the model parameters.
- Formulate and estimate a generalized additive model.
- Write a well-structured technical report on statistical analysis performed.
- Write a report on statistical analysis performed, for a non-statistical audience.
Exam
Due: S2 exam period
Weighting: 25%
On successful completion you will be able to:
- Formulate a generalized linear model; estimate the parameters using SAS or other appropriate statistical software; perform diagnostic model checking; perform model selection; and interpret the model parameters.
- Derive the maximum likelihood estimators for a generalized linear model, and test hypotheses.
- Carry out in-depth graphical data exploration, and perform appropriate data transformations.
- Formulate and estimate a model for correlated data, using random effects or generalized estimating equations, as appropriate; interpret the model parameters.
- Formulate and estimate a generalized additive model.
Delivery and Resources
Lectures and tutorials are at the following times:
Lecture: Monday 6-8pm, E6A 131
Tutorial: Monday 8-9pm, E4B 214
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. SAS manual, available in the SAS help menu.
Some references to texts on Generalized Linear Models using SAS are given on http://www.statsci.org/glm/books.html
Recommended web sites
http://en.wikipedia.org/wiki/Generalized_linear_models
TECHNOLOGY USED
Software
We will be using the software SAS version 9.3. If you require the software for your home computer, we will supply you with a fully working version (with one year’s licence). You can also access SAS remotely using the iLab application. Please see separate handout concerning this.
iLearn
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 lectures Audio recordings of the lectures (Echo) will be available on the iLearn site.
Unit Schedule
|
Week |
Topics |
|
1 |
The classical normal linear model |
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2 |
Introduction to GLMs: The framework of generalized linear models is introduced, and the theory behind maximum likelihood estimation of the parameters started. |
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3 |
Maximum likelihood estimation of the parameters; Poisson regression for count data |
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4 |
Inference; comparison of models The deviance as a measure of fit; hypothesis testing |
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5 |
Model checking: Definition of residuals in glms; checking for violation of model assumptions |
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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 |
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7 |
Binary responses: logistic regression |
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Session 2 Break |
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8 |
No lecture (public holiday) |
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9 |
Logistic regression contd; Zero-inflated models; Generalized additive models |
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10 |
Regression models for ordinal and categorical responses |
|
11 |
Correlated data: Models for longitudinal data, and other data structures in which there is clustering or correlation between observations |
|
12 |
Correlated data |
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13 |
Correlated data |
Policies and Procedures
Macquarie University policies and procedures are accessible from Policy Central. Students should be aware of the following policies in particular with regard to Learning and Teaching:
Academic Honesty Policy http://mq.edu.au/policy/docs/academic_honesty/policy.html
Assessment Policy http://mq.edu.au/policy/docs/assessment/policy.html
Grading Policy http://mq.edu.au/policy/docs/grading/policy.html
Grade Appeal Policy http://mq.edu.au/policy/docs/gradeappeal/policy.html
Grievance Management Policy http://mq.edu.au/policy/docs/grievance_management/policy.html
Disruption to Studies Policy http://www.mq.edu.au/policy/docs/disruption_studies/policy.html The Disruption to Studies Policy is effective from March 3 2014 and replaces the Special Consideration Policy.
In addition, a number of other policies can be found in the Learning and Teaching Category of Policy Central.
Student Code of Conduct
Macquarie University students have a responsibility to be familiar with the Student Code of Conduct: https://students.mq.edu.au/support/student_conduct/
Student Support
Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/
Learning Skills
Learning Skills (mq.edu.au/learningskills) provides academic writing resources and study strategies to improve your marks and take control of your study.
Student Services and Support
Students with a disability are encouraged to contact the Disability Service who can provide appropriate help with any issues that arise during their studies.
Student Enquiries
For all student enquiries, visit Student Connect at ask.mq.edu.au
IT Help
For help with University computer systems and technology, visit http://informatics.mq.edu.au/help/.
When using the University's IT, you must adhere to the Acceptable Use Policy. The policy applies to all who connect to the MQ network including students.
Graduate Capabilities
PG - Discipline Knowledge and Skills
Our postgraduates will be able to demonstrate a significantly enhanced depth and breadth of knowledge, scholarly understanding, and specific subject content knowledge in their chosen fields.
This graduate capability is supported by:
Learning outcomes
- Formulate a generalized linear model; estimate the parameters using SAS or other appropriate statistical software; perform diagnostic model checking; perform model selection; and interpret the model parameters.
- Derive the maximum likelihood estimators for a generalized linear model, and test hypotheses.
- Carry out in-depth graphical data exploration, and perform appropriate data transformations.
- Formulate and estimate a model for correlated data, using random effects or generalized estimating equations, as appropriate; interpret the model parameters.
- Formulate and estimate a generalized additive model.
Assessment tasks
- Assignment 1
- Assignment 2
- Assignment 3
- Take home exam
- Exam
PG - Critical, Analytical and Integrative Thinking
Our postgraduates will be capable of utilising and reflecting on prior knowledge and experience, of applying higher level critical thinking skills, and of integrating and synthesising learning and knowledge from a range of sources and environments. A characteristic of this form of thinking is the generation of new, professionally oriented knowledge through personal or group-based critique of practice and theory.
This graduate capability is supported by:
Learning outcomes
- Formulate a generalized linear model; estimate the parameters using SAS or other appropriate statistical software; perform diagnostic model checking; perform model selection; and interpret the model parameters.
- Carry out in-depth graphical data exploration, and perform appropriate data transformations.
- Formulate and estimate a model for correlated data, using random effects or generalized estimating equations, as appropriate; interpret the model parameters.
- Formulate and estimate a generalized additive model.
Assessment tasks
- Assignment 1
- Assignment 2
- Assignment 3
- Take home exam
- Exam
PG - Research and Problem Solving Capability
Our postgraduates will be capable of systematic enquiry; able to use research skills to create new knowledge that can be applied to real world issues, or contribute to a field of study or practice to enhance society. They will be capable of creative questioning, problem finding and problem solving.
This graduate capability is supported by:
Learning outcomes
- Formulate a generalized linear model; estimate the parameters using SAS or other appropriate statistical software; perform diagnostic model checking; perform model selection; and interpret the model parameters.
- Carry out in-depth graphical data exploration, and perform appropriate data transformations.
- Formulate and estimate a model for correlated data, using random effects or generalized estimating equations, as appropriate; interpret the model parameters.
- Formulate and estimate a generalized additive model.
Assessment tasks
- Assignment 1
- Assignment 2
- Assignment 3
- Take home exam
- Exam
PG - Effective Communication
Our postgraduates will be able to communicate effectively and convey their views to different social, cultural, and professional audiences. They will be able to use a variety of technologically supported media to communicate with empathy using a range of written, spoken or visual formats.
This graduate capability is supported by:
Learning outcomes
- Write a well-structured technical report on statistical analysis performed.
- Write a report on statistical analysis performed, for a non-statistical audience.
Assessment tasks
- Assignment 1
- Assignment 2
- Assignment 3
- Take home exam
- Exam
PG - Engaged and Responsible, Active and Ethical Citizens
Our postgraduates will be ethically aware and capable of confident transformative action in relation to their professional responsibilities and the wider community. They will have a sense of connectedness with others and country and have a sense of mutual obligation. They will be able to appreciate the impact of their professional roles for social justice and inclusion related to national and global issues
This graduate capability is supported by:
Learning outcomes
- Write a well-structured technical report on statistical analysis performed.
- Write a report on statistical analysis performed, for a non-statistical audience.
Assessment task
- Take home exam
PG - Capable of Professional and Personal Judgment and Initiative
Our postgraduates will demonstrate a high standard of discernment and common sense in their professional and personal judgment. They will have the ability to make informed choices and decisions that reflect both the nature of their professional work and their personal perspectives.
This graduate capability is supported by:
Learning outcomes
- Write a well-structured technical report on statistical analysis performed.
- Write a report on statistical analysis performed, for a non-statistical audience.
Assessment tasks
- Assignment 1
- Assignment 2
- Assignment 3
- Take home exam
Changes since First Published
| Date | Description |
|---|---|
| 01/08/2014 | Consultation hours for Gillian Heller changed. |