- Class Number 1623
- Term Code 3220
- Class Info
- Unit Value 6 units
- Mode of Delivery In Person
- Dr Tao Zou
- Dr Tao Zou
- Class Dates
- Class Start Date 10/01/2022
- Class End Date 11/03/2022
- Census Date 21/01/2022
- Last Date to Enrol 21/01/2022
This course is intended to introduce students to generalised linear modelling methods, with emphasis on, but not limited to, common methods for analysing categorical data. Topics covered include a review of multiple linear regression and the analysis of variance, log-linear models for contingency tables, logistic regression for binary response data, Poisson regression, model selection and model checking , mixed effects models. Additional topics may include Bayesian analysis for generalized linear models and generalized mixed effect models.
Upon successful completion, students will have the knowledge and skills to:
- Explain in detail the role of generalised linear modelling techniques (GLMs) in modern applied statistics and implement methodology;
- Demonstrate an in-depth understanding of the underlying assumptions for GLMs and perform diagnostic checks whilst identifying potential problems; and
- Perform statistical analyses using statistical software, incorporating underlying theory and methodologies.
Where possible, topics will be related to current research problems and reflect real world situations to emphasize the use of the techniques covered.
Additional Course Costs
The only other additional course costs are a calculator and printing materials.
Examination Material or equipment
There is no examination for this course. Please see Assessment sections for details and required material.
Class materials, including detailed lecture notes, slides, lecture demonstrations, tutorials, assignments and other relevant materials, will be made available on the class web page on Wattle. It is essential that you visit the class Wattle site regularly.
As there is a lot of detailed course material already available, and the course lecture notes are designed to be self-contained, there is NO prescribed text for this course. However, I will provide a list of suggested references for optional supplementary reading in the lecture notes.
Students will be given feedback (through both verbal and written comments) in the following forms in this course:
• To the whole class during lectures.
• Within tutorials.
• Individually during consultation hours.
Students will also be given written comments in the marked assignments.
ANU is committed to the demonstration of educational excellence and regularly seeks feedback from students. Students are encouraged to offer feedback directly to their Course Convener or through their College and Course representatives (if applicable). The feedback given in these surveys is anonymous and provides the Colleges, University Education Committee and Academic Board with opportunities to recognise excellent teaching, and opportunities for improvement. The Surveys and Evaluation website provides more information on student surveys at ANU and reports on the feedback provided on ANU courses.
Information listed in this course summary is tentative, and students will be made aware of any alterations to aspects of the course e.g., changes in assessment due dates, information regarding the structure of the intensive week, via the course Wattle site.
|Week/Session||Summary of Activities||Assessment|
|1||Pre-intensive period (4 weeks remote; Jan 10- Feb 4; each week consists of 3 x 1 hour of pre-recorded lectures, 1 x 15 minutes of pre-recorded weekly catch-up, and 1 x 2 hours pre-recorded tutorial): C1: Introduction (W1) C2: Revision of Linear Models (W1) C3: ANOVA and ANCOVA (W2) C4: Linear Mixed Effects Models (W3) C5: Introduction to Generalised Linear Models (GLMs) (W4)||Assignment 1 open and due|
|2||Intensive period (1 week in online live format; Feb 7 - Feb 11; each day consists of 1 x 3.5 hours online live lectures + 1 x 2 hours online live tutorials. Both tutorials and lectures will be recorded): C6: Binary Logistic Regression C7: Inference and Variable Selection for GLMs C8: Poisson Log-Linear Regression C9: Model Diagnostics C10: Binomial Logistic Regression C11: Multicategory Logistic Regression C12: Over and Under-Dispersion C13: Various Topics of Interest|
|3||Post-intensive period (4 weeks remote; Feb 14 - Mar 11; there are no classes during this period.)||Assignment 2 open and due Final Project open and due|
Tutorial registration is not required.
|Assessment task||Value||Due Date||Return of assessment||Learning Outcomes|
|Assignment 1||15 %||04/02/2022||11/02/2022||1,2,3|
|Assignment 2||25 %||28/02/2022||07/03/2022||1,2,3|
|Final Project||60 %||11/03/2022||18/04/2022||1,2,3|
* If the Due Date and Return of Assessment date are blank, see the Assessment Tab for specific Assessment Task details
ANU has educational policies, procedures and guidelines, which are designed to ensure that staff and students are aware of the University’s academic standards, and implement them. Students are expected to have read the Academic Misconduct Rule before the commencement of their course. Other key policies and guidelines include:
- Student Assessment (Coursework) Policy and Procedure
- Special Assessment Consideration Policy and General Information
- Student Surveys and Evaluations
- Deferred Examinations
- Student Complaint Resolution Policy and Procedure
The ANU is using Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. For additional information regarding Turnitin please visit the Academic Integrity . In rare cases where online submission using Turnitin software is not technically possible; or where not using Turnitin software has been justified by the Course Convener and approved by the Associate Dean (Education) on the basis of the teaching model being employed; students shall submit assessment online via ‘Wattle’ outside of Turnitin, or failing that in hard copy, or through a combination of submission methods as approved by the Associate Dean (Education). The submission method is detailed below.
Moderation of Assessment
Marks that are allocated during Semester are to be considered provisional until formalised by the College examiners meeting at the end of each Semester. If appropriate, some moderation of marks might be applied prior to final results being released.
During the four week pre-intensive period, there will be 3 x 1 hour of pre-recorded lectures, 1 x 15 minutes of pre-recorded weekly catch-up, plus 1 x 2 hours of pre-recorded tutorials per week.
During the one-week intensive period, the course will operate in an online live (recorded) format. Each day of the intensive week will consists of 1 x 3.5 hour online live lectures, plus 1 x 2 hours online live tutorial. All lectures and tutorials will be online live, as well as recorded.
There will be no classes during the post-intensive period.
All assessment tasks will be conducted remotely.
Consultations will be live through Zoom during the four week pre-intensive period and the post-intensive period. The Zoom link will be given on Wattle class site.
There is no examination for this course.
Assessment Task 1
Learning Outcomes: 1,2,3
Turnitin submission. The students are expected to complete this assignment individually. This assignment is built based on materials C1- C4. Assignments will include both derivation and application problems. The assignment questions will be released two weeks before the due date. The notification about access to the assignment will also be announced in the weekly catch-up recording and on Wattle. Assignments are expected to be in a PDF or Word file.
Value: 15% and compulsory.
Estimated return date: The week after submission.
Assessment Task 2
Learning Outcomes: 1,2,3
Turnitin submission. The students are expected to complete this assignment individually. This assignment is built based on materials C1- C13. Assignments will include both derivation and application problems. The assignment questions will be released two weeks before the due date. The notification about access to the assignment will also be announced on Wattle. Assignments are expected to be in a PDF or Word file.
Value: 25% and compulsory.
Estimated return date: The week after submission.
Assessment Task 3
Learning Outcomes: 1,2,3
Turnitin submission. The students are expected to complete this project individually. This final project will be based on all the materials covered throughout the duration of this course. The final project is a compulsory piece of assessment and worth 60% of the final raw score. Students will be provided with further details regarding the final project on the last day of the one-week intensive period. This project requires the use of R to analyse real data. This project is designed to apply all the materials introduced in this course to analyse real datasets assigned by the course convener. Written reports for this project (10 pages maximum for the main manuscript and 20 pages maximum for the appendix based on the format below, and all the R code should be relegated to the appendix) are expected to be submitted via Turnitin. Turnitin similarity check will be conducted for all the submitted reports.
Value: 60% and compulsory.
Report Format – PDF or Word Upload
Use Australian English spelling. All pages (uploaded in PDF or Word form) must be as follows:
• Black type, or occasional coloured type for highlighting purposes;
• Single column;
• White A4 size paper with at least 0.5 cm margin on each side, top and bottom;
• Text must be size 12 point Times New Roman or an equivalent size before converting to PDF format and must be legible to assessors; and
• References and appendices only can be in 10 point Times New Roman or equivalent.
Academic integrity is a core part of the ANU culture as a community of scholars. At its heart, academic integrity is about behaving ethically, committing to honest and responsible scholarly practice and upholding these values with respect and fairness.
The ANU commits to assisting all members of our community to understand how to engage in academic work in ways that are consistent with, and actively support academic integrity. The ANU expects staff and students to be familiar with the academic integrity principle and Academic Misconduct Rule, uphold high standards of academic integrity and act ethically and honestly, to ensure the quality and value of the qualification that you will graduate with.
The Academic Misconduct Rule is in place to promote academic integrity and manage academic misconduct. Very minor breaches of the academic integrity principle may result in a reduction of marks of up to 10% of the total marks available for the assessment. The ANU offers a number of online and in person services to assist students with their assignments, examinations, and other learning activities. Visit the Academic Skills website for more information about academic integrity, your responsibilities and for assistance with your assignments, writing skills and study.
Any student identified, either during the current semester or in retrospect, as having used ghost writing services will be investigated under the University’s Academic Misconduct Rule. You will be required to electronically sign a declaration as part of the submission of your assignment. Please keep a copy of the assignment for your records. Unless an exemption has been approved by the Associate Dean (Education) submission must be through Turnitin.
There is no hardcopy submission in the course.
No submission of assessment tasks without an extension after the due date will be permitted. If an assessment task is not submitted by the due date, a mark of 0 will be awarded.
Accepted academic practice for referencing sources that you use in presentations can be found via the links on the Wattle site, under the file named “ANU and College Policies, Program Information, Student Support Services and Assessment”. Alternatively, you can seek help through the Students Learning Development website.
The marked assignments will be returned online.
Extensions and Penalties
Extensions and late submission of assessment pieces are covered by the Student Assessment (Coursework) Policy and Procedure. Extensions may be granted for assessment pieces that are not examinations or take-home examinations. If you need an extension, you must request an extension in writing on or before the due date. If you have documented and appropriate medical evidence that demonstrates you were not able to request an extension on or before the due date, you may be able to request it after the due date.
Resubmission of Assignments
It will not be possible for assignments to be resubmitted.
Distribution of grades policy
Academic Quality Assurance Committee monitors the performance of students, including attrition, further study and employment rates and grade distribution, and College reports on quality assurance processes for assessment activities, including alignment with national and international disciplinary and interdisciplinary standards, as well as qualification type learning outcomes.
Since first semester 1994, ANU uses a grading scale for all courses. This grading scale is used by all academic areas of the University.
Support for students
The University offers students support through several different services. You may contact the services listed below directly or seek advice from your Course Convener, Student Administrators, or your College and Course representatives (if applicable).
- ANU Health, safety & wellbeing for medical services, counselling, mental health and spiritual support
- ANU Diversity and inclusion for students with a disability or ongoing or chronic illness
- ANU Dean of Students for confidential, impartial advice and help to resolve problems between students and the academic or administrative areas of the University
- ANU Academic Skills and Learning Centre supports you make your own decisions about how you learn and manage your workload.
- ANU Counselling Centre promotes, supports and enhances mental health and wellbeing within the University student community.
- ANUSA supports and represents undergraduate and ANU College students
- PARSA supports and represents postgraduate and research students
Covariance regression modelling, network data modelling, financial statistics, environmental statistics, dependent data analysis and big data analysis
Dr Tao Zou
Dr Tao Zou