• Class Number 3375
  • Term Code 3630
  • Class Info
  • Unit Value 6 units
  • Mode of Delivery In Person
  • COURSE CONVENER
    • AsPr Francis Hui
  • LECTURER
    • AsPr Francis Hui
  • Class Dates
  • Class Start Date 23/02/2026
  • Class End Date 29/05/2026
  • Census Date 31/03/2026
  • Last Date to Enrol 02/03/2026
SELT Survey Results

Generalised Linear Modelling (STAT8130)

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.

Learning Outcomes

Upon successful completion, students will have the knowledge and skills to:

  1. Explain in detail the role of generalised linear modelling techniques (GLMs) in modern applied statistics and implement methodology.
  2. Demonstrate an in-depth understanding of the underlying assumptions for GLMs and perform diagnostic checks whilst identifying potential problems.
  3. Perform statistical analyses using statistical software, incorporating underlying theory and methodologies.

Research-Led Teaching

This course builds upon the foundations students have been exposed to and mastered in introductory statistics course/s, particularly regression modelling. By adopting a research-oriented teaching framework, the contents and activities of this course is designed to enable students to build a rigorous knowledge base around specification, estimaton, and inference with generalised linear models (GLMs). Furthermore, all course contents and activities will involve a large degree of statistical computing via R and RStudio/Posit, encouraging students to develop good practical habits and an effectie statistical model-building workflow as they apply GLMs to a diverse range of real-life datasets.

All assessments are partly designed to partly replicate real-world research scenarios, positioning students as statistical consultants tasked with analysing an array of non-continuous data using (G)LMs. One or more assessments will also involve collaboration with other students to collect and analyze non-continuous data using GLMs and extensions thereof, fostering peer-assisted learning and cultivating a research-oriented mindset.

Examination Material or equipment

A single A4 (two-sided) page of notes may be permitted, although details of this will be made available on the Canvas course page no later than Monday 5pm Canberra time of Week 10.

Dictionaries and calculators will not be permitted into the final exam. 

Required Resources

Class material, including detailed lecture slides, lecture recordings, tutorials, assignments, and other relevant material, will be made available on the Canvas course page. Students should regularly visit the course page for updates and new uploads.

In the course we will make use of the R software and its RStudio interface, which are both freely available online. R must be downloaded and installed prior to downloading and installing RStudio. It is recommended to install the latest versions of these.

There are no other prescribed resources for this course: the course material made available on Canvas course page are designed to be self-contained and of sufficient detail to satisfy all learning outcomes of the course. A list of suggested references for optional further reading will be provided on the Canvas course page.

Staff Feedback

Students will be given feedback (through both verbal and written comments) via one or more of the following forms in this course:

  • to the whole class during lectures;
  • within tutorials;
  • individually during consultation hours;
  • through written comments in the marked assessments available via the Canvas course page.

Student Feedback

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). Feedback can also be provided to Course Conveners and teachers via the Student Experience of Learning & Teaching (SELT) feedback program. SELT surveys are confidential and also provide the Colleges and ANU Executive with opportunities to recognise excellent teaching, and opportunities for improvement.

Class Schedule

Week/Session Summary of Activities Assessment
1 Introduction and Revision of Linear Models (LMs)
2 Analysis of (co)variance
3 Binary regression & Poisson regression Assignment 1 open
4 Poisson regression & introduction to Generalised linear models (GLMs)
5 Introduction to GLMs Assignment 1 due
6 Inference and variable selection in GLMs part I
7 Inference and variable selection in GLMs II
8 Model Diagnostics Assignment 2 open
9 Binomial regression & Multicategory regression 
10 Multicategory regression & Over- and under-dispersion
11 Over- and under-dispersion, other count models Assignment 2 due
12 Other count models, and beyond GLMs (final topics to be confirmed)

Tutorial Registration

Tutorials will be held weekly (starting from week 2). ANU utilises MyTimetable to enable students to view the timetable for their enrolled courses, browse it, and self-allocate to small teaching activities/tutorials, so they can better plan their time. Tutorial registration will be available two weeks before the beginning of the semester and will close at the end of week 1. More details can be found on the Timetable webpage: https://www.anu.edu.au/students/program-administration/timetabling

Assessment Summary

Assessment task Value Due Date Return of assessment Learning Outcomes
Assignment 1 20 % 26/03/2026 30/03/2026 1,2
Assignment 2 30 % 21/05/2026 26/06/2026 1,2,3
Final Exam 50 % 04/06/2026 02/07/2026 1,2,3

* If the Due Date and Return of Assessment date are blank, see the Assessment Tab for specific Assessment Task details

Policies

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 Integrity Rule before the commencement of their course. Other key policies and guidelines include:

Assessment Requirements

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 Skills website. 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 ‘Canvas’ 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.

Participation

Course content delivery will take the form of weekly in-person lectures (recorded and available via Echo360 on the Cavas course page) in weeks 1-12, and weekly in-person tutorials in weeks 2-12, all delivered on campus. Attendance at lectures and tutorials, while not compulsory, is expected in line with the “Code of Practice for Teaching and Learning” clause 2 paragraph (b).


Comments on the Canvas page discussion board to be monitored by both the course convener and tutor.

Examination(s)

The final exam will be a centrally invigilated, 2-hour closed-book exam (with 15 min reading time), held in-person during the exam period. Details of the final exam will be made available on the Canvas course page no later than Monday 5pm Canberra time of Week 10. Dictionaries and calculators will not be permitted into the final exam. 

As a centrally scheduled and invigilated exam, Examinations, Graduations & Prizes will timetable the final exam in a timely manner prior to the examination period. Please check ANU Timetabling for further information in due course.

Assessment Task 1

Value: 20 %
Due Date: 26/03/2026
Return of Assessment: 30/03/2026
Learning Outcomes: 1,2

Assignment 1

The assignment will assess up to Weeks 1-4 of the course. It must be completed individually, and is not redeemable. This assignment is designed to help you reinforce the ideas and concepts behinds the application of multiple linear regression models as well as the basics of generalised linear models. This assignment is expected to involve a combination of mathematical/conceptual questions, and a project component involving data wrangling, visualization, and (G)LM analysis in R and RStudio.


A tentative marking rubric for the project component of Assignment 1 is provided below, noting there will be varying levels of judgement calls and marker's discretion where grading project reports, and noting that many aspects of the rubric are overlapping i.e., a strong/weak statistical analysis and modeling workflow will tie into strong data visualisation and exploration along with strong interpretation of results.


Note as part of this assessment, you will be graded on aspects such as clarity and concisement of report presentaton, and choices and accuracy of data visualisation. Such aspects will not be covered in this course to any substantive degree, and it is the student's responsibility to learn and attempt these as best as possible.



Submission format: Online via the appropriate activity on the Canvas course page

Availability: By 5:00 pm Canberra time, Monday of Week 3

Due date: 5:00 pm Canberra Time, Thursday of Week 5

Value: 20%

Rubric

High Distinction (4-5)Distinction (3-4)Credit (2-3)Pass (1-2)Fail (0-1)

Data visualisation and exploration

Project report has all 4 qualities:

  • Accurate and relevant choices of visualisations suitable for the data type and the conclusions being drawn. All visualisations must be well-labeled for clear interpretation;
  • As appropriate, includes accurate and relevant tabulations/summary statistics reflecting initial exploration of the response/covariate and potential relationships;
  • Relevant exploratory data analysis focused on generating an initial understanding of the responses and/or predictors;
  • Connected to the above, addresses and identify potential outliers, influential observations or other potential data quality issues.

Project report has 3 of the 4 qualities:

  • Accurate and relevant choices of visualisations suitable for the data type and the conclusions being drawn. All visualisations must be well-labeled for clear interpretation;
  • As appropriate, includes accurate and relevant tabulations/summary statistics reflecting initial exploration of the response/covariate and potential relationships;
  • Relevant exploratory data analysis focused on generating an initial understanding of the responses and/or predictors;
  • Connected to the above, addresses and identify potential outliers, influential observations or other potential data quality issues.

Project report has 2 of the 4 qualities:

  • Accurate and relevant choices of visualisations suitable for the data type and the conclusions being drawn. All visualisations must be well-labeled for clear interpretation;
  • As appropriate, includes accurate and relevant tabulations/summary statistics reflecting initial exploration of the response/covariate and potential relationships;
  • Relevant exploratory data analysis focused on generating an initial understanding of the responses and/or predictors;
  • Connected to the above, addresses and identify potential outliers, influential observations or other potential data quality issues.

Project report has 1 of the 4 qualities:

  • Accurate and relevant choices of visualisations suitable for the data type and the conclusions being drawn. All visualisations must be well-labeled for clear interpretation;
  • As appropriate, includes accurate and relevant tabulations/summary statistics reflecting initial exploration of the response/covariate and potential relationships;
  • Relevant exploratory data analysis focused on generating an initial understanding of the responses and/or predictors;
  • Connected to the above, addresses and identify potential outliers, influential observations or other potential data quality issues.

Project report has none of the 4 qualities:

  • Accurate and relevant choices of visualisations suitable for the data type and the conclusions being drawn. All visualisations must be well-labeled for clear interpretation;
  • As appropriate, includes accurate and relevant tabulations/summary statistics reflecting initial exploration of the response/covariate and potential relationships;
  • Relevant exploratory data analysis focused on generating an initial understanding of the responses and/or predictors;
  • Connected to the above, addresses and identify potential outliers, influential observations or other potential data quality issues.

Statistical analysis and model building workflow


Project report has all 4 qualities:

  • A correct and close to complete statistical analysis used to support all interpretations and conclusions. The analysis should be relevant to the research questions and data type;
  • Provides correct justifications for steps taken in the model-building workflow e.g., includes explaining the certain covariates being included, choice of statistical model, data transformations;
  • As appropriate and connected to the above, undertakes relevant and correct model diagnostics to assess modeling assumptions;
  • As appropriate and connected to the above, applies appropriate model selection techniques to choose a model balancing statistical criteria with domain knowledge.

Project report has 3 of 4 qualities:

  • A correct and close to complete statistical analysis used to support all interpretations and conclusions. The analysis should be relevant to the research questions and data type;
  • Provides correct justifications for steps taken in the model-building workflow e.g., includes explaining the certain covariates being included, choice of statistical model, data transformations;
  • As appropriate and connected to the above, undertakes relevant and correct model diagnostics to assess modeling assumptions;
  • As appropriate and connected to the above, applies appropriate model selection techniques to choose a model balancing statistical criteria with domain knowledge.

Project report has 2 of the 4 qualities:

  • A correct and close to complete statistical analysis used to support all interpretations and conclusions. The analysis should be relevant to the research questions and data type;
  • Provides correct justifications for steps taken in the model-building workflow e.g., includes explaining the certain covariates being included, choice of statistical model, data transformations;
  • As appropriate and connected to the above, undertakes relevant and correct model diagnostics to assess modeling assumptions;
  • As appropriate and connected to the above, applies appropriate model selection techniques to choose a model balancing statistical criteria with domain knowledge.

Project report has 1 of the 4 qualities:

  • A correct and close to complete statistical analysis used to support all interpretations and conclusions. The analysis should be relevant to the research questions and data type;
  • Provides correct justifications for steps taken in the model-building workflow e.g., includes explaining the certain covariates being included, choice of statistical model, data transformations;
  • As appropriate and connected to the above, undertakes relevant and correct model diagnostics to assess modeling assumptions;
  • As appropriate and connected to the above, applies appropriate model selection techniques to choose a model balancing statistical criteria with domain knowledge.

Project report has none of the 4 qualities:

  • A correct and close to complete statistical analysis used to support all interpretations and conclusions. The analysis should be relevant to the research questions and data type;
  • Provides correct justifications for steps taken in the model-building workflow e.g., includes explaining the certain covariates being included, choice of statistical model, data transformations;
  • As appropriate and connected to the above, undertakes relevant and correct model diagnostics to assess modeling assumptions;
  • As appropriate and connected to the above, applies appropriate model selection techniques to choose a model balancing statistical criteria with domain knowledge.

Interpretation of results, presentation of conclusions, limitations/challenges, and references/acknowledgements.

Project report has all 4 qualities:

  • Substantive and correct interpretation of modeling results;
  • A succinct summary of the main conclusions of the analysis;
  • Discussion around limitations of analysis, modeling assumptions made, and what more could have been done;
  • Proper references and acknowledgements as appropriate e.g., of websites, dicussion with peers, use of large language models and generative AI.

Project report has 3 of the 4 qualities:

  • Substantive and correct interpretation of modeling results;
  • A succinct summary of the main conclusions of the analysis;
  • Discussion around limitations of analysis, modeling assumptions made, and what more could have been done;
  • Proper references and acknowledgements as appropriate e.g., of websites, dicussion with peers, use of large language models and generative AI.

Project report has 2 of the 4 qualities:

  • Substantive and correct interpretation of modeling results;
  • A succinct summary of the main conclusions of the analysis;
  • Discussion around limitations of analysis, modeling assumptions made, and what more could have been done;
  • Proper references and acknowledgements as appropriate e.g., of websites, dicussion with peers, use of large language models and generative AI.

Project report has 1 of the 4 qualities:

  • Substantive and correct interpretation of modeling results;
  • A succinct summary of the main conclusions of the analysis;
  • Discussion around limitations of analysis, modeling assumptions made, and what more could have been done;
  • Proper references and acknowledgements as appropriate e.g., of websites, dicussion with peers, use of large language models and generative AI.

Project report has none of the 4 qualities:

  • Substantive and correct interpretation of modeling results;
  • A succinct summary of the main conclusions of the analysis;
  • Discussion around limitations of analysis, modeling assumptions made, and what more could have been done;
  • Proper references and acknowledgements as appropriate e.g., of websites, dicussion with peers, use of large language models and generative AI.

Overall presentation

Project report has all 4 qualities:

  • Visually appealing and consistent formatting;
  • Clear and concise writing style, using statistical jargon as appropriate;
  • Well-written with little to no grammatical and spelling errors;
  • Clear structure including statement of research question/s.

Project report has 3 of the 4 qualities:

  • Visually appealing and consistent formatting;
  • Clear and concise writing style, using statistical jargon as appropriate;
  • Well-written with little to no grammatical and spelling errors;
  • Clear structure including statement of research question/s.

Project report has 2 of the 4 qualities:

  • Visually appealing and consistent formatting;
  • Clear and concise writing style, using statistical jargon as appropriate;
  • Well-written with little to no grammatical and spelling errors;
  • Clear structure including statement of research question/s.

Project report has 1 of the 4 qualities:

  • Visually appealing and consistent formatting;
  • Clear and concise writing style, using statistical jargon as appropriate;
  • Well-written with little to no grammatical and spelling errors;
  • Clear structure including statement of research question/s.

Project report has none of the 4 qualties:

  • Visually appealing and consistent formatting;
  • Clear and concise writing style, using statistical jargon as appropriate;
  • Well-written with little to no grammatical and spelling errors;
  • Clear structure including statement of research question/s.

Assessment Task 2

Value: 30 %
Due Date: 21/05/2026
Return of Assessment: 26/06/2026
Learning Outcomes: 1,2,3

Assignment 2

The assignment will assess Weeks 1-10 of the course. It will involve a combinaton of individual work, along with a component that will be a group project. The assignment is not redeemable. This assignment is designed to help you understand and reinforce many of the elements of specifying, fitting, and performing inference using generalised linear models models, and is expected to involve a combination of mathematical/conceptual questions, while the group project component will involve data wrangling, visualization, and (G)LM analysis in R and RStudio.


A tentative marking rubric for the project component of Assignment 1 is provided below, noting there will be varying levels of judgement calls and marker's discretion where grading project reports, and noting that many aspects of the rubric are overlapping i.e., a strong/weak data collection and description will tie into strong data visualisation and exploration.


The group project component of the assignment will be designed such that all students will be provided with the opportunity to demonstrate their skills as individuals. Information will be made available on the Canvas course page on where to go for assistance if group dynamics become difficult.


Note as part of this assessment, you will be graded on aspects such as mode of study design and data collection, clarity and concisement of report presentaton, and choices and accuracy of data visualisation. Such aspects will not be covered in this course to any substantive degree, and it is the student's responsibility to learn and attempt these as best as possible.



Submission format: Online via the appropriate activity on the Canvas course page

Availability: By 5:00 pm Canberra time, Monday of Week 8

Due date: 5:00 pm Canberra Time, Thursday of Week 11

Value: 30% (~15% group component and ~15% individual component)

Rubric

High Distinction (4-5)Distinction (3-4)Credit (2-3)Pass (1-2)Fail (0-1)

Data collection and description

Project report has all 4 qualities:

  • Detailed descriptions of how the data were collected e.g., the data source, time frame, and so on.
  • Demonstrable evidence of thought given into the study design, acknowledging potential limitations;
  • A concise summary of all variables recorded and its designated role in the modeling to follow e.g., response variable, covariates, potential confounders;
  • Connected to the above, critical information around variable type (e.g., binary/ordinal factor, continuous, range of values it can take) that is relevant to the research question of interest.

Project report has 3 of the 4 qualities:

  • Detailed descriptions of how the data were collected e.g., the data source, time frame, and so on.
  • Demonstrable evidence of thought given into the study design, acknowledging potential limitations;
  • A concise summary of all variables recorded and its designated role in the modeling to follow e.g., response variable, covariates, potential confounders;
  • Connected to the above, critical information around variable type (e.g., binary/ordinal factor, continuous, range of values it can take) that is relevant to the research question of interest.

Project report has 2 of the 4 qualities:

  • Detailed descriptions of how the data were collected e.g., the data source, time frame, and so on.
  • Demonstrable evidence of thought given into the study design, acknowledging potential limitations;
  • A concise summary of all variables recorded and its designated role in the modeling to follow e.g., response variable, covariates, potential confounders;
  • Connected to the above, critical information around variable type (e.g., binary/ordinal factor, continuous, range of values it can take) that is relevant to the research question of interest.

Project report has 1 of the 4 qualities:

  • Detailed descriptions of how the data were collected e.g., the data source, time frame, and so on.
  • Demonstrable evidence of thought given into the study design, acknowledging potential limitations;
  • A concise summary of all variables recorded and its designated role in the modeling to follow e.g., response variable, covariates, potential confounders;
  • Connected to the above, critical information around variable type (e.g., binary/ordinal factor, continuous, range of values it can take) that is relevant to the research question of interest.

Project report has none of the 4 qualities:

  • Detailed descriptions of how the data were collected e.g., the data source, time frame, and so on.
  • Demonstrable evidence of thought given into the study design, acknowledging potential limitations;
  • A concise summary of all variables recorded and its designated role in the modeling to follow e.g., response variable, covariates, potential confounders;
  • Connected to the above, critical information around variable type (e.g., binary/ordinal factor, continuous, range of values it can take) that is relevant to the research question of interest.

Data wrangling, visualisation, and exploration

Project report has all 4 qualities:

  • Accurate and relevant choices of visualisations suitable for the data type and the conclusions being drawn. All visualisations must be well-labeled for clear interpretation;
  • As appropriate, includes accurate and relevant tabulations/summary statistics reflecting initial exploration of the response/covariate and potential relationships;
  • Relevant data wrangling and exploratory data analysis focused on generating an initial understanding of the responses and/or predictors;
  • Connected to the above, addresses and identify potential outliers, influential observations or other potential data quality issues.

Project report has 3 of the 4 qualities:

  • Accurate and relevant choices of visualisations suitable for the data type and the conclusions being drawn. All visualisations must be well-labeled for clear interpretation;
  • As appropriate, includes accurate and relevant tabulations/summary statistics reflecting initial exploration of the response/covariate and potential relationships;
  • Relevant data wrangling and exploratory data analysis focused on generating an initial understanding of the responses and/or predictors;
  • Connected to the above, addresses and identify potential outliers, influential observations or other potential data quality issues.

Project report has 2 of the 4 qualities:

  • Accurate and relevant choices of visualisations suitable for the data type and the conclusions being drawn. All visualisations must be well-labeled for clear interpretation;
  • As appropriate, includes accurate and relevant tabulations/summary statistics reflecting initial exploration of the response/covariate and potential relationships;
  • Relevant data wrangling and exploratory data analysis focused on generating an initial understanding of the responses and/or predictors;
  • Connected to the above, addresses and identify potential outliers, influential observations or other potential data quality issues.

Project report has 1 of the 4 qualities:

  • Accurate and relevant choices of visualisations suitable for the data type and the conclusions being drawn. All visualisations must be well-labeled for clear interpretation;
  • As appropriate, includes accurate and relevant tabulations/summary statistics reflecting initial exploration of the response/covariate and potential relationships;
  • Relevant data wrangling and exploratory data analysis focused on generating an initial understanding of the responses and/or predictors;
  • Connected to the above, addresses and identify potential outliers, influential observations or other potential data quality issues.

Project report has none of the 4 qualities:

  • Accurate and relevant choices of visualisations suitable for the data type and the conclusions being drawn. All visualisations must be well-labeled for clear interpretation;
  • As appropriate, includes accurate and relevant tabulations/summary statistics reflecting initial exploration of the response/covariate and potential relationships;
  • Relevant data wrangling and exploratory data analysis focused on generating an initial understanding of the responses and/or predictors;
  • Connected to the above, addresses and identify potential outliers, influential observations or other potential data quality issues.

Statistical analysis and model building workflow

Project report has all 4 qualities:

  • A correct and close to complete statistical analysis used to support all interpretations and conclusions. The analysis should be relevant to the research questions and data type;
  • Provides correct justifications for steps taken in the model-building workflow e.g., includes explaining the certain covariates being included, choice of statistical model, data transformations;
  • As appropriate and connected to the above, undertakes relevant and correct model diagnostics to assess modeling assumptions;
  • As appropriate and connected to the above, applies appropriate model selection techniques to choose a model balancing statistical criteria with domain knowledge.

Project report has 3 of the 4 qualities:

  • A correct and close to complete statistical analysis used to support all interpretations and conclusions. The analysis should be relevant to the research questions and data type;
  • Provides correct justifications for steps taken in the model-building workflow e.g., includes explaining the certain covariates being included, choice of statistical model, data transformations;
  • As appropriate and connected to the above, undertakes relevant and correct model diagnostics to assess modeling assumptions;
  • As appropriate and connected to the above, applies appropriate model selection techniques to choose a model balancing statistical criteria with domain knowledge.

Project report has 2 of the 4 qualities:

  • A correct and close to complete statistical analysis used to support all interpretations and conclusions. The analysis should be relevant to the research questions and data type;
  • Provides correct justifications for steps taken in the model-building workflow e.g., includes explaining the certain covariates being included, choice of statistical model, data transformations;
  • As appropriate and connected to the above, undertakes relevant and correct model diagnostics to assess modeling assumptions;
  • As appropriate and connected to the above, applies appropriate model selection techniques to choose a model balancing statistical criteria with domain knowledge.

Project report has 1 of the 4 qualities:

  • A correct and close to complete statistical analysis used to support all interpretations and conclusions. The analysis should be relevant to the research questions and data type;
  • Provides correct justifications for steps taken in the model-building workflow e.g., includes explaining the certain covariates being included, choice of statistical model, data transformations;
  • As appropriate and connected to the above, undertakes relevant and correct model diagnostics to assess modeling assumptions;
  • As appropriate and connected to the above, applies appropriate model selection techniques to choose a model balancing statistical criteria with domain knowledge.

Project report has none of the 4 qualities:

  • A correct and close to complete statistical analysis used to support all interpretations and conclusions. The analysis should be relevant to the research questions and data type;
  • Provides correct justifications for steps taken in the model-building workflow e.g., includes explaining the certain covariates being included, choice of statistical model, data transformations;
  • As appropriate and connected to the above, undertakes relevant and correct model diagnostics to assess modeling assumptions;
  • As appropriate and connected to the above, applies appropriate model selection techniques to choose a model balancing statistical criteria with domain knowledge.

Interpretation of results, presentation of conclusions, limitations/challenges, and references/acknowledgements.

Project report has all 4 qualities:

  • Substantive and correct interpretation of modeling results;
  • A succinct summary of the main conclusions of the analysis;
  • Discussion around limitations of analysis, modeling assumptions made, and what more could have been done both in analysis and in data collection/experimental design;
  • Proper references and acknowledgements as appropriate e.g., of websites, dicussion with peers, use of large language models and generative AI, as well as including statements of contributions of each individual to the project.

Project report has 3 of the 4 qualities:

  • Substantive and correct interpretation of modeling results;
  • A succinct summary of the main conclusions of the analysis;
  • Discussion around limitations of analysis, modeling assumptions made, and what more could have been done both in analysis and in data collection/experimental design;
  • Proper references and acknowledgements as appropriate e.g., of websites, dicussion with peers, use of large language models and generative AI, as well as including statements of contributions of each individual to the project.

Project report has 2 of the 4 qualities:

  • Substantive and correct interpretation of modeling results;
  • A succinct summary of the main conclusions of the analysis;
  • Discussion around limitations of analysis, modeling assumptions made, and what more could have been done both in analysis and in data collection/experimental design;
  • Proper references and acknowledgements as appropriate e.g., of websites, dicussion with peers, use of large language models and generative AI, as well as including statements of contributions of each individual to the project.

Project report has 1 of the 4 qualities:

  • Substantive and correct interpretation of modeling results;
  • A succinct summary of the main conclusions of the analysis;
  • Discussion around limitations of analysis, modeling assumptions made, and what more could have been done both in analysis and in data collection/experimental design;
  • Proper references and acknowledgements as appropriate e.g., of websites, dicussion with peers, use of large language models and generative AI, as well as including statements of contributions of each individual to the project.

Project report has none of the 4 qualities:

  • Substantive and correct interpretation of modeling results;
  • A succinct summary of the main conclusions of the analysis;
  • Discussion around limitations of analysis, modeling assumptions made, and what more could have been done both in analysis and in data collection/experimental design;
  • Proper references and acknowledgements as appropriate e.g., of websites, dicussion with peers, use of large language models and generative AI, as well as including statements of contributions of each individual to the project.

Overall presentation

Project report has all 4 qualities:

  • Visually appealing and consistent formatting;
  • Clear and concise writing style, using statistical jargon as appropriate;
  • Well-written with little to no grammatical and spelling errors;
  • Clear structure including statement of research question/s.

Project report has 3 of the 4 qualities:

  • Visually appealing and consistent formatting;
  • Clear and concise writing style, using statistical jargon as appropriate;
  • Well-written with little to no grammatical and spelling errors;
  • Clear structure including statement of research question/s.

Project report has 2 of the 4 qualities:

  • Visually appealing and consistent formatting;
  • Clear and concise writing style, using statistical jargon as appropriate;
  • Well-written with little to no grammatical and spelling errors;
  • Clear structure including statement of research question/s.

Project report has 1 of the 4 qualities:

  • Visually appealing and consistent formatting;
  • Clear and concise writing style, using statistical jargon as appropriate;
  • Well-written with little to no grammatical and spelling errors;
  • Clear structure including statement of research question/s.

Project report has none of the 4 qualities:

  • Visually appealing and consistent formatting;
  • Clear and concise writing style, using statistical jargon as appropriate;
  • Well-written with little to no grammatical and spelling errors;
  • Clear structure including statement of research question/s.

Assessment Task 3

Value: 50 %
Due Date: 04/06/2026
Return of Assessment: 02/07/2026
Learning Outcomes: 1,2,3

Final Exam

The final exam will be a centrally invigilated, 2-hour closed-book exam (with 15 min reading time), held in-person during the exam period. Details of the final exam will be made available on the Canvas course page no later than Monday 5pm Canberra time of Week 10. Dictionaries and calculators will not be permitted into the final exam. 


As a centrally scheduled and invigilated exam, Examinations, Graduations & Prizes will timetable the final exam in a timely manner prior to the examination period. Please check ANU Timetabling for further information in due course.


The final exam must be completed individually, is not redeemable, and is not a hurdle assessment. The exam will assess all weeks of the course, and will consist of up to four questions, each with multiple parts.


Value: 50%

Academic Integrity

Academic integrity is a core part of the ANU culture as a community of scholars. The University’s students are an integral part of that community. The academic integrity principle commits all students to engage in academic work in ways that are consistent with, and actively support, academic integrity, and to uphold this commitment by behaving honestly, responsibly and ethically, and with respect and fairness, in scholarly practice.


The University expects all staff and students to be familiar with the academic integrity principle, the Academic Integrity Rule 2021, the Policy: Student Academic Integrity and Procedure: Student Academic Integrity, and to uphold high standards of academic integrity to ensure the quality and value of our qualifications.


The Academic Integrity Rule 2021 is a legal document that the University uses to promote academic integrity, and manage breaches of the academic integrity principle. The Policy and Procedure support the Rule by outlining overarching principles, responsibilities and processes. The Academic Integrity Rule 2021 commences on 1 December 2021 and applies to courses commencing on or after that date, as well as to research conduct occurring on or after that date. Prior to this, the Academic Misconduct Rule 2015 applies.

 

The University commits to assisting all students to understand how to engage in academic work in ways that are consistent with, and actively support academic integrity. All coursework students must complete the online Academic Integrity Module (Epigeum), and Higher Degree Research (HDR) students are required to complete research integrity training. The Academic Integrity website provides information about services available to assist students with their assignments, examinations and other learning activities, as well as understanding and upholding academic integrity.

Online Submission

You will be required to sign a declaration, in the form of a cover sheet, as part of the submission of Assignments 1 and 2. Please keep a copy of the assignment and signed cover sheets, for your record. Any student identified, either during the current semester or in retrospect, as having used ghost writing services as part of the Assignments 1 and 2 will be investigated under the University’s Academic Misconduct Rule.

Each assignment must be submitted as one or more electronic files, preferably a combination of pdf and R software scripts (as appropriate), to the appropriate activity on the Canvas course page. If submitting handwritten mathematical derivations, ensure that your handwriting is legible, appropriate working out is shown, and then scan the derivations in e.g., by using your smartphone camera and Microsoft Lens.

More information about online submissions of each assessments task will be given on the course Canvas course page, in a timely manner,

Hardcopy Submission

There is no hardcopy submission in the course.

Late Submission

No submission of any assessment tasks is permitted. If an assessment task is submitted after the due date, a mark of zero will be awarded.

Referencing Requirements

The Academic Skills website has information to assist you with your writing and assessments. The website includes information about Academic Integrity including referencing requirements for different disciplines. There is also information on Plagiarism and different ways to use source material. Any use of artificial intelligence must be properly referenced. Failure to properly cite use of Generative AI will be considered a breach of academic integrity.

Returning Assignments

Graded assignments along with feedback should be made available via the relevant activity on the course Canvas site at an appropriate time.

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

Resubmission of assessments is not allowed under any circumstance.

Privacy Notice

The ANU has made a number of third party, online, databases available for students to use. Use of each online database is conditional on student end users first agreeing to the database licensor’s terms of service and/or privacy policy. Students should read these carefully. In some cases student end users will be required to register an account with the database licensor and submit personal information, including their: first name; last name; ANU email address; and other information.
In cases where student end users are asked to submit ‘content’ to a database, such as an assignment or short answers, the database licensor may only use the student’s ‘content’ in accordance with the terms of service – including any (copyright) licence the student grants to the database licensor. Any personal information or content a student submits may be stored by the licensor, potentially offshore, and will be used to process the database service in accordance with the licensors terms of service and/or privacy policy.
If any student chooses not to agree to the database licensor’s terms of service or privacy policy, the student will not be able to access and use the database. In these circumstances students should contact their lecturer to enquire about alternative arrangements that are available.

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 Accessibility 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 supports you make your own decisions about how you learn and manage your workload.
  • ANU Counselling promotes, supports and enhances mental health and wellbeing within the University student community.
  • ANUSA supports and represents all ANU students

Convener

Details Consulting Hours
AsPr Francis Hui
02 6125 1976
francis.hui@anu.edu.au

Research Interests


correlated data analysis; ecological statistics; mixed effects modeling; semiparametric regression; variable selection

AsPr Francis Hui

Wednesday 15:00 16:00
Wednesday 15:00 16:00
By Appointment

Instructor

Details Consulting Hours
AsPr Francis Hui
02 6125 1976
francis.hui@anu.edu.au

Research Interests

AsPr Francis Hui

Wednesday 15:00 16:00
Wednesday 15:00 16:00
By Appointment

Responsible Officer: Registrar, Student Administration / Page Contact: Website Administrator / Frequently Asked Questions