STAT4027: Semester 2, 2025

Units STAT4027 Semester 2 2025 [Normal day]

Unit outline_

STAT4027: Advanced Statistical Modelling

Semester 2, 2025 [Normal day] - Camperdown/Darlington, Sydney

Overview

Applied Statistics fundamentally brings statistical learning to the wider world. Some data sets are complex due to the nature of their responses or predictors or have high dimensionality. These types of data pose theoretical, methodological and computational challenges that require knowledge of advanced modelling techniques, estimation methodologies and model selection skills. In this unit you will investigate contemporary model building, estimation and selection approaches for linear and generalised linear regression models. You will learn about two scenarios in model building: when an extensive search of the model space is possible; and when the dimension is large and either stepwise algorithms or regularisation techniques have to be employed to identify good models. These particular data analysis skills have been foundational in developing modern ideas about science, medicine, economics and society and in the development of new technology and should be in the toolkit of all applied statisticians. This unit will provide you with a strong foundation of critical thinking about statistical modelling and technology and give you the opportunity to engage with applications of these methods across a wide scope of applications and for research or further study.

Unit details and rules

Academic unit	Mathematics and Statistics Academic Operations
Credit points	6
Prerequisites ?	(STAT3X12 or STAT3X22 or STAT4022) and (STAT3X13 or STAT3X23 or STAT4023)
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	A three year major in statistics or equivalent including familiarity with material in DATA2X02 and STAT3X22 (applied statistics and linear models) or equivalent
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Jennifer Chan, jennifer.chan@sydney.edu.au

Assessment

The census date for this unit availability is 1 September 2025

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written exam ?	Exam Written exam	50%	Formal exam period	2 hours	AI prohibited
Outcomes assessed:
Data analysis	Assignment 1 1-2 theoretical questions in tutorial class with no AI.	10%	Week 04 Due date: 31 Aug 2025 at 11:59 Closing date: 07 Sep 2025	1 week	AI allowed
Outcomes assessed:
Data analysis	Assignment 2 1-2 theoretical questions in tutorial class with no AI.	10%	Week 08 Due date: 28 Sep 2025 at 11:59 Closing date: 05 Oct 2025	1 week	AI allowed
Outcomes assessed:
Data analysis	Assignment 3 1-2 theoretical questions in tutorial class with no AI.	10%	Week 12 Due date: 02 Nov 2025 at 11:59 Closing date: 09 Nov 2025	1 week	AI allowed
Outcomes assessed:
Case studies	Project Project and presentation	10%	Week 12 Due date: 02 Nov 2025 at 09:56 Closing date: 09 Nov 2025	1 week	AI allowed
Outcomes assessed:
Presentation	Tutorial presentation Tutorial presentation	10%	Weekly	2 weeks	AI allowed
Outcomes assessed:
= hurdle task ? = group assignment ?

Assessment summary

Assignment 1-4: test learning outcomes 1-2, 3-4, 5-6, and 7-9 respectively. Online submission and marking by hand.

Project and presentation: test learning outcomes 1-9 and applications. Online submission and marking by hand.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

No late penalty

Academic integrity

The University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity	Learning outcomes
Weekly	Estimation methods, GLM, model strategies, survivial analysis, regression for count data, regression for binary data, regression for nominal and ordinal data, regression for rate data	Lecture (24 hr)
Weekly	Topic presentation	Tutorial (11 hr)

Attendance and class requirements

No compulsory attendance of lectures.

Compulsory attendance of tutorials and presentations.

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. Apply inference methods to estimate the model parameters. These methods include maximum likelihood, expectation maximumisation, iterative re-weighted least square, M-estimation, quasi-likelihood method and generalised estimating equation.
LO2. Understand the idea of generalised linear models and exponential family to model counts, binary data, and data with a positive domain.
LO3. Apply the different modeling strategies to describe the location of a data distribution including generalised additive model, regime switching, quantile, mixture and state space model.
LO4. Analyse survival data with censoring using Kaplan Meier model and perform regression using proportional hazard with Weibull, piece-wise exponential hazard and Cox's proportional hazard models.
LO5. Perform regression for count data allowing for different levels of dispersion using mixture model and Poisson, negative binomial and generalised Poisson distributions as well as allowing for zero inflation using zero-inflated and hurdle models.
LO6. Perform regression for binary data using logit, probit and complementary log-log link functions. Understand the properties of these models and goodness-of-fit. Apply Fisher exact test to 2x2 contingency table and measure association between two binary variables.
LO7. Perform regression for multinominal data in contingency table with different experimental designs using log-linear model and two logit structures: multinominal and hierarchical. Explore the relationship with Poisson and binominal regressions. Interpret the types of assoication for different log-linear models. Study special cases of collapsing table, decomposable table, incomplete table, symmetric (and quasi-symmetric) table and marginal homogenous table.
LO8. Perform regression for ordinal data using order logit link.
LO9. Perform beta regression for rate data.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

No changes have been made since this unit was last offered.

Additional information

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

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