STAT4027: Semester 2, 2021

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Units STAT4027 Semester 2 2021 [Normal day]

Unit outline_

STAT4027: Advanced Statistical Modelling

Semester 2, 2021 [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

Type	Description	Weight	Due	Length
Tutorial quiz	Final exam Final exam	40%	Formal exam period Due date: 29 Nov 2021 at 14:00 Closing date: 29 Nov 2021	2 hours
Outcomes assessed:
Skills-based evaluation	Project Report and presentation	10%	STUVAC Due date: 21 Nov 2021 at 14:00 Closing date: 21 Nov 2021	1 week
Outcomes assessed:
Assignment	Assignment 1 written answers to questions	10%	Week 04 Due date: 05 Sep 2021 at 11:59 Closing date: 19 Sep 2021	1 week
Outcomes assessed:
Assignment	Assignment 2 Written answers to questions	10%	Week 06 Due date: 19 Sep 2021 at 11:59 Closing date: 19 Sep 2021	1 week
Outcomes assessed:
Assignment	Assignment 3 Written answers to questions	10%	Week 09 Due date: 17 Oct 2021 at 11:59 Closing date: 17 Oct 2021	1 week
Outcomes assessed:
Assignment	Assignment 4 Written answers to questions	10%	Week 11 Due date: 31 Oct 2021 at 11:59 Closing date: 31 Oct 2021	1 week
Outcomes assessed:
Creative assessment / demonstration	Tutorial presentation Tutorial presentation	10%	Weekly	2 weeks
Outcomes assessed:
= 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.

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 Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

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.

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

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

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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