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Unit of study_

BSTA5210: Regression Models for Biostatistics 1 (RM1)

Semester 1, 2022 [Online] - Camperdown/Darlington, Sydney

The aim of this unit is to lay the foundation of biostatistical modelling to analyse data from randomised or observational studies. These skills are essential for biostatistics in practice and will be used by students for the remainder of their Master of Biostatistics studies. This unit will introduce the motivation for different regression analyses and how to choose an appropriate modelling strategy. This unit will teach how to use linear regression to analyse continuous outcomes and logistic regression for binary outcomes. Emphasis will be placed on interpretation of results and checking the model assumptions. Stata and R software will be used to apply the methods to real study datasets.

Unit details and rules

Unit code BSTA5210
Academic unit Public Health
Credit points 6
BSTA5007 or BSTA5008
(BSTA5100 or (BSTA5001 and BSTA5023)) and (BSTA5011 or PUBH5010 or CEPI5100)
Assumed knowledge


Available to study abroad and exchange students


Teaching staff

Coordinator Tim Schlub,
Type Description Weight Due Length
Assignment Assignment 1
Statistical analysis and written report
30% Week 06 Approximately 8 pages
Outcomes assessed: LO1 LO2 LO4 LO5
Assignment Oral presentation
Oral presentation of statistical analysis
30% Week 10 10 minute oral presentation
Outcomes assessed: LO1 LO2 LO4 LO5
Assignment Assignment 2
Statistical analysis and written written report
40% Week 13 8 pages
Outcomes assessed: LO1 LO2 LO3 LO4 LO5

Assessment summary

There are three assessments for RM1. Assessment 1 and 3 will be a statistical anslysis followed by a written report, and may also require mathematical deriviations of statistical principals. Assessment 2 will be a statistical analysis followed by an oral presentation. Assessment 2 will also include a peer feedback component whereby you practice your oral presentation with a small group of peers.

Assessment criteria

Result code

Result name

Mark range



High distinction

85 - 100

Awarded when you demonstrate the learning outcomes for the unit at an exceptional standard, as defined by grade descriptors or exemplars outlined by your faculty or school.



75 - 84

Awarded when you demonstrate the learning outcomes for the unit at a very high standard, as defined by grade descriptors or exemplars outlined by your faculty or school.



65 - 74

Awarded when you demonstrate the learning outcomes for the unit at a good standard, as defined by grade descriptors or exemplars outlined by your faculty or school.



50 - 64

Awarded when you demonstrate the learning outcomes for the unit at an acceptable standard, as defined by grade descriptors or exemplars outlined by your faculty or school.



0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard.


Absent fail

0 - 49

When you haven’t completed all assessment tasks or met the attendance requirements.

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:

For every calendar day up to and including ten calendar days after the due date, a penalty of 5% of the maximum awardable marks will be applied to late work. For work submitted more than ten calendar days after the due date a mark of zero will be awarded. The marker may elect to, but is not required to, provide feedback on such work.

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.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

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.

WK Topic Learning activity Learning outcomes
Multiple weeks There will be an online live tutorial and discussion held in multiple weeks across the semester (on average every 2 weeks). These are held in the evenings during week nights. Online class (15 hr) LO1 LO2 LO3 LO4 LO5
Weekly Each week there will be a set of learning activities that should be completed. These learning activities vary week to week and include: Lecture recordings; readings; independent exercises; collaborative exercises; and independent investigations. Individual study (60 hr) LO1 LO2 LO3 LO4 LO5

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 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.

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

  • LO1. Explain the motivations for different regression analyses and be able to select and apply a suitable modelling approach based on the research aim
  • LO2. Analyse data using normal linear models, and be able to assess model fit and diagnostics
  • LO3. Analyse data using logistic regression models for binary data, and be able to assess model fit and diagnostics
  • LO4. Accurately interpret and manipulate mathematical equations that relate to regression analysis
  • LO5. Effectively communicate the outcomes and justification of a regression analysis

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

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

This is the first time this unit of study is running. We have designed the learning activities for this unit based on feedback from other units of study in the Master of Biostatistics program.


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.