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Unit of study_

ENVX3002: Statistics in the Natural Sciences

This unit of study is designed to introduce students to the analysis of data they may face in their future careers, in particular data that are not well behaved. The data may be non-normal, there may be missing observations, they may be correlated in space and time or too numerous to analyse with standard models. The unit is presented in an applied context with an emphasis on correctly analysing authentic datasets, and interpreting the output. It begins with the analysis and design of experiments based on the general linear model. In the second part, students will learn about the generalisation of the general linear model to accommodate non-normal data with a particular emphasis on the binomial and Poisson distributions. In the third part linear mixed models will be introduced which provide the means to analyse datasets that do not meet the assumptions of independent and equal errors, for example data that is correlated in space and time. The units ends with an introduction to machine learning and predictive modelling. A key feature of the unit is using R to develop coding skills that are become essential in science for processing and analysing datasets of ever increasing size.


Academic unit Life and Environmental Sciences Academic Operations
Unit code ENVX3002
Unit name Statistics in the Natural Sciences
Session, year
Semester 1, 2021
Attendance mode Normal day
Location Remote
Credit points 6

Enrolment rules

ENVX2001 or STAT2X12 or BIOL2X22 or DATA2X02 or QBIO2001
Available to study abroad and exchange students


Teaching staff and contact details

Coordinator Floris Van Ogtrop,
Type Description Weight Due Length
Final exam (Record+) Type B final exam Online Exam
Final exam - canvas timed quiz
50% Formal exam period 2 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7
Assignment Report 1
Data analysis and written report
12.5% Week 05 6 pages
Outcomes assessed: LO1 LO2
Assignment Report 2
Data analysis and written report
12.5% Week 08 6 pages
Outcomes assessed: LO3 LO4
Assignment Report 3
Data analysis and written report
12.5% Week 11 6 pages
Outcomes assessed: LO5
Assignment Report 4
Data analysis and written report
12.5% Week 13 6 pages
Outcomes assessed: LO6 LO7
Type B final exam = Type B final exam ?

Detailed information for each assessment can be found on Canvas.

Assessment criteria

Result name

Mark range


High distinction

85 - 100

At HD level, a student demonstrates a flair for the subject as well as a detailed and comprehensive understanding of the unit material. A ‘High Distinction’ reflects exceptional achievement and is awarded to a student who demonstrates the ability to apply their subject knowledge and understanding to produce original solutions for novel or highly complex problems and/or comprehensive critical discussions of theoretical concepts.


75 - 84

At DI level, a student demonstrates an aptitude for the subject and a well-developed understanding of the unit material. A ‘Distinction’ reflects excellent achievement and is awarded to a student who demonstrates an ability to apply their subject knowledge and understanding of the subject to produce good solutions for challenging problems and/or a reasonably well-developed critical analysis of theoretical concepts.


65 - 74

At CR level, a student demonstrates a good command and knowledge of the unit material. A ‘Credit’ reflects solid achievement and is awarded to a student who has a broad general understanding of the unit material and can solve routine problems and/or identify and superficially discuss theoretical concepts.


50 - 64

At PS level, a student demonstrates proficiency in the unit material. A ‘Pass’ reflects satisfactory achievement and is awarded to a student who has threshold knowledge.


0 - 49

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

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:

Any assessment submitted after the due time and date (or extended due time and date) will incur a late penalty of 5% of the total marks per 24 hour period, or part thereof, late (note that this is applied to the mark gained after the submitted work is marked)

Special consideration

If you experience short-term circumstances beyond your control, such as illness, injury or misadventure or if you have essential commitments which impact your preparation or performance in an assessment, you may be eligible for special consideration or special arrangements.

Academic integrity

The Current Student website provides information on academic honesty, academic dishonesty, 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 dishonesty or plagiarism seriously.

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

WK Topic Learning activity Learning outcomes
Week 01 Linear regression models Workshop (5 hr)  
Week 02 ANOVA and its extensions Workshop (5 hr)  
Week 03 General linear model Workshop (5 hr)  
Week 04 Generalised linear models: binary data Workshop (5 hr)  
Week 05 Generalised linear models: count data Workshop (5 hr)  
Week 06 Generalised additive models Workshop (5 hr)  
Week 07 Introduction to linear mixed models Workshop (5 hr)  
Week 08 Repeated-measures analysis Workshop (5 hr)  
Week 09 Generalised linear mixed models Workshop (5 hr)  
Week 10 Machine learning Workshop (5 hr)  
Week 11 Machine learning Workshop (5 hr)  
Week 12 Machine learning Workshop (5 hr)  
Week 13 Revision Workshop (5 hr)  

Attendance and class requirements

Due to the exceptional circumstances caused by the COVID-19 pandemic, attendance requirements for this unit of study have been amended. Where online tutorials/workshops/virtual laboratories have been scheduled, students should make every effort to attend and participate at the scheduled time. Penalties will not be applied if technical issues, etc. prevent attendance at a specific online class. In that case, students should discuss the problem with the coordinator, and attend another session, if available.

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 similarities and differences between ANOVA and regression data analysis in terms of the general linear model
  • LO2. demonstrate proficiency in the use of R (and interpretation of the output) for modelling data with a general linear model
  • LO3. demonstrate proficiency in the use of R (and interpretation of the output) for modelling datasets that have non-normal distributions (binomial, Poisson) using a generalised linear model
  • LO4. demonstrate proficiency in the use of R (and interpretation of the output) for modelling univariate relationship using non-linear functions and splines
  • LO5. demonstrate proficiency in the use of R (and interpretation of the output) for modelling datasets that fail to meet the assumption of i.i.d errors (non-normality and/or correlation in space and time) by using residual maximum likelihood (REML)
  • LO6. demonstrate proficiency in the use of R (and interpretation of the output) for making predictive models using tree-based models, and for assessing the quality of the models
  • LO7. demonstrate proficiency in the use of R (and interpretation of the output) in analysing big data using modern computational intensive techniques.

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
A key piece of feedback in 2021 was trying to create more consistency between the lecturers who teach into ENVX3002. This is something that we will endeavour to do in 2021 by encouraging collaboration between those teaching the course.

Work, health and safety

We are governed by the Work Health and Safety Act 2011, Work Health and Safety Regulation 2011 and Codes of Practice. Penalties for non-compliance have increased. Everyone has a responsibility for health and safety at work. The University’s Work Health and Safety policy explains the responsibilities and expectations of workers and others, and the procedures for managing WHS risks associated with University activities.

General Laboratory Safety Rules

  • No eating or drinking is allowed in any laboratory under any circumstances 
  • A laboratory coat and closed-toe shoes are mandatory 
  • Follow safety instructions in your manual and posted in laboratories 
  • In case of fire, follow instructions posted outside the laboratory door 
  • First aid kits, eye wash and fire extinguishers are located in or immediately outside each laboratory 
  • As a precautionary measure, it is recommended that you have a current tetanus immunisation. This can be obtained from University Health Service:


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