Outline

Overview

This unit builds on introductory 1st year statistics units and is targeted towards students in the agricultural, life and environmental sciences. It consists of two parts and presents, in an applied manner, the statistical methods that students need to know for further study and their future careers. In the first part the focus is on designed studies including both surveys and formal experimental designs. Students will learn how to analyse and interpret datasets collected from designs from more than 2 treatment levels, multiple factors and different blocking designs. In the second part the focus is on finding patterns in data. In this part the students will learn to model relationships between response and predictor variables using regression, and find patterns in datasets with many variables using principal components analysis and clustering. This part provides the foundation for the analysis of big data. In the practicals the emphasis is on applying theory to analysing real datasets using the statistical software package R. A key feature of the unit is using R to develop coding skills that have become essential in science for processing and analysing datasets of ever-increasing size.

Unit details and rules

Academic unit	Life and Environmental Sciences Academic Operations
Credit points	6
Prerequisites ?	[6cp from (ENVX1001 or ENVX1002 or BIOM1003 or MATH1011 or MATH1015 or DATA1001 or DATA1901)] OR [3cp from (MATH1XX1 or MATH1906 or MATH1XX3 or MATH1907) and an additional 3cp from (MATH1XX5)]
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Aaron Greenville, aaron.greenville@sydney.edu.au
Lecturer(s)	Floris Van Ogtrop, floris.vanogtrop@sydney.edu.au
	Thomas Bishop, thomas.bishop@sydney.edu.au
	Januar Harianto, januar.harianto@sydney.edu.au
	Mathew Crowther, mathew.crowther@sydney.edu.au
	Aaron Greenville, aaron.greenville@sydney.edu.au

Type	Description	Weight	Due	Length
Supervised exam ?	Final exam Timed MCQ + Short answer exam	45%	Formal exam period	2 hours
Outcomes assessed:
Supervised test ?	Mid semester exam Computer based test held during your tutorial class.	20%	Week 08	1 hour
Outcomes assessed:
Assignment	Analysing experimental data Written report	15%	Week 10 Due date: 05 May 2023 at 23:59	See canvas for details
Outcomes assessed:
Presentation	Presentation - Topics 7-12 Powerpoint Audio presentation + individual reflection	15%	Week 13	5 minutes. See Canvas for more details
Outcomes assessed:
Online task	Online quizzes Quizzes are completed in Canvas	5%	Weekly	120 minutes
Outcomes assessed:
= group assignment ?

Assessment summary

Topics 1-6: There will be 1 individual report. The report details will be released via Canvas.
Topics 7-12: There will be a group presentation involving a modelling exercise integrating all Topics in Module 2. Groups will collect datasets.The report details will be released via Canvas
Online quizzes: There will be weekly online multiple choice quizzes that will be assessed during the semester. The questions will be taken from the output in the practical classes and the analysis of example datasets. The questions should be seen as practice for the final exam and multiple attempts will be allowed. They will be released on Friday at 5pm and be due by the following Wednesday 11pm. Two hours will be given to complete the online quizzes.
Mid-semester exam: There will be a mid semester test, which is designed to test you abilities in analysing data using R and also interpretting R output. In-semester test will be run in week 08 during your tutorial class. More details will be provided in class.
Final exam: will cover key concepts learnt throughout semester. Note: This assessment is compulsory and failure to attend, attempt, or submit will result in the award of an AF grade. If a second replacement exam is required, this exam may be delivered via an alternative assessment method, such as a viva voce (oral exam). The alternative assessment will meet the same learning outcomes as the original exam. The format of the alternative assessment will be determined by the unit coordinator.

Detailed information for each assessment can be found on Canvas.

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.

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
Week 01	Introduction	Lecture and tutorial (5 hr)
Week 02	Sampling designs	Lecture and tutorial (5 hr)
Week 03	1-way ANOVA	Lecture and tutorial (5 hr)
Week 04	Residual diagnostics & post hoc tests	Lecture and tutorial (5 hr)
Week 05	Experimental design	Lecture and tutorial (5 hr)
Week 06	ANOVA with blocking	Lecture and tutorial (5 hr)
Week 07	Regression modelling	Lecture and tutorial (5 hr)
Week 08	Regression model development	Lecture and tutorial (5 hr)
Week 09	Regression model assessment	Lecture and tutorial (5 hr)
Week 10	Principle component analysis	Lecture and tutorial (5 hr)
Week 11	Clustering	Lecture and tutorial (5 hr)
Week 12	Multidimensional scaling	Lecture and tutorial (5 hr)
Week 13	Revision	Lecture and tutorial (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

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. demonstrate proficiency in designing sample schemes and analysing data from them using using R
LO2. describe and identify the basic features of an experimental design; replicate, treatment structure and blocking structure
LO3. demonstrate proficiency in the use or the statistical programming language R to apply an ANOVA and fit regression models to experimental data
LO4. demonstrate proficiency in the use or the statistical programming language R to use multivariate methods to find patterns in data
LO5. interpret the output and understand conceptually how its derived of a regression, ANOVA and multivariate analysis that have been calculated by R
LO6. write statistical and modelling results as part of a scientific report
LO7. appraise the validity of statistical analyses used publications.

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.

Assessment 1 and 2 have increased in weight by 5% and the final exam has reduced in weight by 10% to increase the use of authentic assessments.

Additional information

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
Follow safety instructions in your manual and posted in laboratories
In case of fire, follow instructions posted outside the laboratory door

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

Work, health and safety

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect

Current students

ENVX2001: Applied Statistical Methods

Semester 1, 2023 [Normal day] - Camperdown/Darlington, Sydney

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

Work, health and safety

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect