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Unit outline_

ENVX1002: Introduction to Statistical Methods

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

This is an introductory data science unit for students in the agricultural, life and environmental sciences. It provides the foundation for statistics and data science skills that are needed for a career in science and for further study in applied statistics and data science. The unit focuses on developing critical and statistical thinking skills for all students. It has 4 modules: exploring data, modelling data, sampling data and making decisions with data. Students will use problems and data from the physical, health, life and social sciences to develop adaptive problem-solving skills in a team setting. Taught interactively with embedded technology, ENVX1002 develops critical thinking and skills to problem-solve with data.

Unit details and rules

Academic unit Life and Environmental Sciences Academic Operations
Credit points 6
Prerequisites
? 
None
Corequisites
? 
None
Prohibitions
? 
ENVX1001 or MATH1005 or MATH1905 or MATH1015 or MATH1115 or DATA1001 or DATA1901 or BUSS1020 or STAT1021 or ECMT1010
Assumed knowledge
? 

None

Available to study abroad and exchange students

Yes

Teaching staff

Coordinator Floris Van Ogtrop, floris.vanogtrop@sydney.edu.au
Lecturer(s) Floris Van Ogtrop, floris.vanogtrop@sydney.edu.au
Aaron Greenville, aaron.greenville@sydney.edu.au
Januar Harianto, januar.harianto@sydney.edu.au
Type Description Weight Due Length
Supervised exam
? 
Final exam
Multiple choice & short answer questions
45% Formal exam period 2 hours
Outcomes assessed: LO2 LO7 LO1 LO5 LO4 LO6
Assignment Describing Data
Report submitted via Turn-it-in
10% Week 05
Due date: 24 Mar 2023 at 23:59
Please see Assignment outline on Canvas
Outcomes assessed: LO3 LO7
Supervised test
? 
Mid-semester exam
CANVAS timed, open book exam. Analyse a dataset with R with SAQs.
20% Week 08 1 hour
Outcomes assessed: LO1 LO5 LO4
Assignment Comparing two sample populations
Report submitted via Turn-it-in
10% Week 10
Due date: 05 May 2023 at 23:59
Please see Assignment outline on Canvas
Outcomes assessed: LO3 LO1 LO5 LO4 LO7
Presentation group assignment Modelling relationships in data
Class presentation + peer review - see Canvas for details
15% Week 13 5 minutes - see Canvas for details
Outcomes assessed: LO1 LO5 LO6 LO7 LO3
group assignment = group assignment ?

Assessment summary

Using skills and concepts learnt in Lectures, Tutorials and Practical sessions, there are three main assignments associated with each of the three modules, there a mid-semester test and a final exam. All assessments are to be completed individually, with the exception of Assessment 3 which is a group assignment. 

Detailed information for each assessment can be found on Canvas.
 

  • Assessment 1: Students are to find a dataset of interest and write a report summarizing key features of the data using R Studio, ensuring analytical methods are clearly described.
     
  • Mid-semester exam: will cover key concepts learnt throughout the first half of the semester.
     
  • Assessment 2: Students write a report comparing two sample populations by collecting data through an experiment or combining datasets found online, using an appropriate statistical test.
     
  • Assessment 3: Requires students to collect data and determine whether there is a relationship between two continuous variables. Data collection, analysis and presentations are conducted as a group, followed by completing a peer review exercise.
     
  • Final exam: 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.

Assessment criteria

Result name Mark Range Description
High Distinction 85-100 To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at an exceptional standard as defined by grade descriptors or exemplars established by the faculty.
Distinction 75-84 To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at a very high standard as defined by grade descriptors or exemplars established by the faculty.
Credit 65-74 To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at a good standard as defined by grade descriptors or exemplars established by the faculty.
Pass 50-64 To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at an acceptable standard as defined by grade descriptors or exemplars established by the faculty
Fail 0-49 To be awarded to students who, in their performance in assessment tasks, fail to demonstrate the learning outcomes for the unit at an acceptable standard established by the faculty.

 

 

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.

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
Week 01 Introduction and Scientific Method; Lectures 1 & 2 Lecture (2 hr) LO1
Introduction to statistics and the scientific method - Guided tutorial Independent study (1 hr) LO1
Transition & Introduction to statistics and the scientific method Computer laboratory (2 hr) LO1
Week 02 Exploring Data Lecture (2 hr) LO1 LO3
Exploring data - Guided tutorial Independent study (1 hr) LO1 LO3
Exploring data Computer laboratory (2 hr) LO1 LO3
Week 03 Normal and discrete distributions Lecture (2 hr) LO1 LO2 LO3 LO5
Normal and discrete distributions - Guided tutorial Independent study (1 hr) LO1 LO2 LO3 LO5
Normal and discrete distributions Computer laboratory (2 hr) LO1 LO2 LO3 LO5
Week 04 Sampling distributions Lecture (2 hr) LO1 LO2 LO3 LO5
Sampling distributions - Guided tutorial Independent study (1 hr) LO1 LO2 LO3 LO5
Sampling distributions Computer laboratory (2 hr) LO1 LO2 LO3 LO5
Week 05 1 - sample tests Lecture (2 hr) LO1 LO4 LO5
1 - sample tests - Guided tutorial Independent study (1 hr) LO1 LO4 LO5
1 - sample tests Computer laboratory (2 hr) LO1 LO4 LO5
Week 06 2 - sample tests Lecture (2 hr) LO1 LO4 LO5
2 - sample tests - Guided tutorial Independent study (1 hr) LO1 LO4 LO5
2 - sample tests Computer laboratory (2 hr) LO1 LO4 LO5
Week 07 Non-parametric tests I Lecture (2 hr) LO1 LO4 LO5
Non-parametric tests I - Guided tutorial Independent study (1 hr) LO1 LO4 LO5
Non-parametric tests I Computer laboratory (2 hr) LO1 LO4 LO5
Week 08 Non-parametric tests II Lecture (2 hr) LO1 LO4 LO5
Non-parametric tests II - Guided tutorial Independent study (1 hr) LO1 LO4 LO5
Non-parametric tests II Computer laboratory (2 hr) LO1 LO4 LO5
Week 09 Describing relationships Lecture (2 hr) LO1 LO5 LO6
Describing relationships - Guided tutorial Independent study (1 hr) LO1 LO5 LO6
Describing relationships Computer laboratory (2 hr) LO1 LO5 LO6
Week 10 Simple linear regression Lecture (2 hr) LO1 LO5 LO6
Simple linear regression - Guided tutorial Independent study (1 hr) LO1 LO5 LO6
Simple linear regression Computer laboratory (2 hr) LO1 LO5 LO6
Week 11 Multiple linear regression Lecture (2 hr) LO1 LO5 LO6
Multiple linear regression - Guided tutorial Independent study (1 hr) LO1 LO5 LO6
Multiple linear regression Computer laboratory (2 hr) LO1 LO5 LO6
Week 12 Non-linear regression Lecture (2 hr) LO1 LO5 LO6
Non-linear regression - Guided tutorial Independent study (1 hr) LO1 LO5 LO6
Non-linear regression Computer laboratory (2 hr) LO1 LO5 LO6
Week 13 Revision Lecture (1 hr) LO1 LO4 LO5 LO6
Group presentations Presentation (2 hr) LO1 LO3 LO5 LO6 LO7

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. Describe the role of statistics, experimentation and hypothesis testing in relation to scientific research.
  • LO2. Understand the concept of probability and calculate probabilities by applying probability laws and theoretical results
  • LO3. Perform data exploration using R.
  • LO4. Understand the concept of experimental inference and select the correct statistical test (among 1-sample, 2-sample, chi-square, and non-parametric tests) appropriate for a particular experiment.
  • LO5. Demonstrate proficiency in the use R and Excel to describe and analyse data from simple experiments.
  • LO6. Model relationships between variables using linear and non-linear functions using R and Excel.
  • LO7. Write and present statistical and modelling results as part of a scientific report and oral presentation as a individual and as a team.

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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

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

We appreciate the very positive feedback we have received for this unit. We will continue to make improvements based on suggestions by students such as improving feedback for assessments. We have updated the look of the canvas site to help guide students to relevant sections and the course is designed so that students work through the modules in consecutive order. We have adjusted the assessment weights such that the exam is now worth 45% and not 55% as well as balancing up the assessment weightings. Furthermore, we will decrease the length of the guided tutorials and there are no longer practicals directly after the lecture which will hopefully allow for more time to complete the tutorial prior to the practical. We continue to work towards making classes more engaging.

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.