ECON3209 Statistics for Econometrics - 2023

Subject Code
ECON3209
Study Level
Undergraduate
Commencing Term
Term 1
Total Units of Credit (UOC)
6
Delivery Mode
On Campus
School
Economics
The course outline is not available for current term. To view outlines from other year and/or terms visit the archives

1. Course Details

Summary of Course

This course provides the foundations for undertaking modern econometric methods including statistical distribution theory, asymptotic theory, mathematical methods and an introduction to statistical computing including bootstrap and simulation methods. Mastering this course will give students a deeper understanding of the statistical underpinnings of methods and knowledge acquired in other econometrics courses. Throughout the course, material will be presented in the context of simple models in order to concentrate on the concepts.

Teaching Times and Locations

Please note that teaching times and locations are subject to change. Students are strongly advised to refer to the Class Timetable website for the most up-to-date teaching times and locations.

View course timetable

Course Policies & Support

The Business School expects that you are familiar with the contents of this course outline and the UNSW and Business School learning expectations, rules, policies and support services as listed below:

  • Program Learning Outcomes
  • Academic Integrity and Plagiarism
  • Student Responsibilities and Conduct
  • Special Consideration
  • Protocol for Viewing Final Exam Scripts
  • Student Learning Support Services

Further information is provided in the Assessment and Policies and Support sections.

Students may not circulate or post online any course materials such as handouts, exams, syllabi or similar resources from their courses without the written permission of their instructor.

Course Aims and Relationship to Other Courses

This course aims to cover the parts of probability, statistical distribution theory and statistical inference essential for a deep understanding of econometrics. It develops the statistical foundations for econometric techniques relating to the analysis of economic and financial data. Uncertainty governs both data analyses undertaken by scientists and judgments made by all of us in our everyday lives. This course is a first look at the use of quantitative methods to handle decision making under uncertainty.

This course is designed to provide a foundation for the statistical theory covered in statistical inference and other econometrics courses. Ultimately this course aims to develop your ability to model quantitative relationships and deepen your understanding of how statistical concepts are used in econometrics, the science and art of determining what type of model to build, estimating the parameters of the model, and testing the model statistically.

The major topic will be probability theory and introductory inferential statistics. These two topics form the platform on which all statistical work is built. To understand these advanced methods, it is vital to have a background in these topics. Unfortunately, this means that we will read little applied research and will devote most of our time to the abstract world of probability theory and the logic of statistical inference. Students who intend to take this course should remember that the content is highly theoretical and analytical.

In addition, the course is designed with the following aims in mind:

  • Deepen your mathematical and statistical skills;
  • Foster your analytical and critical thinking;
  • Develop your econometrics problem solving abilities.

Prerequisite mathematics knowledge is at the level of a typical second-year quantitative course equivalent to ECON2206 Introductory Econometrics. Students are advised to revise their knowledge of ECON1202 Quantitative Analysis for Business and Economics and ECON1203 Business and Economic Statistics. Familiarity with algebra, calculus and elementary linear algebra is assumed. The main vehicle for understanding the material is a solid understanding of calculus: differentiation, integration, infinite series, Taylor expansions, limits, etc. No previous experience with statistics or probability (or gambling) is necessary.

Relationship to Other Courses

This course is a prerequisite for ECON3203 Econometric Theory and Methods. ECON3203 is a building block for ECON4202 Advanced Econometric Theory and Methods. Accordingly, this course is a necessary building block for advanced econometric courses at the Honours and postgraduate levels.

Student Learning Outcomes

The Course Learning Outcomes (CLOs) are what you should be able to demonstrate by the end of this course, if you participate fully in learning activities and successfully complete the assessment items.

CLOs also contribute to your achievement of the Program Learning Outcomes (PLOs), which are developed across the duration of a program. PLOs are, in turn, directly linked to UNSW graduate capabilities. More information on Coursework PLOs is available under Policies and Support. For PG Research PLOs, including MPDBS, please refer to the UNSW HDR Learning Outcomes.

The following table shows how the CLOs for this course relate to the overall PLOs and indicates where each CLO and PLO is assessed:

Course Learning Outcomes Program Learning Outcomes Course Assessment Item
On successful completion of the course, you should be able to: This course helps you to develop the following Program Learning Outcomes: This learning outcome will be assessed in the following items:

1. Demonstrate an appreciation of the relationship between theoretical statistics and the econometric techniques used to analyse real world data.

 

PLO 1: Business knowledge
PLO 4: Teamwork
PLO 5: Responsible business practice
PLO 6: Global and cultural competence
PLO 7: Leadership development
  • Online Quiz
  • Assignment 1 & 2
  • Video Presentation

2. Apply the mathematical techniques of probability and distributions to inference, estimation and hypothesis testing, which are the formal methods by which we learn from noisy data, random samples, and other such uncertain real-world measurements.

PLO 1: Business knowledge
PLO 2: Problem solving
PLO 6: Global and cultural competence
  • Online Quiz
  • Assignment 1 & 2
  • Video Presentation
  • Final Exam

3. Undertake computer calculations concerning statistical distributions and perform computer simulations of models.

PLO 1: Business knowledge
PLO 2: Problem solving
PLO 4: Teamwork
  • Online Quiz
  • Assignment 1 & 2
  • Video Presentation

4. Demonstrate problem solving skills, especially how to prove results analytically from a set of given assumptions.

PLO 2: Problem solving
PLO 4: Teamwork
PLO 7: Leadership development
  • Online Quiz
  • Assignment 1 & 2
  • Final Exam

5. Demonstrate effective communication of the analytical complexity of econometric problems.

PLO 3: Business communication
PLO 5: Responsible business practice
PLO 7: Leadership development
  • Assignment 1 & 2
  • Video Presentation

2. Staff Contact Details

Position Title Name Email Location Phone Consultation Times
Lecturer-in-chargeDrRachida OuysseRoom 441, UNSW Business School9385 3321Tuesdays 11:30-12:30 ( online and by appointment)
LecturerMsRoelien Christien Timmer
–
–
Mondays 16:30-17:30 (online), or by appointment

Communication with staff

Students are encouraged to ask questions related to this subject during lectures when time permits and especially during tutorials.

The course learning and teaching team are available for one-on-one consultations. Consultations are an opportunity for you to ask questions about the course material with your lecturer (Ms Roelien Week 1-5, Dr Ouysse  Weeks 6-10). You may need to ask about the material introduced in lectures, the problems you have attempted or questions that are not fully answered in tutorials. For any questions about the assessments and the admin matters, please contact the LIC Dr Ouysse by email.

Emails should have a clear subject line and be sharply focused. The LIC usually answers your email inquiries within 48 hours (not including weekends). Please keep emails short and defer lengthy discussions to consultation time.  

Student Enrolment Requests

Students can vary their own enrolment (including switching lecture streams or tutorials) via myUNSW until the end of Week 1. In general, most other student enrolment requests should be directed to The Nucleus: Student Hub (formerly Student Central). These include enrolment in full courses or tutorials, course timetable clashes, waiving prerequisites for any course, transfer-of-credit (international exchange, transfer to UNSW, cross-institutional study, etc.), or any other request which requires a decision about equivalence of courses and late enrolment for any course. Where appropriate, the request will be passed to the School Office for approval before processing. Note that enrolment changes are rarely considered after Week 2 classes have taken place.

3. Learning and Teaching Activities

Use of your Webcam and Digital Devices: If you enrol in an online class, or the online stream of a hybrid class, teaching and associated activities will be conducted using Teams, Zoom, or similar a technology. Using a webcam is optional, but highly encouraged, as this will facilitate interaction with your peers and instructors. If you are worried about your personal space being observed during a class, we encourage you to blur your background or make use of a virtual background. Please contact the Lecturer-in-Charge if you have any questions or concerns.

Some courses may involve undertaking online exams for which your own computer or digital devices will be required. Monitoring of online examinations will be conducted directly by University staff and is bound by the University's privacy and security requirements. Any data collected will be handled accordance with UNSW policies and standards for data governance. For more information on how the University manages personal information please refer to the UNSW Student Privacy Statement and the UNSW Privacy Policy.

Approach to Learning and Teaching in the Course

The philosophy underpinning this course and its teaching and learning strategies is based on the “Guidelines on Learning that Inform Teaching at UNSW”. Specifically, the lectures, tutorials and assessment have been designed to appropriately challenge students and support the achievement of the desired learning outcomes. A climate of inquiry and dialogue is encouraged between students and teachers and among students (in and out of class). The lecturers and tutors aim to provide meaningful and timely feedback to students to improve learning outcomes.

This is not a course where you can become proficient just by observing. You will need to get involved in class activities - evaluating information, and asking and answering questions. You also must learn to organise your independent study and practice enough problems to gain a thorough understanding of concepts and how to apply them.

Students are expected to:

  • Put a consistent effort into learning activities throughout the term by preparing for the regular assessment tasks;
  • Take a responsible role in preparing for tutorials and participating in them;
  • Develop communication skills through engaging in classroom discussions and preparing assignments;
  • Concentrate more on understanding how and why to use formulae and less on memorising them;
  • Make continuous improvements by using the feedback from tutorials and assessments.

Learning Activities and Teaching Strategies

​The examinable content of the course is defined by the references given in the lecture schedule, the content of lectures, and the content of the tutorial/homework and assignment programs. Out-of-class study is an integral part of the learning process. This course requires a solid commitment and a continuing effort.

Lectures

Lectures will provide broad coverage of the main topics considered in the course. Lectures will introduce and emphasise the course content. They will include an explanation of relevant topics and theory together with the use of worked examples to demonstrate the theory in practice. However, students should only regard their content as partial.

Lectures will be delivered online, with the first lecture each week (Tuesday 10:00 - 11:30) streamed live via Zoom, and the second lecture each week (Thursday 14:00 - 15:30) pre-recorded and posted on the day it is scheduled. Recordings of both lectures will be available to students on the course Moodle site.

This is a lecture-based course, which will proceed as quickly or slowly as is necessary. You must make every effort to attend the synchronous online lectures to have instant answers to their questions as the lecture progresses and to view the second lecture recording on the scheduled day to stay on top of the material.

It is important for the student to devote a considerable amount of time to private study to achieve an appropriate level of understanding and to practice the different econometric tools introduced. Lectures provide one of the principal means of learning instruction, but it is essential that their contribution be bolstered and supported by other learning resources.

Students are expected to develop the skills and ability to derive results on their own. Memorising formulae and final results will not be of great help in the assessments; only a proper ability to develop these results will ensure success.

To get the most out of the lectures, students are strongly encouraged to familiarise themselves with the prescribed text readings as given in the course outline prior to attending each lecture, and to be prepared to take notes during the lecture itself.

Tutorials

The more you read the more you know, but the more you practice the more you learn and understand. Accordingly, the key to understanding in this course is problem solving.

There will be weekly tutorials. The purpose of tutorials is to enable you to raise questions about difficult topics or problems encountered in your studies, and to provide experience with problem solving. Students must not expect another lecture, but must come to tutorials prepared with informed questions of their own.

Discussion will be based normally on a sequence of exercise sheets (homework) that will be distributed regularly during the course. You are expected to make a serious attempt at all questions on an exercise sheet before attending the tutorial at which it is discussed. It will not be possible to discuss all the problems set in the allotted time and you should not expect all questions to be solved in depth at the tutorials. Some tutorial exercises (and assignments) will require the use of statistical software (Python) to undertake calculations concerning distributions and simulations of statistical models.

In tutorials, some students may be randomly chosen to discuss their attempts to answer the tutorial problems. The aim is to encourage discussion within the classroom and to solve the issues you and your classmates have encountered with the problems.

Out-of-Class Study

While you may have preferred individual learning strategies, most learning will be achieved outside of class time. Lectures can only provide a structure to assist your study, and tutorial time is limited.

The required textbook for this course is by Miller & Miller (MM) (see the Course Resources section for more details). There is also a highly recommended book by DeGroot & Schervish (DG). You only need to buy one. The course schedule and reading guide refer to both textbooks.

You are strongly encouraged to (heavily) use the reference textbooks. Both textbooks contain exhaustive and detailed derivations of results and proofs of theorems introduced in the lectures. There are also many applications and case studies presented in the textbooks that will help you understand the possible applications of the various theoretical concepts covered in class.

The reading load for this course is mild - perhaps ten to twenty pages per class. However, the work load will be high. It is important to carefully read and understand every result in the text. This requires full attention when reading the text. My advice to you is to make the book your friend and use the consultation time to come and ask for help in understanding what you read.

Student Workload

Indicated below is the expected student workload per week for this subject

​No. timetabled hours/week*
No. personal study hours/week**
​		Total workload hours/week***
​
​4.5 hours/week
(3 hours of lecture + 1.5 hours of tutorial)
​5.5 to 7 hours/week
​10 to 12 hours/week

* Total time spent per week at lectures and tutorials

** Total time students are expected to spend per week in studying, completing assignments, reading reference text and resources, etc.

*** Combination of timetabled class hours and personal study.



5. Course Resources

​Online lectures will take place in weeks 1-5,7-9 on Tuesdays from 10:00-11:30 (live synchronous) and on Thursdays (recorded and available on the day and after 15:30).

All Lectures will be recorded and available on the course website.

Whether you want to join live lectures or watch recordings of previous lectures, you should log into Moodle. The course Moodle site is minimalistic, whose only purpose is to provide you with links to (1) lectures//Tutorials, (2) assessments submission links, and (3) final examination details and submission.

The rest of the action all happens on the Ed platform: access to course material including lecture notes, tutorial material (questions and answers), assessment information, and last but not least Message Board/Forums to ask and answer questions.

Instructions on how to access the Ed platform and links will be available from the Moodle site. Please use the link provided on Moodle to access the Ed platform. You can only access it from within your Moodle account.

Required Textbook

  • Miller, I., & M. Miller (2014), John S. Freund’s Mathematical Statistics with Applications, 8th Edition, Pearson Prentice Hall. (MM)

The course will mostly follow Miller and Miller (MM) but will skip some topics and add some others from the additional readings. The LIC will make copies of any extra material available to students as we progress.

Highly Recommended

  • DeGroot, M.H., & M.J. Schervish (2014), Probability and Statistics, 4th. Edition, Reading, Mass. Addison-Wesley. (DG)
  • Schervish, M.J. (2012), Student Solutions Manual (for Probability and Statistics, 4th. Edition), Boston, Mass. Addison-Wesley.

Additional Readings. These are slightly more advanced!

  • Casella, G., & R.L. Berger (2002), Statistical Inference, Duxbury Press.
  • Hogg, R.V., & A.T. Craig (1978), Introduction to Mathematical Statistics, 4th Edition, New York: Macmillan.
  • Bierens, H.J. (2004), Introduction to the Mathematical and Statistical Foundations of Econometrics, Cambridge University Press.

Copies of DeGroot and Miller have been put into the High Use Collection at the UNSW Library.

Software

We will be using Python for the. computational exercises/applications in this course. More information about Python will be made available on Ed.

6. Course Evaluation & Development

Feedback is regularly sought from students and continual improvements are made based on this feedback. At the end of this course, you will be asked to complete the myExperience survey, which provides a key source of student evaluative feedback. Your input into this quality enhancement process is extremely valuable in assisting us to meet the needs of our students and provide an effective and enriching learning experience. The results of all surveys are carefully considered and do lead to action towards enhancing educational quality.

​The School of Economics strives to be responsive to student feedback. If you would like more information on how the design of this course and changes made to it over time have taken students’ needs and preferences into account, please contact the Director of Education at the School of Economics.

​The School of Economics strives to be responsive to student feedback. If you would like more information on how the design of this course and changes made to it over time have taken students’ needs and preferences into account, please contact the Director of Education at the School of Economics.

Consent for De-Identified Data to be Used for Secondary Research into Improving Student Experience

To enhance your student experience, researchers at UNSW conduct academic research that involves the use of de-identified student data, such as assessment outcomes, course grades, course engagement and participation, etc. Students of this course are being invited to provide their consent for their de-identified data to be shared with UNSW researchers for research purposes after the course is completed.

Providing consent for your de-identified data to be used in academic research is voluntary and not doing so will not have an impact on your course grades.

Researchers who want to access your de-identified data for future research projects will need to submit individual UNSW Ethics Applications for approval before they can access your data.

A full description of the research activities aims, risks associated with these activities and how your privacy and confidentiality will be protected at all times can be found here.

If you consent to have your de-identified data used for academic research into improving student experience, you do not need to do anything. Your consent will be implied, and your data may be used for research in a format that will not individually identify you after the course is completed.

If you do not consent for this to happen, please email the opt-out form to seer@unsw.edu.au to opt-out from having your de-identified data used in this manner. If you complete the opt-out form, the information about you that was collected during this course will not be used in academic research.

7. Course Schedule

Note: for more information on the UNSW academic calendar and key dates including study period, exam, supplementary exam and result release, please visit: https://student.unsw.edu.au/new-calendar-dates
Week Activity Topic Assessment/Other
Week 1: 13 FebruaryLecture

- Probability

- Random Variables & Probability Distributions

- Miller & Miller: Chapters 1 & 2 & 3

- DeGroot: Chapters 1 & 2 & 3

Week 2: 20 FebruaryLecture

Moments/Expectation of Random Variables

- Miller & Miller: Chapter 4

- DeGroot: Chapter 4

Tutorial

Previous week's lecture topics

–
Week 3: 27 FebruaryLecture

Special Probability Distributions

 

- Miller & Miller: Chapters 5 & 6

- DeGroot: Chapter 5

Tutorial

Previous week's lecture topics

Online Quiz

Due Friday, 4 PM.

Week 4: 6 MarchLecture

- Functions of Random Variables

- Estimation I: Bayesian Estimators

- Miller &Miller: Chapters 7 & 10

- DeGroot: Chapters 3 & 7

Tutorial

Previous week's lecture topics

–
Week 5: 13 MarchLecture
  • Estimation II: Maximum Likelihood
  • Sampling Distributions
  • Bayesian Estimation/Inference
  • Miller & Miller: Chapters 8 & 10
  • DeGroot: Chapters 6 & 7 & 8
Tutorial

Previous Week's Topics

–
Assessment

Material from Week 1 up to and including Week 4.

Assignment 1

Due Friday, 4 PM.

Week 6 FLEXIBILITY WEEK: 20 MarchNo scheduled activity

No lectures/tutorials

–
Week 7: 27 MarchLecture

- Asymptotic Inference:

  • Consistency, Asymptotic efficiency
  • Consistency of MLE

- Interval estimation

- Simulation Methods (*)

(*) Time permitting

- Miller & Miller: Chapters 4 & 8 & 10

- DeGroot: Chapters 6 & 8 & 12

Tutorial

Previous Week's Topics

Video Presentation

Due Friday, 4 PM.

Week 8: 3 AprilLecture

- Classical Hypothesis Testing

- Bayesian Simulations

 

  • Miller &Miller: Chapters 7, 9 & 10
  • DeGroot: Chapter 9
Tutorial

Previous Week's Topics

–
Week 9: 11 AprilLecture

- Inference in the Linear Statistical Model

**Monday 10 Apr is a public holiday**

 

 

  • Miller & Miller: Chapter 11
  • DeGroot: Chapter 11
Tutorial

Previous week's lecture topics

–
Assessment

All material from Week 1 up to and including Week 8.

Assignment 2

Due Friday 4 PM.

Week 10: 17 AprilTutorial

No lectures this week.

Previous week's lecture topics

–

8. Policies and Support

Information about UNSW Business School program learning outcomes, academic integrity, student responsibilities and student support services. For information regarding special consideration, supplementary exams and viewing final exam scripts, please go to the key policies and support page.

Program Learning Outcomes

The Business School places knowledge and capabilities at the core of its curriculum via seven Program Learning Outcomes (PLOs). These PLOs are systematically embedded and developed across the duration of all coursework programs in the Business School.

PLOs embody the knowledge, skills and capabilities that are taught, practised and assessed within each Business School program. They articulate what you should know and be able to do upon successful completion of your degree.

Upon graduation, you should have a high level of specialised business knowledge and capacity for responsible business thinking, underpinned by ethical professional practice. You should be able to harness, manage and communicate business information effectively and work collaboratively with others. You should be an experienced problem-solver and critical thinker, with a global perspective, cultural competence and the potential for innovative leadership.

All UNSW programs and courses are designed to assess the attainment of program and/or course level learning outcomes, as required by the UNSW Assessment Design Procedure. It is important that you become familiar with the Business School PLOs, as they constitute the framework which informs and shapes the components and assessments of the courses within your program of study.

PLO 1: Business knowledge

Students will make informed and effective selection and application of knowledge in a discipline or profession, in the contexts of local and global business.

PLO 2: Problem solving

Students will define and address business problems, and propose effective evidence-based solutions, through the application of rigorous analysis and critical thinking.

PLO 3: Business communication

Students will harness, manage and communicate business information effectively using multiple forms of communication across different channels.

PLO 4: Teamwork

Students will interact and collaborate effectively with others to achieve a common business purpose or fulfil a common business project, and reflect critically on the process and the outcomes.

PLO 5: Responsible business practice

Students will develop and be committed to responsible business thinking and approaches, which are underpinned by ethical professional practice and sustainability considerations.

PLO 6: Global and cultural competence

Students will be aware of business systems in the wider world and actively committed to recognise and respect the cultural norms, beliefs and values of others, and will apply this knowledge to interact, communicate and work effectively in diverse environments.

PLO 7: Leadership development

Students will develop the capacity to take initiative, encourage forward thinking and bring about innovation, while effectively influencing others to achieve desired results.


These PLOs relate to undergraduate and postgraduate coursework programs.  For PG Research PLOs, including Master of Pre-Doctoral Business Studies, please refer to the UNSW HDR Learning Outcomes

Business School course outlines provide detailed information for students on how the course learning outcomes, learning activities, and assessment/s contribute to the development of Program Learning Outcomes.

UNSW Graduate Capabilities

The Business School PLOs also incorporate UNSW graduate capabilities, a set of generic abilities and skills that all students are expected to achieve by graduation. These capabilities articulate the University’s institutional values, as well as future employer expectations.

UNSW Graduate CapabilitiesBusiness School PLOs
Scholars capable of independent and collaborative enquiry, rigorous in their analysis, critique and reflection, and able to innovate by applying their knowledge and skills to the solution of novel as well as routine problems.
  • PLO 1: Business knowledge
  • PLO 2: Problem solving
  • PLO 3: Business communication
  • PLO 4: Teamwork
  • PLO 7: Leadership development

Entrepreneurial leaders capable of initiating and embracing innovation and change, as well as engaging and enabling others to contribute to change
  • PLO 1: Business knowledge
  • PLO 2: Problem solving
  • PLO 3: Business communication
  • PLO 4: Teamwork
  • PLO 6: Global and cultural competence
  • PLO 7: Leadership development

Professionals capable of ethical, self-directed practice and independent lifelong learning
  • PLO 1: Business knowledge
  • PLO 2: Problem solving
  • PLO 3: Business communication
  • PLO 5: Responsible business practice

Global citizens who are culturally adept and capable of respecting diversity and acting in a socially just and responsible way.
  • PLO 1: Business knowledge
  • PLO 2: Problem solving
  • PLO 3: Business communication
  • PLO 4: Teamwork
  • PLO 5: Responsible business practice
  • PLO 6: Global and cultural competence

While our programs are designed to provide coverage of all PLOs and graduate capabilities, they also provide you with a great deal of choice and flexibility.  The Business School strongly advises you to choose a range of courses that assist your development against the seven PLOs and four graduate capabilities, and to keep a record of your achievements as part of your portfolio. You can use a portfolio as evidence in employment applications as well as a reference for work or further study. For support with selecting your courses contact the UNSW Business School Student Services team.





Academic Integrity and Plagiarism

Academic Integrity is honest and responsible scholarship. This form of ethical scholarship is highly valued at UNSW. Terms like Academic Integrity, misconduct, referencing, conventions, plagiarism, academic practices, citations and evidence based learning are all considered basic concepts that successful university students understand. Learning how to communicate original ideas, refer sources, work independently, and report results accurately and honestly are skills that you will be able to carry beyond your studies.

The definition of academic misconduct is broad. It covers practices such as cheating, copying and using another person’s work without appropriate acknowledgement. Incidents of academic misconduct may have serious consequences for students.

Plagiarism

UNSW regards plagiarism as a form of academic misconduct. UNSW has very strict rules regarding plagiarism. Plagiarism at UNSW is using the words or ideas of others and passing them off as your own. All Schools in the Business School have a Student Ethics Officer who will investigate incidents of plagiarism and may result in a student’s name being placed on the Plagiarism and Student Misconduct Registers.

Below are examples of plagiarism including self-plagiarism:

Copying: Using the same or very similar words to the original text or idea without acknowledging the source or using quotation marks. This includes copying materials, ideas or concepts from a book, article, report or other written document, presentation, composition, artwork, design, drawing, circuitry, computer program or software, website, internet, other electronic resource, or another person's assignment, without appropriate acknowledgement of authorship.

Inappropriate Paraphrasing: Changing a few words and phrases while mostly retaining the original structure and/or progression of ideas of the original, and information without acknowledgement. This also applies in presentations where someone paraphrases another’s ideas or words without credit and to piecing together quotes and paraphrases into a new whole, without appropriate referencing.

Collusion: Presenting work as independent work when it has been produced in whole or part in collusion with other people. Collusion includes:

  • Students providing their work to another student before the due date, or for the purpose of them plagiarising at any time
  • Paying another person to perform an academic task and passing it off as your own
  • Stealing or acquiring another person’s academic work and copying it
  • Offering to complete another person’s work or seeking payment for completing academic work

Collusion should not be confused with academic collaboration (i.e., shared contribution towards a group task).

Inappropriate Citation: Citing sources which have not been read, without acknowledging the 'secondary' source from which knowledge of them has been obtained.

Self-Plagiarism: ‘Self-plagiarism’ occurs where an author republishes their own previously written work and presents it as new findings without referencing the earlier work, either in its entirety or partially. Self-plagiarism is also referred to as 'recycling', 'duplication', or 'multiple submissions of research findings' without disclosure. In the student context, self-plagiarism includes re-using parts of, or all of, a body of work that has already been submitted for assessment without proper citation.

To see if you understand plagiarism, do this short quiz: https://student.unsw.edu.au/plagiarism-quiz

Cheating

The University also regards cheating as a form of academic misconduct. Cheating is knowingly submitting the work of others as their own and includes contract cheating (work produced by an external agent or third party that is submitted under the pretences of being a student’s original piece of work). Cheating is not acceptable at UNSW.

If you need to revise or clarify any terms associated with academic integrity you should explore the 'Working with Academic Integrity' self-paced lessons available at: https://student.unsw.edu.au/aim.

For UNSW policies, penalties, and information to help you avoid plagiarism see: https://student.unsw.edu.au/plagiarism as well as the guidelines in the online ELISE tutorials for all new UNSW students: http://subjectguides.library.unsw.edu.au/elise. For information on student conduct see: https://student.unsw.edu.au/conduct.

For information on how to acknowledge your sources and reference correctly, see: https://student.unsw.edu.au/referencing. If you are unsure what referencing style to use in this course, you should ask the lecturer in charge.



Student Responsibilities and Conduct

​Students are expected to be familiar with and adhere to university policies in relation to class attendance and general conduct and behaviour, including maintaining a safe, respectful environment; and to understand their obligations in relation to workload, assessment and keeping informed.

Information and policies on these topics can be found on the 'Managing your Program' website.

Workload

It is expected that you will spend at least ten to twelve hours per week studying for a course except for Summer Term courses which have a minimum weekly workload of twenty to twenty four hours. This time should be made up of reading, research, working on exercises and problems, online activities and attending classes. In periods where you need to complete assignments or prepare for examinations, the workload may be greater. Over-commitment has been a cause of failure for many students. You should take the required workload into account when planning how to balance study with employment and other activities.

We strongly encourage you to connect with your Moodle course websites in the first week of semester. Local and international research indicates that students who engage early and often with their course website are more likely to pass their course.

View more information on expected workload

Attendance and Engagement

Your regular attendance and active engagement in all scheduled classes and online learning activities is expected in this course. Failure to attend / engage in assessment tasks that are integrated into learning activities (e.g. class discussion, presentations) will be reflected in the marks for these assessable activities. The Business School may refuse final assessment to those students who attend less than 80% of scheduled classes where attendance and participation is required as part of the learning process (e.g. tutorials, flipped classroom sessions, seminars, labs, etc.). If you are not able to regularly attend classes, you should consult the relevant Course Authority.

View more information on attendance

General Conduct and Behaviour

You are expected to conduct yourself with consideration and respect for the needs of your fellow students and teaching staff. Conduct which unduly disrupts or interferes with a class, such as ringing or talking on mobile phones, is not acceptable and students may be asked to leave the class.

View more information on student conduct

Health and Safety

UNSW Policy requires each person to work safely and responsibly, in order to avoid personal injury and to protect the safety of others.

View more information on Health and Safety

Keeping Informed

You should take note of all announcements made in lectures, tutorials or on the course web site. From time to time, the University will send important announcements to your university e-mail address without providing you with a paper copy. You will be deemed to have received this information. It is also your responsibility to keep the University informed of all changes to your contact details.




Student Support and Resources

The University and the Business School provide a wide range of support services and resources for students, including:

Business School Learning Support Tools
Business School provides support a wide range of free resources and services to help students in-class and out-of-class, as well as online. These include:

  • Academic Communication Essentials – A range of academic communication workshops, modules and resources to assist you in developing your academic communication skills.
  • Learning consultations – Meet learning consultants who have expertise in business studies, literacy, numeracy and statistics, writing, referencing, and researching at university level.
  • PASS classes – Study sessions facilitated by students who have previously and successfully completed the course.
  • Educational Resource Access Scheme – To support the inclusion and success of students from equity groups enrolled at UNSW Sydney in first year undergraduate Business programs.

The Nucleus - Business School Student Services team
The Nucleus Student Services team provides advice and direction on all aspects of enrolment and graduation. Level 2, Main Library, Kensington 02 8936 7005 / https://nucleus.unsw.edu.au/en/contact-us

Business School Equity, Diversity and Inclusion
The Business School Equity, Diversity and Inclusion Committee strives to ensure that every student is empowered to have equal access to education. The Business School provides a vibrant, safe, and equitable environment for education, research, and engagement that embraces diversity and treats all people with dignity and respect. BUSEDI@unsw.edu.au

UNSW Academic Skills
Resources and support – including workshops, individual consultations and a range of online resources – to help you develop and refine your academic skills. See their website for details.
academicskills@unsw.edu.au

Student Support Advisors
Student Support Advisors work with all students to promote the development of skills needed to succeed at university, whilst also providing personal support throughout the process.
John Goodsell Building, Ground Floor.
advisors@unsw.edu.au
02 9385 4734

International Student Support
The International Student Experience Unit (ISEU) is the first point of contact for international students. ISEU staff are always here to help with personalised advice and information about all aspects of university life and life in Australia.
Advisors can support you with your student visa, health and wellbeing, making friends, accommodation and academic performance.
International.student@unsw.edu.au
02 9385 4734

Equitable Learning Services
Equitable Learning Services (formerly Disability Support Services) is a free and confidential service that provides practical support to ensure that your health condition doesn't adversely affect your studies. Register with the service to receive educational adjustments.
Ground Floor, John Goodsell Building.
els@unsw.edu.au
02 9385 4734

UNSW Counselling and Psychological Services
Provides support and services if you need help with your personal life, getting your academic life back on track or just want to know how to stay safe, including free, confidential counselling.
Level 2, East Wing, Quadrangle Building.
counselling@unsw.edu.au
02 9385 5418

Library services and facilities for students
The UNSW Library offers a range of collections, services and facilities both on-campus and online.
Main Library, F21.
02 9065 9444

Moodle eLearning Support
Moodle is the University’s learning management system. You should ensure that you log into Moodle regularly.
externalteltsupport@unsw.edu.au
02 9385 3331

UNSW IT
UNSW IT provides support and services for students such as password access, email services, wireless services and technical support.
UNSW Library Annexe (Ground floor).
02 9385 1333



Support for Studying Online

The Business School and UNSW provide a wide range of tools, support and advice to help students achieve their online learning goals. 

The UNSW Guide to Online Study page provides guidance for students on how to make the most of online study.

We recognise that completing quizzes and exams online can be challenging for a number of reasons, including the possibility of technical glitches or lack of reliable internet. We recommend you review the Online Exam Preparation Checklist of things to prepare when sitting an online exam.

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