BUSS4931: Semester 1, 2023

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Units BUSS4931 Semester 1 2023 [Normal day]

Unit of study_

BUSS4931: Advanced Business Statistics

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

Overview

This unit aims to provide a thorough and rigorous coverage of some of the statistical theory underpinning modern estimation methods in Business Statistics. Development of the material is rigorous, and comparisons are drawn between different approaches to estimation and inference. The theory is complemented with relevant business examples allowing students to gain a deep understanding of the models and the ability to tailor them to various business applications.

Unit details and rules

Unit code	BUSS4931
Academic unit	Business Analytics
Credit points	6
Prohibitions ?	None
Prerequisites ?	Students must meet the entry requirements for the Bachelor of Advanced Studies (Advanced Coursework), including completion of a pass undergraduate degree and a major in Business Analytics (including QBUS3600)
Corequisites ?	None
Assumed knowledge ?	Students are assumed to be familiar with statistical modelling, Optimisation and Machine Learning
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Artem Prokhorov, artem.prokhorov@sydney.edu.au

Type	Description	Weight	Due	Length
Assignment	Assignment 5% attendance -- see Additional Information 5% - two written problem sets	10%	Week 01	n/a
Outcomes assessed:
Supervised test ?	Mid-semester exam Written Exam	40%	Week 07 Due date: 03 Apr 2023 at 13:00	1.5 hours
Outcomes assessed:
Assignment	Final exam Written and uploaded to Canvas. Due date to be allocated in class.	50%	Week 13	n/a
Outcomes assessed:

Assessment summary

Assignment:

Mid-semester exam:

Final exam:

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.

Result name	Mark range	Description
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.
Distinction	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.
Credit	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.
Pass	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.
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see sydney.edu.au/students/guide-to-grades.

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.

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.

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	Review	Lecture (3 hr)
Week 02	OLS	Lecture (3 hr)
Week 03	GLS and Other Extensions	Lecture (3 hr)
Week 04	GMM & GEL	Lecture (3 hr)
Week 05	IV	Lecture (3 hr)
Week 06	Panels	Lecture (3 hr)
Week 07	Mid-term	Lecture (3 hr)
Week 08	Semi- and Non-parametrics	Lecture (3 hr)
Week 09	MLE and Bayes	Lecture (3 hr)
Week 10	Discrete Choice	Lecture (3 hr)
Week 11	Duration Models	Lecture (3 hr)
Week 12	Revision	Lecture (3 hr)

Attendance and class requirements

Attendance is required and will count toward 5% of the mark as part of the assignment part of the assessment structure. For Zoom lectures, attendance is defined as being on the Zoom call, in person, with camera on. Participation by asking thoughtful questions is encouraged but not required.

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.

Required readings

1. Introduction to Econometrics by Bruce E. Hansen

2. Econometrics by Bruce E. Hansen

both books are available for free at

https://ssc.wisc.edu/~bhansen/econometrics/

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. Identify the advanced statistical methods required to model business data and explain their advantages and disadvantages
LO2. Successfully choose the most appropriate and relevant statistical tools for solving the business analytic problem of interest
LO3. Accurately identify and communicate the positives as well as the limitations of a range of analytical methods

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.

No changes have been made since this unit was last offered. Previous run had outstanding student feedback.

Additional information

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

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

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