Outline

Overview

Statistical analysis of quantitative data is a fundamental aspect of modern business. The pervasiveness of information technology in all aspects of business means that managers are able to use very large and rich data sets. This unit covers a range of methods to model and analyse the relationships in such data, extending the introductory methods in BUSS1020. The methods are useful for detecting, analysing and making inferences about patterns and relationships within the data so as to support business decisions. This unit offers an insight into the main statistical methodologies for modelling the relationships in both discrete and continuous business data. This provides the information requirements for a range of specific tasks that are required, e.g. in financial asset valuation and risk measurement, market research, demand and sales forecasting and financial analysis, among others. The unit emphasises real empirical applications in business, finance, accounting and marketing, using modern software tools.

Unit details and rules

Academic unit	Business Analytics
Credit points	6
Prerequisites ?	Students commencing from 2018: QBUS1040; Pre-2018 continuing students: BUSS1020 or DATA1001 or ECMT1010 or ENVX1001 or ENVX1002 or STAT1021 or ((MATH1005 or MATH1015) and MATH1115) or 6 credit points of MATH units which must include MATH1905.
Corequisites ?	None
Prohibitions ?	ECMT2110
Assumed knowledge ?	This unit relies on mathematical knowledge at the level of the Maths in Business program, including calculus and matrix algebra. Students who do not meet this requirement are strongly encouraged to acquire the needed mathematical skills prior to enrolling in this unit.
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Artem Prokhorov, artem.prokhorov@sydney.edu.au
Tutor(s)	Joe Nguyen, joseph.nguyen1@sydney.edu.au
	Suman Saha, s.saha@sydney.edu.au
	Thi Ngoc Lan Le, lan.le@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam (Take-home short release)	Final exam Written exam with MCQ	30%	Formal exam period	2.5 hours
Outcomes assessed:
Assignment	Individual assignment 1 Practical assessment with written component	20%	Week 05	n/a
Outcomes assessed:
In-semester test (Record+)	Mid-semester exam Written exam with MCQ	30%	Week 07 Due date: 20 Sep 2021 at 10:00	80 minutes
Outcomes assessed:
Assignment	Group project Practical assessment with written component	20%	Week 13	n/a
Outcomes assessed:
= group assignment ? = Type B in-semester exam ? = Type D final exam ?

Assessment summary

Individual assignment: These tasks consist of written problem sets covering theoretical, conceptual, analytical, and interpretative questions based on all the material covered up until the submission deadline. The questions will measure students’ knowledge of the principles in business analytics and their ability to provide a complete and comprehensible description of their essential characteristics, as well as their ability to complete standard analytical tasks using Python software, discuss and interpret software output, as discussed in the unit in all prior to submission.
Group project: Groups perform an analytical exercise and subsequent statistical description of a set of data; a modelling exercise and quantitative analysis of dataset; a report and an executive summary. Groups will be expected to write up a professional report that describes every step of the approach taken in the analysis, findings and policy interpretations, and an executive summary. The project is designed to develop and test your ability to undertake an independent piece of quantitative analysis, and to develop and test your ability, as a group, to communicate the results to a professional standard.
Mid-semester exam: This exam will examine concepts covered in this unit, up until the submission deadline. The question will measure students’ knowledge of the major principles and concepts in business analytics, and their ability to provide a complete and comprehensible description of their essential characteristics, as well as their ability to complete standard analytical tasks and discuss and interpret Python statistical output, as discussed in weeks 1-6 of this unit. The exam will not require students to write or develop Python code.
Final exam: The exam will examine concepts covered in weeks 1-13 of this unit. The questions will measure students’ knowledge of the major principles and concepts in business analytics, and their ability to provide a complete and comprehensible description of their essential characteristics, as well as their ability to complete standard analytical tasks and discuss and interpret Python statistical output, as discussed in weeks 1-13 of this unit. The exam will not require students to write or develop Python code.

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.

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 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	1. Introduction to patterns and dependences; 2. Correlation and regression; 3. Two sample testing; 4. Contingency tables; 5. Testing for relationships between categorical variables	Lecture and tutorial (3.5 hr)
Week 02	1. Testing for relationships between categorical variables and two-sample testing (ctd); 2. Simple linear regression	Lecture and tutorial (3.5 hr)
Week 03	1. Simple linear regression (ctd); 2. Statistical inference for simple linear regression	Lecture and tutorial (3.5 hr)
Week 04	1. Statistical inference for simple linear regression (ctd); 2. Multiple linear regression	Lecture and tutorial (3.5 hr)
Week 05	1. Multiple linear regression (ctd); 2. Collinearity; 3. Statistical inference for multiple regression	Lecture and tutorial (3.5 hr)
Week 06	1. Statistical inference for multiple regression (cdt); 2. Regression modelling: non-constant error variance, categorical regressors, and interactions	Lecture and tutorial (3.5 hr)
Week 07	1. Regression modelling: categorical regressors and interactions (ctd); 2. Understanding linear models with linear algebra	Lecture and tutorial (3.5 hr)
Week 08	1. Understanding linear models with linear algebra (ctd); 2. Analysis of variance (ANOVA)	Lecture and tutorial (3.5 hr)
Week 09	1. Analysis of variance (ANOVA) (ctd); 2. Linear model diagnostics: outliers, leverage and influence points	Lecture and tutorial (3.5 hr)
Week 10	1. Linear model diagnostics: leverage and influence points (ctd); 2. Regression modelling: variable transformations, polynomial regressions	Lecture and tutorial (3.5 hr)
Week 11	1. Regression modelling: regression splines; 2. Model and variable selection	Lecture and tutorial (3.5 hr)
Week 12	1. Model and variable selection (ctd); 2. Logit and probit models	Lecture and tutorial (3.5 hr)
Week 13	Generalised linear models and revision	Lecture and tutorial (3.5 hr)

Attendance and class requirements

Lecture recordings: All lectures and seminars are recorded and will be available on Canvas for student use. Please note the Business School does not own the system and cannot guarantee that the system will operate or that every class will be recorded. Students should ensure they attend and participate in all classes.

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

All readings for this unit can be accessed through Reading List, available on Canvas.

Fox, John. Applied regression analysis and generalized linear models. Sage Publications, 2015.

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. develop an understanding of the principles of statistical modelling of business-related variables
LO2. develop a deeper understanding of statistically measuring and analysing relationships between business variables via a range of quantitative models and methods
LO3. develop proficiency in using relationships between variables and analytic methods to inform and assist business decision making
LO4. develop introductory skills in how to manage data and in how to extract objective quantitative information from them
LO5. develop proficiency in a software package, e.g. Python, for analysing and assessing relationships between business variables, and in dealing with large data sets
LO6. communicate empirical findings using adequate statistical reporting methods and appropriate technical language, as well as layman’s terms.

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.

Additional information

Python: The main computing tool for this unit will be Python, which is a free and open-source general purpose programming language available for all major platforms. A working knowledge of Python, and experience with Python in at least one prior unit is assumed knowledge in this unit. You are encouraged to use Python on your own computer(s).

More information can be found on Canvas.

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

On this page

Useful links

Media

Student links

About us

Connect

Current students

QBUS2810: Statistical Modelling for Business

Semester 2, 2021 [Normal day] - Remote

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

On this page

Useful links

Media

Student links

About us

Connect